RECENT ADVANCES INCIVIL ENGINEERING EDITORS S.B. Dwivedi Rajesh Kumar K.K. Pandey Kesheo Prasad Medha Jha Anurag Ohri P.B. Ramudu By Department of Civil Engineering, Institute of Technology, Banaras Hindu University, Varanasi-221005, India Proceeding of the National Conference on Recent Advances in Civil Engineering October 14-16, 2011, Varanasi Editors, RACE-2011 No part of the publication can be reproduced in any form or by any means without the prior Written Permission of the publisher. Due care has been taken to ensure that the information provided in this book is correct. However, the editors bear no responsibility for any damage resulting from any inadvertent omission or inaccuracy in this book. ISBN 978-81-921121-0-7 Department of Civil Engineering, Institute of Technology, Banaras Hindu University, Varanasi-221005, India RECENT ADVANCES IN CIVIL ENGINEERING-2011 i EDITORIAL DESK A consistent endeavor has been made by the Civil Engineering professionals since last few years to solve the complex problemsand challengesbyemploying certain advancedsolution. In view offulfillingtheresearchgapaNationalconferenceonRecentAdvancesinCivilEngineering (RACE-2011)isbeing organizedin Department of Civil Engineering, Institute of Technology, BanarasHinduUniversityduring14thto16thOctober-2011.Inordertohighlighttherecent advancesinCivilEngineeringpresentedpapersoftheconferencerelatedtoStructural Engineering,HydraulicandWaterResourcesEngineering,GeotechnicalEngineering, EnvironmentalEngineering,TransportationEngineering,GIS&RemoteSensing,Applied Geology and Disaster Mitigation have been included in this edited volume of Proceeding RACE-2011. This proceeding, we believe, shall be acknowledged in future as a land mark for acquiring a good understanding of the potential role of Recent Advances in Civil Engineering to solve the problems. Themes covered in the proceedingare not only quite relevant but also carries a lot of weightinaddressingCivilEngineeringproblemsandseekthesolutionsusingrecent research/innovations which took place till today. For the reasons mention above this proceeding is more informative and worth full for researchers. It is exciting to know that recent problems in CivilEngineeringe.g.Earthquakeresistantstructures,WaterHarvesting,Avalanches,Flood Mitigation,Geo-environmentalproblem,SolidWasteEngineering,GIS&RemoteSensing, AppliedGeologyetc.whichcallforgreaterattentionforthecountryandhasbeenaddressed among the various papers of the proceeding. This volume includes one hundred twenty research papers which are peer reviewed. We highly appreciate the efforts of all researchers, scientists and engineers from different parts of the country who readily accepted and responded to our request andsubmittedthefullpaperswellintime.Most ofthepapersofthisproceedingarebasedon original data base and organized properly. WemustacknowledgethefinancialsupportprovidedbyMinistryofEarthScience,Govt.of India,NewDelhi,Head,SERCDivision,DepartmentofScienceandTechnology,NewDelhi, Director General, Councilfor Scientific and Industrial Research, New Delhi,Ministry ofWater Resources, New Delhi, Head, Department of Civil Engineering and Banaras Hindu University to organizetheNationalconferenceonRecentAdvancesinCivilEngineering(RACE-2011) during 14th to 16th October, 2011 and bringing out this proceeding in present shape.ThisproceedingisacommendableattempttohighlighttheRecentAdvancesinCivil EngineeringamongtheresearchersofdifferentdisciplinesofCivilEngineering.Allpapersare properlyarrangedcoveringthevariedaspectsofCivilEngineeringproblems.However,still therearemanygapsintherelevantknowledgewhichcanbefulfilledbybringingoutmore volume on similar themes in future. We are pretty sure that this proceeding will be useful to the post graduate students researchers working in the field of Civil Engineering.

Editors RECENT ADVANCES IN CIVIL ENGINEERING-2011ii CONTENTS OF THE PROCEEDINGSPAGE No EDITORIAL DESKi CONTENTSiivii 1VARIABILITYOFMASSTRANSFERCOEFFICIENTINCONTROLLEDCHAMBER EVAPORATION EXPERIMENTS C.S.P. Ojha, Hiroshi Yasuda, Surampalli Rao, Mohamed A. M. Abd Elbasit, Manoj Kumar 1-6 2ASMOOTHDMS-FEMFORMULATIONFORMINDLINPLATEBENDING PROBLEMS S. Narayan,D. Roy, R. M. Vasu 7-12 3ENHANCINGTHEREACTIVITYOFFLYASHFORGEOTECHNICALAND GEOENVIRONMENTAL APPLICATIONS P.V. Sivapullaiah 13-21 4SORPTIONBEHAVIOUROFDEEPSEATEDCOAL:AGEOENGINEERING APPROACH TO CARBON DIOXIDE SINKING V. Vishal, T. N. Singh 22-26 5CHARACTERISTICS OF BADLANDS MORPHOLOGY Sanjay Tignath,Medha Jha 27-30 6PERFORMANCE GRADING OF INDIAN BITUMINOUS BINDERS G. Bharath, M. Amaranatha Reddy 31-36 7RECENT DEVELOPMENTS IN NITRATE REMOVAL TECHNIQUES FROM WATER P.K. Singh, Y.C. Sharma, A. L. Srivastav 37-47 8SWELLINGBEHAVIOUROFEXPANSIVESOILMIXEDWITHLIMEANDFLYASH AS ADDITIVES Dhirendra Kumar, Suresh Kumar, Bala Ramudu Paramkusam 48-51 9USEOFMULTIPLICATIVEDECOMPOSITIONMETHODFORBUSTRAVELTIME PREDICTION UNDER HETEROGENEOUS TRAFFIC CONDITIONS S.Vasantha Kumar,Lelitha Vanajakshi 52-57 10INFLUENCE OF TYPE OF BINDER AND CRUMBRUBBER ON THE MORPHOLOGY OF CRUMB RUBBER MODIFIED BITUMEN P. S. Divya, J. Murali Krishnan 58-62 11HYDROINFORMATICS IN GROUNDWATER EXPLORATION: A CASE STUDY D.K. Pardeshi, A.O. Rathor, D. G. Regulwar 63-68 12APPLICATION OF RS AND GIS FOR APPROXIMATING STREAM FLOW Om Prakash Dubey 69-73 13SEISMIC VULNERABILITY ASSESSMENT OF SKEW BRIDGES S.P. Deepu, S. Ray-Chaudhuri 74-80 14GEOCHRONOLOGICALCONSTRAINTSOFEASTERNDHARWARCRATON:A REVIEW D. Prakash, P. Chandra Singh, S. Tewari, I.N. Sharma 81-87 15VELOCITIES IN NON-UNIFORM FLOW ON CLOSELY PACKED ROUGH BED Kesheo Prasad, C.S.P. Ojha, K. M. Singh 88-94 16ANALYSIS AND CONSTRUCTION OF A LOW COST FOLDABLE DOME Rajesh Kumar, Aniruddh Vashisth, Vineet Singla 95-99 17GROUNDWATERPROSPECTINGOFURBANAREAOFTHEJABALPURDISTRICT USING REMOTE SENSING AND GIS Medha Jha, Sanjay Tignath 100-102 18PARAMETRICSTUDYOFPREDICTIONOFSCOURDEPTHINSPILLWAYS-A NEURAL NETWORK APPROACH Arun Goel 103-107 19URBAN INFRASTRUCTURE PLANNING USING GIS A CASE STUDY OF SURAT Shri Chetan R. Patel, Nikunj B. Shah 108-111 RECENT ADVANCES IN CIVIL ENGINEERING-2011iii 20ASTUDYONLANDFILLLEACHINGUSINGTHREEDIMENSIONALCOLUMN LEACH APPARATUS P.J .Barman, S.A. Kartha,B. Pradhan 112-115 21WATERSCARCITY:ISSUES,CONCERNSANDOPTION(ACASESTUDYOF KOLKATA AND SUB- URBAN AREAS) Sabita Madhvi Singh 116-121 22SITESELECTIONFORGROUNDWATERRECHARGEUSINGTREATED MUNICIPAL WASTEWATERS- A CASE STUDY OF VARANASI (INDIA) P.K.Singh, Anurag Ohri, Abhishek Kumar Bhardwaj 122-130 23ANALYSIS OF GAS DYNAMICS IN A FUNNEL DURING AIR SUCTION K.K. Pandey, Aniruddh Vashisth 131-136 24EVOLUTIONOFSOMERESISTANCELAWFORNON-UNIFORMACCELERATED FLOW OVER CLOSELY PACKED ROUGH BED Kesheo Prasad, C.S.P. Ojha, K. M. Singh 137-143 25FACTORS AFFECTING BEHAVIOUR OF PILED RAFT FOUNDATION Rajendra Singh Bisht, Baleshwar Singh 144-150 26OPTIMISATIONOFLOWRISEMULTI-STOREYBUILDINGUSINGSTEEL-CONCRETECOMPOSITESECTION&SEMI-RIGIDBEAMTOCOLUMN CONNECTIONS P. S. Buradkar 151-156 27ARTIFICIALNEURALNETWORKSFORREALTIMERESERVOIRINFLOW MODELING AND PREDICTION A.K. Pardeshi, D.G. Regulwar 157-163 28FLOOD FORECASTING SYSTEM IN KOSI BASIN L. B. Roy, Sunil Kumar, Jay Prakash Kumar 164-169 29PUSHOVER ANALYSIS OF RCC BUILDING WITH AND WITHOUT INFILL WALL R. E. Dalvi 170-174 30DEFORMATION AND STRENGTH CHARACTERISTICS OF FLY ASH ADDED SOILS Tapas Das, Baleshwar Singh 175-180 31GIS: AS A NECESSARY TOOL FOR URBAN PLANNING Arati S. Petkar,Eshwar M. Bahirat 181-186 32CANAL REGULATION AND SCADA Kinnari R. Mishra, N.K.Sherasia 187-189 33DELINEATIONOFGROUNDWATERSATURATEDFRACTUREZONESINHARD ROCK TERRAINS USING INTEGRATED RESISTIVITY SURVEY APPROACH N.P. Singh 190-194 34VISCO-ELASTO-PLASTICANALYSISOFLAYEREDPLATE:AUNIFIEDOVERLAY APPROACH P. Srimani, S. Roy Chowdhury, K. K. Ghosh, 195-200 35AUTOMATED HIGHWAY SYSTEMS S. Lakshminarasimhan,K.S. Yuvaraaj 201-204 36ACASESTUDYOFMULARESERVOIRSEDIMENTATIONASSESSMENTUSING BATHYMETRY ALONG WITH SATELLITEREMOTE SENSING TECHNOLOGY D.D.Bhide,, P.R.Bhamare,S.N.Kulkarni 205-209 37REVIEW ON USE OF RECYCLED AGGREGATES IN CONCRETE G.D. Ransinchung R.N, Kumar Praveen,Chauhan Prakash Arun 210-214 38EFFECTOFLOADINGFREQUENCYONPERFORMANCEOFRCBEAM-COLUMN CONNECTIONS C. Marthong,A. Dutta, S.K. Deb 215-220 39STUDIESONTHETREATMENTOFVEGETABLEWASTEWITHHIGHSOLID ANAEROBIC DIGESTERS S. Venkatesh 221-226 40PROTOTYPEDEVELOPMENTFORABUSARRIVALTIMEPREDICTIONSYSTEM UNDER INDIAN TRAFFIC CONDITIONS Akhilesh Koppineni, Krishna Chaithanya, K. Sidharth,Lelitha Vanajakshi,227-233 RECENT ADVANCES IN CIVIL ENGINEERING-2011iv 41CORROSIONMONITORINGTECHNIQUESFOREVALUATIONOFPERFORMANCE OF STEEL IN CONCRETE STRUCTURE Jitu Kujur, B. Bhattacharjee 234-238 42ASSESSMENT OF CONCRETE STRENGTH USING FLYASH AND RICE HUSK ASH Satish D. Kene, Pravin V. Domke, Sandesh D. Deshmukh, R.S. Deotale 239-245 43PILE FOUNDATIONS DESIGN AND CONSTRUCTION M. Sujan 246-249 44MODELINGQUEUING&DISCHARGEBEHAVIOUROFHETEROGENEOUS TRAFFIC AT MANUALLY CONTROLLED URBAN INTERSECTION Ghadiyali G.A., Shah M.K., Katti B.K. 250-255 45SENSITIVITY ANALYSIS OF SHALLOW FOUNDATION RESPONSES Prishati Raychowdhury, Sumit Jindal 256-261 46DUSTDEPOSITIONCAPACITYOFCERTAINROADSIDEPLANTSINAIZAWL, MIZORAM: IMPLICATIONS FOR ENVIRONMENTAL GEOMAGNETIC STUDIES Prabhat Kumar Rai 262-270 47DISASTERMITIGATIONANDMANAGEMENTFORFLOODCONTROLUSING GEOINFORMATICAL TOOLS M.B.Kumthekar, P.K.Deshpande, R.D.Padhye, S.B.Thalange 271-275 48DYNAMICANALYSISOFLAMINATEDCOMPOSITEFOLDEDPLATEFOR DIFFERENT BOUNDARY CONDITIONS Sourav Chandra, Sreyashi Das, A. Guha Niyogi 276-279 49CONCRETEBEAMSSTRENGTHENEDWITHNEARSURFACEMOUNTEDAFRP RODS V.G. Kalpana,K. Subramanian 280-282 50DAMAGE ANALYSIS OF STEEL STRUCTURES Pranav P. Pande 283-285 51STREAM FLOW MODELLING OF BEAS RIVER AT MANALI, HIMACHAL PRADESH S. K. Jain, S. P Rai, Rajeev S. Ahluwalia 286-289 52RECENT DEVELOPMENTS IN THE MEASUREMENT OF WETTING SWCC C. Malaya, S. Sreedeep 290-293 53CHANNELANDCOREDUNITFORROOFING\FLOORINGFORLOWCOST HOUSING: A REVEIW Rajesh Kumar,Harshit Jain, S. B Dwivedi 294-298 54A STUDY ON DUCTILITY OF CONCRETE AND MORTAR WITH THE ADDITION OF RUBBER POWDER C. B. K. Rao, R. Bhaskar,V. Rajendra Prasad 299-305 55EFFECTSOFAGGRESSIVECHLORIDEENVIRONMENTONHIGHPERFORMANCE STEEL FIBRE REINFORCED CONCRETE Dhirendra Singhal, Bal Krishan 306-310 56THE INFLUENCE OF GEO-ENVIRONMENTAL PROPERTIES ON MUNICIPAL SOLID WASTE Sunita Kumari,A.K. Nema, K.B. Ladhane 311-316 57CEMENTANDPOLYPROPYLENEFIBRESTOIMPROVETHELOADBEARING CAPACITY OF PEAT BehzadKalantari,Arun Prasad 317-322 58EFFECTOFNaOHMOLARITYONCOMPRESSIVESTRENGTHOFGEOPOLYMER CONCRETE IN AMBIENT CURING CONDITION S. V. Joshi,M. S. Kadu 323-327 59ECONOMICSANDSTUDYUNDERBORDERIRRIGATIONONGROUNDNUTCROP AT VARIOUS WATER APPLICATION LEVELS A.B. Rathod,B.Jigar Parekh 328-330 60PETROGRAPHICEXAMINATIONOFCORROSIONINHIBITORSADMIXED CONCRETE B.N. Singh, S.K. Singh, V. Kumar, M.A. Quraishi 331-335 RECENT ADVANCES IN CIVIL ENGINEERING-2011v 61DETECTIONOFACTIVELANDSLIDEAREASINHIMALAYASUSINGSMALL BASELINE SUBSET INTERFEROMETRY A. Bhattacharya,Manoj. K. Arora, Mukat. L. Sharma 336-342 62TIME PERIOD ANALYSIS OF LIQUID FILLED CYLINDRICAL CONTAINER DURING SLOSHING P. R. Maiti, Abhinav Srivastava 343-348 63AVERAGE P-T ESTIMATES AND MINERAL CHEMISTRY OF THE TWO PYROXENE BEARINGGRANULITESFROMSONAPAHAR,WESTKHASIHILLSDISTRICT, MEGHALAYA S.B.Dwivedi, K.Theunuo 349-354 64CRACK SENSING CAPABILITIES OF SMART CANTILEVER STRUCTURES Nilanjan Mallik 355-359 65ESTIMATIONOFGUSTEFFECTIVEFACTORUSINGIS875(PART3)1987AND MODIFICATIONS PROPOSED BY PREMKRISHNA (2002) Deepak Kumar, S. Mandal 360-364 66ANALYSIS OF SKEW SLAB DECK USING ANALYTICAL METHODS Rajesh Kumar, Veerendra Kumar, Bikram Kesharee Patra 365-369 67REMOVALMECHANISMOFCr(VI)ANDFe(III)METALIONSFROMEXPAN-SIVE SOIL BY ELECTROKINETIC EXTRACTION Ch. Ramakrishna, B. S. Nagendra Prakash, P.V. Sivapullaiah 370-376 68APPLICATIONOFISOTOPICTECHNIQUESINHYDROLOGYOFMOUNTA-INOUS REGION S. P. Rai, Bhishn Kumar, Y.S. Rawat, P. Purushothaman 377-381 69GROUNDWATERZONESAROUNDJHANSITOWNUSINGSATELLITE-REMOTE SENSING DATA IN THE GRANITIC TERRAIN OFBUNDELKHAND S.P. Singh, Yamini Singh 382-387 70INFLUENCEOFCRUSHABILITYANDMIGRATIONOFMOISTUREONCBR CHARACTERISTICS OF POND ASH M.V.S. Sreedhar, G.Venkatappa Rao, R.Ramesh Reddy, 388-391 71PERFORMANCE HISTORY OF WARM MIX ASPHALT Rajan Choudhary, Eleena Gao, Ashok Julaganti 392-395 72DETERIORATIONOFGROUNDQUALITYDUETOOPENCASTCOALMINESAND OTHER RELATED INDUSTRIES IN SINGRAULIAREA Raj K. Sharma, C.S. Singh,. 396-399 73STABLEISOTOPICCHARACTERISATIONOFGROUNDWATERSINBIST-DOAB REGION, PUNJAB P. Purushothaman, M.S. Rao, B. Kumar,Y.S. Rawat, Gopal Krishan 400-404 74LINEAR AND NON LINEAR ANALYSIS OF SQUARE PLATE Ashish Johri, Rajesh Kumar, Veerendra Kumar 405-407 75LOCALSTRATEGICPLANNINGPROCESSESANDSUSTAINABILITYTRANSITIONS IN INFRASTRUCTURE SECTORS Rajesh Singh, P.K.S. Dikshit 408-416 76ENVIRONMENTALCAPACITYCONCEPTINNOISEPOLLUTIONANALYSISOFA ROADWAY Kanakabandi Shalini, Brind Kumar 417-420 77ESTIMATION OF SCOUR DEPTH AROUND BRIDGE PIERS BYUSING HEC-RAS Dilip Kumar, Hira Lal Yadav, Sushil Kumar Himanshu 421-425 78COMPARATIVE STUDY OF BUILDING WITH COMPOSITE ANDRCC STRUCTURE A.K. Chitkeshwar, V.G. Meshram 426-432 79SHEARSTRENGTHOFORGANICSOILTREATEDWITHCEMENTGROUTAND MODIFIED WITH ADDITIVES SinaKazemian, Arun Prasad 433-437 80POTENTIALEVAPOTRANSPIRATIONASSESSMENTBYUSING MWSWATMODELING ON SABARMATI RIVER Shaikh M Zuned, P P Lodha 438-439 RECENT ADVANCES IN CIVIL ENGINEERING-2011vi 81LOCATIONOFWATERHARVESTINGSTRUCTURESINSEETHARIVERBASINOF COASTAL KARNATAKA BY RS AND GISPERSPECTIVE A CASE STUDY Mohandas Chadaga, Ravindranath, Sreeniwas Poudel S., Varun Kadian 440-444 82REVIEW ON SELF COMPACTING CONCRETE Kumar Praveen, G.D. Ransinchung R.N, Jindal Abhishek 445-449 83A STUDY ON SHOTCRETE TECHNOLOGY Vipin Bansal, Abhishek Choudhary, Dipendu Bhunia, Ashutosh Parauha, Nikhil Shrivastava 450-456 84SOMESTUDIESONTHEINFLUENCEOFCLAYON CONSOLIDATIONCHARACTERISTICS OF COHESIVE SOILS S.V. Rao 457-460 85NONDIMENSIONALANALYSISOFACIRCULARPLATERESTINGONTHE PASTERNAK FOUNDATION USING THE STRAIN ENERGY APPROACH Ashish Gupta, Dilip Kumar 461-468 86OPTICALSENSORBASEDCRACKOPENINGDISPLACEMNETSSTUDIESIN CONCRETE K. Samrajyam,B.Sobha, T D Gunneswara Rao 469-471 87DYNAMIC ANALYSIS OF NAILED OPEN CUTS- A CASE STUDY Bhishm Singh Khati, Dilip Kumar, Swami Saran 472-479 88COMPARATIVESTUDYOFOPTIMALCROPPINGPATTERNUSINGLINEAR PROGRAMMING AND GENETIC ALGORITHM Ingle Visha, D.G.Regulwar 480-488 89ACRITICALEVALUATIONOFFOLLOWINGPHENOMENAININDIANMIXED TRAFFIC CONDITIONS K.V.R. Ravi Shankar, Tom V. Mathew 489-491 90FEMFORMULATIONOFDIRECTMETHODOFCONCRETEGRAVITYDAM- NONLINEAR FOUNDATION INTERACTION A Burman, D Maity, A Kumar, S Bhushan 492-496 91DEVELOPMENT OF A STIFFNESS ANALYSIS FOR A STRUCTURAL ELEMENT IN A BUILDING INFRASTRUCTURE Pradeep Kumar, Swatantra K. Porwal 497-501 92EMERGING TECHNOLOGIES FOR COLOUR REMOVAL Shiva Shankar Y, Abhishek Kumar, Harshit Jain, Devendra Mohan 502-506 93COMPARATIVEANALYSISOFBEDLOADFORMULASFORINTENSEBEDLOAD CHANNELS Sumit Talukdar,Bimlesh Kumar,Subashisa Dutta 507-512 94ANALYSISOFROUGHNESSANDCRACKINGONSELECTEDFLEXIBLE PAVEMENT IN BANGALORE CITY Prathima G 513-518 95INVESTIGATIONS OF MIXING IN MECHANICALLY STIRRED TANK:COMPARISON OF CD-6 IMPELLER AND RUSHTON TURBINE Thiyam Tamphasana Devi 519-524 96ESTIMATIONOFHEAVYMETALCONTAMINATIONFROMMUNICIPALSOLID WASTE LANDFILL AT KOLKATA USING, EPACMTP MODEL Sanjukta Basak, Subhasish Chattopadhyay, Swapan Kumar Mukhopadhyay, Amit Dutta 525-532 97BEHAVIOUROFSUCTIONCAISSONASFOUNDATIONSYSTEMFOROFFSHORE WIND TURBINE IN SANDY SOILS Suchit Kumar Patel, Baleshwar Singh 533-537 98DYNAMICANALYSISOFMULTISTORIEDBUILDINGUSINGFLATSLAB, M.S.Janbandhu, V.G. Meshram 538-547 99TECHNO-ECONOMIC STUDY OF REMOTE AREA SMALL HYDRO POWER PLANTS Mon Prakash Upadhyay, Rahul Bhatt, Nitin Kumar Sahu 548-550 100APRAGMATICAPPROACHOFPOTHOLESREPAIRUNDERINDIANSERVICE CONDITIONS G.D. Ransinchung R.N, Praveen Kumar,Brind Kumar 551-557 RECENT ADVANCES IN CIVIL ENGINEERING-2011vii 101ANALYSISOFTHEMEANMONTHLYTEMPERATURESEVENHOMOGENEO-US REGIONS OF INDIA R.K.S. Maurya, G. P. Singh 558-562 102RELATEAVERAGECONCENTRATIONOFSEDIMENTTOLOCAL CONCENTRATION FOR STEADY UNIFORM FLOW N.D. Vernekar, A.R. Bhalerao 563-565 103GEOTECHNICALPROPERTIESOFSOILCONTAMINATEDBYTANNING INDUSTRY EFFLUENTS Ashwani Jain 566-568 104WATERMANAGEMENTONDRIPANDMICROSPRINKLERIRRIGATIONFOR SUMMER GROUNDNUT CROP UNDER DEFICIT WATER SUPPLIES A.B.Rathod, S. A. Trivedi 569-573 105A REVIEW OF NOISE ANALYSIS FOR ROAD TRANSPORT SYSTEMS Brind Kumar, G.D. Ransinchung R.N., Saurabh Gupta, Gajendra Kumar Yadav, Alok Kumar 577-583 106BEARING CAPACITY OF POND ASH REINFORCED WITH A COIR GEOTEXTILE M.V.S. Sreedhar,G. Venkatappa Rao,R. Ramesh Reddy 584-587 107PERFORMANCEOFCONCRETEFILLEDSTEEL(CFT)HOLLOWBEAMSECTIONS IN FLEXURE Sunil Kute, T.Jayashree,Shantanu Pande 591-594 108FORECASTINGOFGROUNDWATERLEVELOFSIMGATEHSIL,RAIPUR DISTRICT, CHHATTISGARH USING ARTIFICIAL NEURAL NETWORK Umank Mishra, K.K. Pandey, S B Dwiwedi 595-602 109REDUCTION OF FLUORIDE WITH FREE JETS FOR INDUSTRIAL APPLICATIONS Suresh Kumar N., Ravi Kumar, S. 598-601 110COMPARATIVEBEHAVIOUROFTWOSTOREYBRICKMASONRYBUILDING MODELS OF CONFINED, UN-CONFINED AND MODEL AS PER IS CODE Vikash Khatri, P. K. Singh, P.R. Maiti 602-609 111DAMAGEESTIMATIONOFR.C.CSTRUCTURALMEMBERBYDISPLACEMENT BASED ANALYSIS TECHNICS A.Vimala, Ramancharla Pradeep Kumar 610-613 112LONG-TERM STATISTICAL EXPRESSION OF TEMPERATURE OVER INDIA R. K. S. Maurya, G. P. Singh 614-617 113 EFFECTSOFCORROSIONINHIBITORONTHEPERFORMANCEOFMILDSTEEL AND PRESTRESSING STEEL Vasugi Jegan , Vandhana Mary Jacob,Radhakrishna G. Pillai 618-624 114REMEDIALMEASURESFORRESTORATIONOFDISTRESSESINPARTPARALLEL TAXIWAY AND APRON A CASE STUDY OF LAL BAHADUR SHASTRI AIRPORT AT BABATPUR, VARANASI Brind Kumar, Pramod Kumar Singh 625-634 115ANEXACTSOLUTIONOFMHDFLOWDUETOACCELERATEDMOTIONOF INFINITE POROUS PLATE J. Singh, Munna Lal, Dheeraj Agrawal, Mona Goyal 635-637 116GPS DERIVED STRAIN ANALYSIS AND CRUSTAL DEFORMATION IN INDIA Abhay P. Singh Abhishek Rai 638-642 117MODELLING HETEROGENEOUS TRAFFIC USING VISSIMA.K. Maurya, S. Sharma, S. Biswas 643-647 118RECENTADVANCESINHYDROLOGICALANALYSIS,MODELING,AND DECISION - MAKING 648-655 R.D. Singh, M. Goel 119STATIC AND DYNAMIC STABILITY OF PLATES AND SHELLS656-662 L.S. Ramachandra120EFFECT OF BRIDGE PIERS SHAPE ON SCOURING Manvendra Singh Chauhan, U. K. Choudhary, P. K. Singh Dikshit 663-666 AUTHOR INDEX667-669 ISBN 978-81-921121-0-7 RECENT ADVANCES IN CIVIL ENGINEERING-20111 VARIABILITYOFMASSTRANSFERCOEFFICIENTIN CONTROLLED CHAMBER EVAPORATION EXPERIMENTS C.S.P. Ojha Dept. of Civil Engineering, Indian Institute of Technology, Roorkee, India Hiroshi Yasuda Arid Land Research Center, Tottori University, Japan Surampalli Rao USEPA, Kansas, USA Mohamed A. M. Abd ElbasitDesertification Research Institute, National Center for Research, Khartoum, Sudan Manoj Kumar Dept. of Civil Engineering, Indian Institute of Technology, Roorkee, India ABSTRACT: Evaporation is estimated usingmass transfer, energyoracombinationapproach whichincludes mass transfer as well as energybased approach.In the literature, there are a large number of studies which use masstransferapproachasthebasisofestimatingevaporation.Inthisstudy,thepotentialofmasstransfer approachareevaluatedusingdatafromcontrolledchamberexperimentsinwhichradiationandwindvelocity werekeptconstantandtemperatureandrelativehumidityprofileswerevariedindifferentpatterns.Currently, FAOprocedureliststhreeapproachestocomputeairvapourpressurebasedontemperatureandrelative humidity profiles. In this study, the impact of using different procedures of estimating air vapour pressure is also examinedto assess the useofmass transfer approach forestimatingevaporation.To achieve this,apartof the dataisusedtocalibratemasstransfercoefficientwhichissubsequentlyusedtoprojectevaporationforfuture states.Accordingly, strategies are ranked for their potential in estimating evaporation. KeyWords:Masstransfercoefficient,Vapourpressure,Evaporationrate,Variability,Relativehumidityand Temperature.

INTRODUCTION Evaporation is the phenomenon by which a substance is convertedfromliquidphasetovapour.Evaporationis relevantinmanydisciplinesfromsmalltoverylarge scale.Itisimportantindeterminingthewaterbalance ofwatersheds,allowingpredictionandestimationof runoff and ground water recharge. Evaporation data are requiredinmanagingbothirrigationanddryland farming operations. The amount and rate of evaporation fromwatersurfacesaretheparametersessentialfor designerstoplanstoragereservoirsofwastewaterand brineponds.Suchinformationisalsoneededin determiningtheevaporationfromnaturallakes, irrigationscheduling,condensercoolingwaterand waterrequirementsforhydroelectricpower,aswellas settling of various water-right disputes, etc.Infact,ifthemanistosurviveandimproveliving standards onearth, somethingmust be done to increase the supply of fresh water. One method of increasing the availablewatersupplycouldbetodecreaseorprevent evaporationfromwatersurfaces.Evaporationnotonly reducestheamountofavailablewater,butitalso decreasesthequalityofwaterandincreasesimpurity concentrationbecauseonlypurewaterislostby evaporation. For all the above-mentioned reasons, there isaneedforbetterunderstandingoftheevaporation process.Itisthereforenecessarytoexaminethe evaporationprocessandcontributingfactorsandto developamethodofaccuratelypredictingevaporation rates from water body. Evaporationresearchwasstartedwiththepioneer workofSirDaltonin1802whoobservedthat evaporationrateiscloselyrelatedtovapour pressuredifferencebetweenevaporatingsurface andthesurroundingair.Thisledtotheclassical Dalton equation, also called aerodynamic equation. Tanner and Sincalir (1983) reported that the vapour pressure deficit is a dominant factor influencing the evaporation.Vapourpressuredenotesthepartial pressure exerted by the watervapour present in the air.Vapourpressureatequilibriumstateiscalled thesaturationvapourpressure(Ps).Itisafunction oftemperatureonly;itincreaseswithincreasein temperature(Dingman,1994).Evaporationrate fromanopenwatersurfacedependsonthe differencebetweenthevapourpressureofwater and air (Pa) (called vapour pressure deficit), vapour pressureofwaterbeingthesaturationvapour pressureforthetemperatureofwater.Thus,the vapourpressuredeficit(Ps-Pa)isanindicatorof actualevaporativedemandofair(Yoderetal 2005).Evaporationcontinuesaslongasvapour pressureofwatersurfaceexceedsthevapour pressureoftheair(TrewarthaandHorn,1980). SinghandXu(1997)reportedthatthemass transferequationstodeterminetheevaporationare sensitive to vapour pressure gradient. Gianniou and Antonopoulos (2007) observed that the particularly lowerevaporationratesaresensitivetoairvapour pressure.Winter(1981)reportedtheerrorin RECENT ADVANCES IN CIVIL ENGINEERING-20112 determiningtheevaporationrateswhichalsodepends onvapourpressuredeficit.Ithasbeenfurtherreported thattheerrorsof25%couldbeintroducedin calculatingtheu(Ps-Pa)term(ubeingthewind velocity)inthemasstransferequationifthe correspondingerrorsinairandwatertemperaturesis 1%. Ojha et al (2010) reported the errors in evaporation and compared the errors with three different models.

Airvapourpressuresarenotdirectlymeasuredbyany instrument.Howeveritsvaluedependsonthe measurementoftemperatureandrelativehumidity which is most commonly used in practice. A number of methodsareavailablefortheestimationofvapour pressure and vapour pressure deficit. Jansen et al (1990) reportedproceduretoestimatethevapourpressure. Allen et al. (1998) reported three models for estimating vapourpressurefromtemperatureandrelative humidity. Chuck and Sparrow (1987)investigated experimentally the evaporation characteristics of a rectangular pan with wateratthebottompaneloftheductandturbulentair was passed through the duct and over the surfaceof the water.Thethermalconditionsoftheairandwaterand evaporation rate were measured. The height of water in thepanwasvariedandtwopanlengthsof12.5and 27.9cmwereused.Adamsetal.(1990)studiedthe evaporationfromheatedwaterbodies.Thedischarge comingoutofthenuclearplantwastakenforstudy wherethetemperatureofwaterwasapproximately700 C.Theoreticalanalysishadbeenusedtoexamine variousevaporationregimesandfoundthatatheated pondsaswindspeedincreases,thethermalloading decreases.Paukenetal.(1999,1995and1993) performedexperimentsbyevaporatingheatedwater fromacircularpaninalowspeedwindtunnel.The evaporationboundarylayerthatresultedwasa combinedturbulentforcedandturbulentfree convectionboundarylayer,wheretheforced convectionwasdominatedbytheairvelocityandthe freeconvectionwasaresultofthedensitydifference betweentheairatthesurfaceofthewaterandthe ambientair.Itwasfoundthat30%oftheevaporation rate was due to free convection and only 10% by forced convection and the rest was by combined free and force convection. Withtheabovebackground,thefollowingobjectives are set for the present study. 1.Toperformcontrolledchamberexperimentsin whichradiationandwindvelocityarekept constant. 2.Toproduce differentvariationsof temperatureand relativehumiditytolookintovariabilityofair vapourpressureasitdependsontemperatureand relativehumidityofairasperFAO(Foodand Agriculture Organization) procedure. 3.Toestimateairvapourpressureusingdifferent approachessuggestedbyFAOandtousethisin developingseveralstrategiesforcomputing mass transfer coefficient. 4.Toevaluatethedifferentstrategiesofmass transfercoefficientscomputationsin estimationofevaporationforfuturestatesand torankthemonthebasisoftheirabilityto forecast future states. DESCRIPTION OF EXPERIMENTAL SETUP Toachievetheaboveobjectives,theexperiments wereconductedincontrolledprogrammable chamberwithbaseareaequalto7.5m2.The chamberislocatedatAridLandresearchcentre, Tottori,Japan.Theradiationandwindvelocity werekeptconstantthroughouttheexperiments. Theaveragevalueofradiationandwindvelocity was75.1Wm-2 (theaveragelightintensitywas 50,000lux)and0.9ms-1,respectively.The chambertemperature(T)andrelativehumidity (RH)havebeenprogrammedtofollowfourT/RH patterns;namely:increasingTdecreasingRH (T/RH1),decreasingTincreasingRH(T/RH2), increasingTincreasingRH(T/RH3),and decreasingTdecreasingRH(T/RH4)showninfig 1.Themaximum,average,andminimum temperature was 30, 20, and 10C, respectively. On theotherhandthemaximum,average,minimum relativehumiditywas75,50,and25%, respectively. The starting temperature and humidity at the four T/RHpatterns was the average. The rate of change in temperature and relative humidity was 2Ch-1,and5%h-1,respectively.Eachpatternhas tack 24 hours to complete its cycle. To measure the waterevaporation,asteelevaporationpanwith 19.8cmdiameterwasplacedonelectronicscale (SartoriusLP-2200P,Goettingen,Germany)with 0.01gaccuracy.Thescalewasprogrammedto transfertheweightdataevery10minutetoa personalcomputershowninfig2.Theair temperatureandrelativehumiditywasalso measuredin10minutetimeintervalusingthermo recorder sensor (RS-12, ESPEC MIC Corp., Osaka, Japan).Theradiationfluxdensityinthechamber wasmeasuredusingpyranometerinWm-2(PYR, DecagonDevices,Washington,USA).The pyranometerwasfixedonthechamberfloorand connectedtoadatalogger(Em50,Decagon Devices, Washington. USA). Fig 1 Temperature and humidity patterns applied to the chamber RECENT ADVANCES IN CIVIL ENGINEERING-20113 Fig 2 Computer-controlled chamber (a)general view of the chamber and (b) experimental setup THEORETICAL BACKGROUND Themasstransfercoefficientdependsonthevapour pressure difference (Pv) between the air and surface of water,evaporationrate(me)andisalsoafunctionof wind velocity f (u). The evaporation rate (me) is directly proportional to pressure difference of vapour, i.e. .( ) evm P (1) Invokingdependenceofmasstransfercoefficienton windvelocitythroughafunctionf(u),onecanalso express eq. (1) as.( )( ) evm Kf u P (2) where,K istheproportionalityconstantand is denoted as mass transfer coefficient. Therefore, .( )evmKf u P(3) Inequation(3)meistakenastheevaporationratein g/m2hr, u asvelocity inm/sec and(Pv) is the pressure difference of vapour in kPa, expressed as( )v s aP P P (4) where,Psthevapourpressureatthesurfaceofwater and Pa is the vapour pressure of air. Ps can be computed usingsteamtablesorapproximaterelationships.One such relationship(Subramanya 2007) is17.27( ) 4.584exp( )237.3s sat sTP P TT (5) where, T is the temperature in degree C ToestimatePa,FAOrecommendsthreeapproaches.According to first approachmax min[ ( ) ( )] / 2a mean s sP RH P T P T (6) According to second approachmin (max) max (min)[ ( ) ( ) )] / 2a s sP P T RH P T RH (7) According to third approachmaxmin( )100a sRHP P T (8) Ineq.(6)Tminisminimumtemperature,Tmaxis maximumtemperatureandRHmeanistheaverage relative humidity which is computed asmax min( ) / 2meanRH RH RH (9) where,RHminisminimumrelativehumidityand RHmax is maximum relative humidityBasedon useof eqs (6), (7) and (8)forcomputing Pa,sevenstrategiesareconsideredtocalculatethe valueofmasstransfercoefficientKineq.(2). These strategies are listed below:Strategy 1 Ps (transient) and Pa (transient) Instrategy(1),PsandPaaretakentransientatany giventimecorrespondingtomeasuredtemperature and relative humidity. Strategy 2 Ps (transient) and Pa (constant)

In strategy (2), Pa is computed from eq. (7) Strategy 3 Ps (average) and Pa (constant)

In strategy (3), Ps is obtained by considering all the values of Ps in a given time period (one day) and Pa is computed from eq. (7) Strategy 4 Ps (average) and Pa (average) Instrategy(4),PsandPaareobtainedconsidering all the value of Ps and Pa in a given time period. Strategy 5 In strategy (5), average value of Ps and Pa are used. Ps (average) is computed as Ps (average) = (Ps (max) + Ps (min))/2 (10) and Pa (average) = RH (average)*Ps (average) (11) where,RH(average)iscomputedas(RHmax+ RHmin)/2(12) Strategy 6 Pa = Ps (Tmin) RHmax, and Ps (transient) is taken similar to strategy 1. Strategy 7 Pa = Ps (Tmin) RHmax and Ps (Average)

ANALYSIS OF EVAPORATION DATA Underdifferentvariationsoftemperatureand relativehumidity,evaporationinthepanshowed the following variation, as given in the fig.3. As per thestrategy(1),PsandPa areconsideredas transient at any given time. Using eq. (5), Ps can be computedasafunctionoftime.Similarly, multiplyingPsatanytimewiththecorresponding relativehumidity,Pacanbeobtainedasafunction oftime.Forexample,correspondingtodataof temperatureandrelativehumidity,thevariationof PsandPawithtimeisshownfordayoneinfig4 and5.Thevaluesofevaporationrateisshownin fig.3andfromtheeq.(2),themasstransfer coefficient is computed and is shown in fig. 6. Fig. RECENT ADVANCES IN CIVIL ENGINEERING-20114 6canbealsousedtocomputeaveragevaluesofmass transfer coefficient for day one. 0501001502002503000 2 4 6 8 10 12 14 16 18 20 22 24Time (hr)Evaporation rate (g/m2hr) Fig.3 Variation of evaporation rate with time 05001000150020002500300035004000450050000 5 10 15 20 25 30Time (hr)Ps (kPa) Fig.4 Variation of Ps with time 020040060080010001200140016000 5 10 15 20 25 30Time (hr)Pa(kPa) Fig.5 Variation of Pa with time Usingthedataoffig.3,masstransfercoefficientis computed using different strategies and thevariation of the same is shown in the fig. 6 RESULTS AND DISCUSSION Fig.3showsthetransientvariationofevaporationrate. It canbe seen that between 0 hrs to 10 hrs, evaporation rateincreasessharplywhilevariationissmallfrom11 hrsto24hrs.Itshowstheevaporationrateinan intervalof10minutesduringtheexperimentalperiod. Fromtheanalysisofevaporationdata,thevariation betweenthemasstransfercoefficientsareobtained usingdifferentstrategiesandalltheseisshowninfig. 6.Tostudytheimpactofusingthesemasstransfer coefficientsevaporationhasbeenestimatedforday2, 3, 4 and 5 of controlled experiments. Day one is used to calibratemasscoefficientandthiscalibratedmass transfercoefficientisusedtocomputeevaporationfor otherdays.Theobjectiveofthisistoidentifythebest performing strategies.Figs.7to10showthecomputedevaporationusing different strategies along with observed evaporationfor daytwotofive.Fromcomparisonofperformanceof different strategies in fig. 7 to 10, it can be seen that the strategy(4)givesthebestagreementbetween computedandobservedevaporationforallthe days.Inreallife,itisverydifficulttohave continuous monitoring of temperature and RH; thus useofstrategy(4)maynotbealwaysfeasible. Thus,toscreenoutthenextbestperforming strategy,fig.7to 10favour the use ofstrategy (5).Itcanbeseenthatthestrategiesconsideredinthis workcanbeclassifiedintothreecategoriesasper the data availability. Strategy (1) and(2) pertain to thecategorywhenthedataavailabilityisnota concernandtransientvariationoftemperatureand RHareavailable.Howeverinmanysituations, such type of data availability lacks.In view of this, one can think of using FAO procedure in which the dataavailabilityisrestrictedtoTmin(minimum temperature), Tmax (maximum temperature), RHmin (minimumrelativehumidity),RHmax(maximum relativehumidity)andevaporationinagiventime period (typically one day). Strategies 5, 6 and 7 fall into this category. The FAO approach also suggests computingPausingwhenthedataonRHmin isnot available, as in strategy (7) Itisinterestingtoseethatstrategies3,4and5 producedlowerestimatesofevaporationthanthe observedone.Onthecontrary,strategies1,2,6 and 7 yield higher estimates of evaporation than the observed one. Inthiswork,theconsideredstrategieshavea potentialofbeingusedindifferentsituationsof dataavailability.Theexperimentaldatain controlledchamberexperimenthasoffered simultaneousevaluationofthesestrategieswhich otherwisewouldhavebeenverydifficultto achieve.Fromtheanalysisofdata,itisapparent thatthereisnosubstituteforthelackofprecise databaseontemperature,RHandevaporation. However,underdatalimitedconditions,the strategies5to7offerbetterpotentialfor application,aspertheanalysisofpresent experiments.Furtherdetailsonthesestrategiesare available in Ojha et al. (2011 b). 020040060080010001200140016000 2 4 6 8 10 12 14 16 18 20 22 24Time (hr)KK (Strategy 1)K (Strategy 2)K (Strategy 3)K (Strategy 4)K (Strategy 5) K (Strategy 6)K (Strategy 7) Fig 6 Variation of constant K with different approaches RECENT ADVANCES IN CIVIL ENGINEERING-20115 0501001502002503001 2 3 4 5 6 7StrategiesEvaporation rate (g/m2 hr)Observed Computed Fig.7Comparisonofobservedandcomputed evaporation rate for second day 0501001502002503001 2 3 4 5 6 7StrategiesEvaporation rate (g/m2 hr)Observed Computed Fig.8Comparisonofobservedandcomputed evaporation rate for third day 0501001502002503001 2 3 4 5 6 7StrategiesEvaporation rate (g/m2 hr)Observed Computed Fig.9Comparisonofobservedandcomputed evaporation rate for fourth day 0501001502002503001 2 3 4 5 6 7StrategiesEvaporation rate (g/m2 hr)Observed Computed Fig.10Comparisonofobservedandcomputed evaporation rate for fifth day CONCLUSIONS 1.Masstransferapproachhasthepotentialof being used as an estimator of evaporation. 2.Therecanbealargenumberofstrategiesto evaluatemasstransfercoefficientsandthese strategiesvary depending on the availability of data. In this work, the use of seven strategies is demonstrated to estimate K. 3.Strategies arefoundtofallinto twocategories intermsoftheirunderestimationor overestimationofevaporation.Strategiesgive thesimilarperformanceforallthedaysof forecasts. 4.Strategybasedonaveragesaturationvapor pressure and average air vapor pressure, where averageisbasedontheconsiderationof completedataincalibrationrange,isfoundto outperform other strategies. NOMENCLATURE ACRONYMS RHRelative humidity ENGLISH SYMBOL Asarea of evaporating surface (m2) cmcentimetre Dhhydraulic diameter of pan (m) DPan diameter (m)gAcceleration due to gravity (m/sec2) Kmass transfer coefficientmmetre .mmass flow rate of air (kg/sec) .e mmass flow rate of water vapour from pan (kg/sec) sP partial pressure of water vapour at the surface of watersatPsaturated water vapour pressure (Pa) aP vapour pressure of air Ppressure (Pa) Ttemperaure (K) uvelocity of air (m/sec) GREEK SYMBOLS dynamic viscosity (Ns/m2)kinematic viscosity (m2/sec) SUBSCRIPTS aair Avgaverage minminimum maxmaximumssaturationvwater vapour RECENT ADVANCES IN CIVIL ENGINEERING-20116 REFERENCE AdamsEE,Cosler,DJ&Helfrich,KR.1990, Evaporationfromheatedwaterbodies:Predicting combinedforcedplusfreeconvection.Water Resour. Res., 26: 425-435 . AllenRG,PareiraLS,RaesD,&Smith,M.1998, Cropevapotranspiration:guidelinesforcomputing cropwaterrequirements.Irrigation andDrainage Paper. No. 56, F.A.O., Rome, Italy.ChuckW&SparrowEM.1987,Evaporativemass transferinturbulentforcedconvectionductflows. InternationalJournalofHeatandMassTransfer. 30: 215-222. DingmanSl.1994,Evapotranspiration.Physics Hydrology.McMillanPub.Co.,NewYork,256-265. GianniouSKandAntopoulosVZ.2007,Evaporation andenergybudgetinLakeVegoritis.Journalof Hydrology. 345: 212-223. IncroperaFP&DeWittDP2002,Fundamentalsof Heat and Mass Transfer. Fifth ed., John Wiley & Sons, New York, 465-531. JensenME,BurmanRDandAllenRG.1990, Evapotranspirationandirrigationwater requirements.ASCEmanualsandreportson engineering practice No. 70, ASCE, New York. OjhaC.S.P.,KhobragadeS.D.andAdeloyeA.J. 2011a,Estimatingairvapourpressureinasemi-aridregionusingFAO-56.JournalofIrrigation and Drainage Engineering. ASCE. Ojha C. S. P., Yasuda H., Rao S., Abd Elbasit M. A. M. andKumarM.2011b,Evaporationinrelationto CO2concentration:Analysisofmasstransfer coefficientAtmosphericEnvironment(inpress) PaukenMT.1999,Anexperimentalinvestigation ofcombined turbulent free and forced evaporation. ExperimentalThermalandFluidScience18:334-340. PaukenMT,FarleyB,JeterSM&Abdel-Khalik. 1995,Anexperimentalinvestigationofwater evaporationintolow-velocityaircurrents. ASHRAE Trans 101: 90-96. PaukenMT,TangTD&JeterSM.1993,Anovel methodformeasuringwaterevaporationintostill air. ASHRAE Trans.99: 297-300. SinghVPandXuCY.1997,Sensitivityofmass transferbasedevaporationequationstoerrorsin dailyandmonthlyinputdata.Hydrological Process, 11: 1465-1473. SubramanyaK.2007,EngineeringHydrology.Tata McGraw-Hill, New Delhi, 74. Tanner C B and SincalirT R. 1983, Efficient water use in crop production: research or research.American Society of Agronomy, Inc., Madision, Wisc.1-27. TrewarthaGTandHornLH.1980,Atmosphericand precipitation.AnIntroductiontoClimate,5th edition,McGrawHillsBookCo.,NewYork.,41-53. WinterTC.1981,Uncertaintiesinestimatingthe water balance of lakes. Water Res. Bul. 17: 82-112. Yoder R E, OdhiamboL Oand Wright W C. 2005Effectsofvapourpressuredeficitandnet irradiancecalculationmethodsonaccuracyof standardizedPenman-Monteithequationina Humidclimate.JournalofIrrigationDrain. Engg., ASCE 131: 228-237. (2005) ISBN 978-81-921121-0-7 RECENT ADVANCES IN CIVIL ENGINEERING-20117 ASMOOTHDMS-FEMFORMULATIONFORMINDLINPLATE BENDING PROBLEMS S. Narayan Computational Mechanics Lab, Department of Civil Engineering, Indian Institute of Science Bangalore, India D. Roy Corresponding Author, Computational Mechanics Lab, Department of Civil Engineering, Indian Institute of Science Bangalore, India, Email: royd@civil.iisc.ernet.in R. M. Vasu Department of Instrumentation and Applied Physics, Indian Institute of Science Bangalore, India

ABSTRACT:Themainpurposeofthisarticleistointroduceastrictlyhigherordercontinuousand polynomialreproducingfunctionalapproximationwithinanFE-likedomaindiscretizationforthenumerical solutionofMindlinplatebendingproblems.WhiletheclassicalC0shapefunctions,typicallyusedwiththe FEM, is known tosuffice to tackletheweakform corresponding to suchproblems, it is demonstrated through several numerical illustrations that the higher order inter-element continuity, possessed by the presently adopted approximation scheme (referred to as theDMS-FEM), indeed playsaremarkably significant role inimproving the numerical accuracy, convergence characteristics and locking-resistant nature of the computed response.

INTRODUCTION Platebendingconstitutesanimportantclassof problemsinengineeringapplicationsofsolid mechanics.Ofthetwowellknownplatetheories, KirchhoffplateandMindlinReissner,theformerhas seenwideapplicationstosolvebendingofthinplates withnegligiblesheardeformation.Amajor complication in the finite element (FE) implementation ofthistheoryistherequirementofuniformlyC1 continuityofshapefunctionsforapproximatingthe displacementfield.Usingdeflection(w)androtation ()asindependentfieldvariables,however,partly eases the requirement ofC1 continuity and thishasled todevelopmentofthediscreteKirchhofftriangle (DKT) element.Here, the conditionof zerotransverse shearstrainissatisfiedatsomediscretepointsinthe elementalong the edges.The basic idea is tocompute the shear strains not directly from the derivatives of the displacementsbutatafewdiscretecollocationpoints usingthedisplacementsalone.Thisallowsfora continuousrepresentationthroughtheirinterpolation overtheelement(s)viaappropriateshapefunctions. Fig. 1TUBA 6 and TUBA 15 elements EffortsatenforcingC1continuityofshapefunction acrossinterelementboundaryhavebeenmadeby taking higher order derivatives of displacement field as degreesoffreedom(DOF-s).TheTUBAfamilyof plateelements(Argyrisetal.1968)isanexampleof suchelements.Theseelementsarebasedoncomplete polynomial displacement functions of order n 5. TotalDOF-srequiredforcompletepolynomial functionofordernis 12(n+1)(n+2).Fig.1shows nodesandcorrespondingDOF-sforTUBA-6(n= 5) and TUBA-15 (n = 7) elements. A problem with theTUBAfamilyistheuseofnormalvectorsto definetheDOF-s,especiallygiventhatavectorto be normal to a surface is not preserved by an affine transformation. Thus a triangle described in natural co-ordinateisnotaffineequivalenttoonein Cartesianco-ordinate.Bell(1969)hasproposeda modifiedTUBA elementbyexpressing the normal derivativeateachmid-pointasalinear combinationoffirstandsecondderivativesofthe function at the corresponding end points. Mindlin-Reissnerplatetheoryaccountsforshear deformation. An advantage of the Mindlin-Reissner modeloverthebiharmonicplatemodelisthatthe energyinvolvesonlyfirstderivativesofthe unknownsandsotheso-calledconformingFE approximations require, inprinciple, only C0 shape functions instead of their C1 counterparts necessary forthebiharmonicmodel.HoweverMindlin-Reissner plate elements exhibit shear locking as the thicknessoftheplateapproacheszero.Shear lockingischaracterizedbythenumerically generatedincorrecttransverseforcesunder bending.Earlymethodshavetriedtoovercome shearlockingbyreducedintegrationoraselective reducedintegration.Theideahasbeentosplitthe strainenergyintotwoparts,oneduetobending andtheotherduetoshear,andusedifferent integrationrulesforthetermsinvolvingbending andshearstrains.Hughes(1982)hasdevelopeda lineartriangle(i.e.a3-nodedtriangleusinglinear shapefunctions)elementusingtheso-calledone pointcentroidalquadratureforintegration. Quadratictriangularelementshavealsobeen RECENT ADVANCES IN CIVIL ENGINEERING-2011 8 exploredtoovercomethisproblem.Szeetal.(1997) havedevelopedT6/3* changing thequadrature rule for integrationoveroriginalT6/3(Szeetal.1997).Zhongnian(1992)hasreporteda3-nodedtriangular elementwith3basicDOF-spernodeand2internal rotationalDOF-s(11DOF-s)usingselectivereduced integrationontheshearenergyterm.Reduced integration results in zero-energy modes and instability duetorankdeficiency.Theenhancedassumedstrain (EAS)methodcanbeusedtoavoidlocking phenomenaaswell(SimoandRifai1990).Szeetal. (1997)havedevelopedanAST-6elementby optimizingsampledstrainsofT6/3*.Theelement T6/3-B3(ZienkiewiczandLefebvre1988)has3 bubbleheterosisnodesasrotationalDOF-sand6 boundarynodes.TRI-6isanassumedstrainquadratic elementthatdoesnothaveabubblenode.MITC7 (Batheetal.1989)isanotherquadratictriangular strain element having a bubble heterosis node. Wang et al.(2004)havetracedshearlockinginMindlin-Reissnerplateformulationstotheinabilityofthe approximationfunctionstoreproducetheKirchhoff mode,andtotheinabilityofthenumericalmethodto achievepurebendingexactness(BE)intheGalerkin approximation.Intheirstudy,theKirchhoffmode reproducingcondition(KMRC)isensuredfor Mindlin-Reissnerplates.Theapproximationfunctions for displacements and rotations are constructed to meet theKMRC.Theyhavederivedintegrationconstraints forachievingBEandproposedacurvaturesmoothing method(CSM)tomeetthebendingintegration constraints. Mesh-freemethodsdevelopedmorerecentlyallow adequateflexibilityincustomizingtheapproximation functionsforthedesiredsmoothness.Inmesh-free methodswithstabilizedconformingnodalintegration (SCNI)(Chenetal.2001),thedomainisdiscretized into cells that in a sense define the field of nodes (such asthecellsofaVoronoidiagram).Integrationis performedalongtheedgesofeachcell.Although mesh-freemethodssuchastheelement-freeGalerkin (EFG)obtaingoodaccuracyandhighconvergence rates,thenon-polynomialapproximationspace increases the computational overhead while evaluating theshapefunctionsandtheweakforms.Recently,the socalledsmoothedfiniteelementmethod(SFEM),an interfaceofthe SCNI with the FEM,hasbeenapplied totwodimensionalelasticityproblems(Liuetal. 2007).ItisshownthattheSFEMisstable,accurate and effective. Otherrecentattemptsatestablishingsuchhandshake strategiesthatcombinetheFEMandmesh-free methods include the NURBS-based smooth parametric methods (Shaw and Roy 2008, Shaw et al. 2008) and a smoothFEMbasedontriangularB-splinesorDMS-splines(SunilkumarandRoy2010a,2010b; Sunilkumaretal.2011).Thelatterclassofmethods (DMS-FEM),whichtypicallyusetriangulationor tetrahedralization for domain discretization (as in a classofmesh-freemethods)whilstadopting polynomialreproductionforderivingtheshape functions(asintheFEM),havebeenshownto possesssuperiorconvergenceandlocking-resistant characteristicsvis--vistheFEMforafairly generalclassoflinearandnonlinearelasticity problems.Theaimofthisworkistoextendthe DMS-FEMforMindlinplatebendingproblems andassessitsperformancefordifferentordersof polynomialreproduction.DMS-Splines(DMS beinganacronymforDahmen,Micchelliand Seidel, authors who introduced the spline; Dahmen etal.1992)areessentiallyweightedsumsof simplexsplinesovertriangles.DMS-Splinesof ordernareCn-1 continuousacrosstheinter-triangularboundaries.Indevelopingtheplate bendingelement,nonumericalartifices,typically employedintheFEliterature,havebeenused. Numericalresultsarecomparedwiththosevia AST-6andMITC7,twoprominentlyusedplate bendingelementsallowingforsheardeformation. Theresultingelement,whichispracticallyfree fromthicknesslockingandspuriouszeroenergy modes,alsoshowsconspicuouslybetteraccuracy than its FE counterparts. DMS-FEM Shape Functions DMS-splines are weighted sums of simplex splines, which areamultivariate generalization of the well-knownunivariateB-splines.Adegreensimplex splineconsistsofafamilyofsmooth,degreen piecewisepolynomialfunctionseachdefinedover asetof dim1 n N + + points,anelement dimNe x Rof which is called a knot andthe set of knots,theknotset.Here dimN denotesthedomain dimension (presently we have dim2 N = ). Simplex splineshave 1 nC continuityifnothreeknotsare collinearintheknotset.ThedomainofDMS-splines is aproper triangulation.A degreenDMS-splinebasisfunctionatapoint 2x eRisdefined overthecontrolpoints Ic|andisgivenby

( )( )det | VIIM x V ||,where a ( )| VIM x| is simplex splinesof degree n and

IV| ,atriangleconsistingoftheendknotsof the knotclouds of the three vertices of the triangle I, correspondingtoacontrolpoint Ic|,i.e.

{ }, ,0 1 20 1 2II I IV v v v || | |= .Thenumberofcontrol pointsisequalto ( 1)( 2)2n n + +in 2R . RECENT ADVANCES IN CIVIL ENGINEERING-2011 9 Dependingontheknotlengthwhichistypicallya smallfractionofthesmallesttriangularside,aDMS-splineisnearlysupportedoveratriangle(more precisely,itissupportedoverthepolygonformedby connecting the knots located close to the vertices of the triangle).EveryDMS-splineisconstructed correspondingtoanodalpointxinthetriangletothe half-openconvexhullofwhichxbelongs.Ingeneral, DMS-splinessatisfythepartitionofunityandtheir derivatives(includingthesplinesthemselves)are globallysmooth.Nevertheless,ithasbeen conclusivelydemonstrated(SunilkumarandRoy 2010a)thatfunctionalapproximationsdirectlybased onthesesplinescouldbeinaccurateowingtotheir sensitivedependenceontheknotlocations.Such sensitivityislargelyovercomebyintroducinga polynomialreproductionstrategywhileconstructing theshapefunctions,withDMS-splinesactingas kernelsintheintegralfunctionalrepresentation. Specifically,lettheshapefunctionsberequiredto reproduceacompletesetofpolynomialsupto degree p n s . Now, the approximant( ) , u x yo fora targetfunction( ) , u x y inaboundeddomainis given in the standard form: 1( , ) ( , )Nndi iiu x y x y uo==

(1) where ndN representsthenumberofnodesin, ( ) ,i i iu u x y = isthefunctionvalueatparticleiand ( ) ,ix y arethegloballysmoothshapefunctions given by:( , ) ( , ) ( , ) ( , )Ti i i i ix y x x y y x y x x y y | = H b

(2) ( ) ,Tx y H isasetofmonomialsdefinedas{ }| | px yo |o| + s,( , ) x y b arecoefficientfunctions determined below and( ) ,i ix x y y | is the DMS-splinebasedat( ) ,i ix y actingasthekernelfunction. Thecoefficients( , ) x y b areobtainedbasedonthe following reproduction conditions: 1( , ) 1ndNiix y== (3) 1( , )( )Nndi i iix y x y x yo | o |==| | p o | + s

(4) Fr om Eq.4 ( ) ( )( ) | || |,01( , )Nndi i iix y x x y yo |o | o= =| | p o | + s (5) From Eqs. 2 and 51( , ) ( , ) ( , ) ( , )NndTi i i i i iix x y y x y x x y y x x y y |= H b H (0) = H (6)Eq.6canbewrittenas( , ) ( , ) (0) x y x y = M b Hwhere,( , ) x y = M1( , ) ( , ) ( , ) ndNTi i i i i iix x y y x x y y x x y y |= H His themomentmatrixandthecoefficientvectoris thus given by: The global shape function in 2D is finally given by: ( , )ix y =

1( , ) ( , ) (0) ( , )Ti i i ix x y y x y x x y y | M H H (5)

MINDILIN PLATE Consistentwiththebasicpostulatesofthe Reissner-Mindlinplatetheory,onedescribesthe displacementfields,in-plane( ) u,v and transverse ( ) w ,purelythroughrotations ( , )x yu u , and the transverse displacement of themid-layer as (Fig. 2):( , ); ( , ); ( , )x yu z x y v z x y w w x y u u = = =(6) Fi g.2 Ki nemat i cs ofM i ndl i n pl at edef or mat i on Strainsonplanesparalleltothemiddlelayerare thus given by: ; ;y y x xx y xyz z zx y y xu uu uc c c c( c c= = = + (c c c c (7) andthoseinthetransversedirectionareobtained as: ;xz x yz yw wx y u u ( c c(= + = + ( (c c (8) Forisotropic,homogeneousmaterials,bending moment resultants are thus derivable as: DL M u = (9) Wherex y xyM , M , MT ( = M ;,Tx yu u( = ; 321 01 012(1 )10 02Etvvvv ( ( (= ( ( ( D; 00xyy x (c (c ( ( c= (c ( ( c c (c c L. Here E and vare Youngs modulus and Poissons ratio,ttheplatethickness.Accountingforsimilar materialconstitution,thetransverseshearforce resultants are given by: RECENT ADVANCES IN CIVIL ENGINEERING-2011 10 | | w o = + V S; Gt o k = .(10) Here G is theshearmodulus of thematerialandk the shearcorrectionfactor(valueofk ispresentlytaken tobe5 6).Fromthemoment-shearandlinear momentum balance we get:0 o V =TL DL + ( ) w+ (11) | | ( ) 0Tw o V V+ + = q (12) subjecttoappropriateprescriptionsofboundary conditions. This is an irreducible system corresponding to minimization of total potential energy.1( , )2Tw d u u uOH = O}(L ) D(L ) ( ) ( )Tw w d u o uO+ V V O}(13)

btwqdO O+ H} (15) DISCRETIZATIONANDNUMERICAL IMPLEMENTATION Properdomaintriangulationispresentlyensuredby Delaunaytriangulation.Numberofcontrolpoints ( )np in 3R requiredforatrianglecorrespondingto ordern DMS-splineis( 1)( 2)2n n + +.Their locationsdefinewherethecontrolpointsinfluence more.Projectionsofcontrolpointscoincidewiththe nodesof np nodedLagrangetriangle.Consistentwith themesh-freeliterature,projectionsofcontrolpoints on triangles may be called particles. Their functionality isthesameasthatofnodesintheFEM.Fororder2 DMS-splines,thetotalnumberofparticlesrequiredis 6 and for order 3 it is 10. Particles and knots for DMS-spline of order 3 are shown in Fig. 3.The field variables are interpolated as 11 3 3.. . . ..nnex p eyepwou uo ( = = ` ` ) )I I N(14) where,I3isthirdorderidentitymatrix,and Tei i xi yiw u u( = isthevectorofkinematic degree of freedoms at the ith node.Substituting Eq. 16 in Eq.9 and Eq.10 we get xxzx b e s eyzxyzcc = = ` ` ) )B B (15) (a) DM S or der3 (b) DM S or der2 Fig. 3 Control points and knotcloud ThustakingvariationinEq.(15)andsubstituting Eq. 17, we obtain discretized form as:312T Tb b s s etd d oO O| |O+ O |\ .} }B DB B B T Td dO I= O+ I} }N N q S(16) whereqisappliedloadvectorandStheapplied traction vector fields. This can be written as:e = K f (17) where K is the stiffness matrix given by: 312T Tb b s std d oO OO+ O} }K = B DB B B (18) InthisformulationKisintegratedusingseven pointgaussquadraturerule.Giventhenon-polynomial rational natureof the DMS-FEM shape functions,useofsuchlargernumberofquadrature pointsisonlyexpectedandanoptimalnumberof suchpointsmustbefounda-priorithroughsome numerical experiments. NUMERICAL RESULTS In thefirst example, a clamped squareplate of side LandwithvaryingLtotratioissubjectedtoa centralpointload.Giventhesymmetry,onlya quadrant of the plate is modeled by 8 elements, see Fig.4.Thecentraldeflections,normalizationby thin plate solution (Timoshenko 1959) aspredicted bytheDMS-FEMandcomparedwiththeAST-6 elementforvaryinglengthtothicknessratio,are presentedinTable1.ThesolutionsviatheDMS-FEM( ) 3 n = notonlyshowhighernumerical accuracy (in terms of their closeness to the solution correspondingtothethinplatelimit),butalso exhibitsignificantlyreduceddeteriorationasL/t increases. Fi g.4 Tr i angul at i on ofa Quadr antofsquar e RECENT ADVANCES IN CIVIL ENGINEERING-2011 11 Table1Normalizedcentraldeflectionofclamped square plate subjected to central point loadL/t ratio AST-6DMS-FEM (n=3) 1020.8720.961 1030.7120.923 1040.7070.913 1050.7070.913 Forthesecondsetofexamples,againconsidera squareplateof1000 L t = .Asbefore,onlya quadrantismeshedusingDelaunaytriangulation(Fig. 3).Inthefirstcase,theplateisassumedtobe subjectedtoacentralpointloadand,inthesecond, uniform pressure. Both simply supported ( ) w=0and fullyclampedboundary conditions( )x yw=0; =0; =0 areconsidered. For numerical work with the DMS-FEM, we have used shapefunctionscorrespondingtoboth2 n = and 3 n = .Thepredicteddeflectionsatthecentre, normalizedwithrespecttothethinplatelimitas before,arelistedinTables2and3.WhiletheDMS-FEM( ) 2 n = showsaslightlypoorerperformance via-a-vistheMITC7orAST-6elements(Fig.5), resultsviaDMS-FEM( ) 3 n = showssignificantly improvedaccuracyincomparisonwithbothofthese elements (Fig. 6). Forthethirdandfinalexample,weadoptMorleys skew platemodel, i.e. a skew rhombic plate with skew angle 30,allsidessimplysupportedandsubjected uniformpressure.ThesameisanalyzedusingDMS-FEM( ) 3 n = .Centraldeflections,asnormalizedby thinplateseriessolutionofMorley(1963)and comparedwiththesolutionsbasedontheAST-6 element,areshowninFig.7.DMS-FEMclearly outperforms the AST-6 scheme once again. Fig.5 Simply supported square plate with a central point load Fig. 6 Simply supported square plate with uniformly distributed loaded Table2Deflectionofasquareplate ( 1000 L t = ) via DMS-FEM( ) 3 n =no ofelements simply supportedFully clamped udlpoint load udlpoint load 181.00004230.9794370.9237410.916524 361.00049060.9889490.9857320.970960 621.00069390.9982450.9964850.994884 1061.00044300.9964210.9986430.993420 2361.00042651.0000170.9998741.001465 Table 3 Deflection of a square plate ( 1000 L t = ) via DMS-FEM( ) 2 n =no ofelements simply supportedFully clampedudlpoint loadudlpoint load 260.9666400.9154730.2225310.24941 360.9687990.9181160.3844130.40163 620.9852700.9603690.6640720.71643 1060.9940970.9747480.8732080.88119 2360.9979200.9901560.9676510.96645 Fig. 7 Morleys skew plate CONCLUDING REMARKS ThesmoothDMS-FEM,whichenablesC1orstill higherordercontinuityuniformlyoverabounded geometricaldomainofinterest,isextendedand exploredforMindlinplatebendingproblems. Whilethedomaindiscretizationisvia triangulation,bearingclosesimilaritywiththeone RECENT ADVANCES IN CIVIL ENGINEERING-2011 12 employedwithinthestandardFEframework,the generationofthepolynomialreproducingsmooth shapefunctionsrequirestheadditional usageofknots, whoseplacementmustsatisfyanon-collinearity constraint, marks a departurefrom the usual FEmesh. WhilecomputingtheDMS-FEMshapefunctions (typicallyneededatthequadraturepoints)is computationallyinvolved,theextranumericalworkis amply compensated through a significant improvement inconvergenceandaccuracyofsolutionsvia-a-vis thoseviasomeofthebestknownfiniteelements,e.g. AST6orMIT7forMindlinplates.Asevidenced throughthepresentednumericalwork,this distinguishingfeatureoftheDMS-FEMismore pronounced as the thin plate limit is approached. Work iscurrentlyunderwaytoextendtomethodto nonlinearplatesandshells,includingformulations involvingfiniterotationsandgeometricallyexact kinematics. REFERENCES Batoz, J.L., Bathe,K.J. & Ho,L.-W.( 1980)A study ofthree-nodetriangularplatebendingelementsInternationalJournalforNumericalMethodsin Engineering,JohnWiley&Sons,Ltd,,15(12), 1771-1812 Lee,S.W.&Zhang,J.C.(1985)Asix-nodefinite elementforplatebending InternationalJournalforNumericalMethodsin Engineering, John Wiley & Sons, Ltd, 21, 131-143Papadopoulos, P. & Taylor, R. L. (1990) A triangular elementbasedonReissner-Mindlinplatetheory InternationalJournalforNumericalMethodsin Engineering,JohnWiley&Sons,Ltd,30(5), 1029-1049Sze,K.Y.,Zhu,D.&Chen,D.P.(1997),Quadratic TriangularC0PlateBendingElement InternationalJournalforNumericalMethodsin Engineering, JohnWiley& Sons, Ltd, 40(5), 937-951Zienkiewicz,O.,Taylor,R.,Papadopoulos,P.& Oate,E.(1990)Platebendingelementswith discreteconstraints:Newtriangularelements Computers & Structures, 35(4), 505 - 522Argyris, J.H., Fried, I. & Scharpf, D. W. (1968) The TUBAfamilyofplateelementsforthematrix displacementmethod AeronauticalJournal,72, 701-709 Zhongnian,X.(1992)Athick-thintriangularplate elementInternationalJournalforNumerical Methods in Engineering,JohnWiley& Sons,Ltd, 33(5), 963-973Zienkiewicz,O.C.&Lefebvre,D.(1988)Arobust triangularplatebendingelementoftheReissner-MindlintypeInternationalJournalfor Numerical Methods in Engineering, John Wiley& Sons, Ltd, 26(5), 1169-1184Bathe,K.J.,Brezzi,F.&Cho,S.W.(1989)The MITC7andMITC9Platebendingelements Computers & Structures, 32(3-4), 797 - 814Wang,D.&Chen,J.S.(2004)Locking-free stabilizedconformingnodalintegrationfor meshfreeMindlin-Reissnerplateformulation ComputerMethodsinAppliedMechanicsand Engineering, 193(12-14), 1065 - 108Nguyen-Xuan,H.,Rabczuk,T.,Bordas,S.& Debongnie,J.(2008)Asmoothedfinite elementmethodforplateanalysisComputer MethodsinAppliedMechanicsand Engineering, 197(13-16), 1184 - 1203Sunilkumar,N.&Roy,D.(2010)ASmooth Finite Element Method Basedon Reproducing KernelDMS-SplinesCMES:Computer ModelinginEngineering&Sciences,65(2), 107-153 Morley,L.S.D.(1963)SkewPlatesand Structures, Pergamon Press, Oxford. Timoshenko,S.&Woinowsky-Krieger,S(1959) TheoryofPlatesandShells, McGraw-Hill, New YorkZienkiewicz,O.C.&TaylorR.L.(2000)The FiniteElementMethodvolume2:Solid Mechanics Butterworth-Heinemann, Oxford ISBN 978-81-921121-0-7 RECENT ADVANCES IN CIVIL ENGINEERING-2011 13 ENHANCINGTHEREACTIVITYOFFLYASHFOR GEOTECHNICAL AND GEOENVIRONMENTAL APPLICATIONS P.V. SivapullaiahProfessor, Department of Civil Engineering, Indian Institute of Science, Bangalore 560012 Email: siva@civil.iisc.ernet.in ABSTRACT: Theuseofabundantlyavailableflyashneedstobepromotedtoavoiddisposalproblemsand associatedenvironmentalhazards.Mostbulkusesofflyashesdependonitspozzolanicnature.Enhancingthe pozzolanic reactivity can enhance its useforbulkgeotechnical andGeoenvironmentalapplications.The useof gypsum,apartfromlime,candecreasenotonlyitslimeleachabiitytosustainandenhanceitspozzolanic reactivitywhichinturnenhancesitsstrengthandvolumechangebehaviour.Thiscanalsoreduceitshydraulic conductivityoneofthemainlimitationforitsapplicationasbarriermaterialforwastedisposalfacilities. Amendinggypsumtoflyashreducestheleachabilityoftraceelementspresentinflyashandimprovesthe retention capacity for different contaminants in leachates.

PRODUCTION OF FLY ASH Currentenergypoliciesworldwideconsiderthermal powergenerationasamajorsourceofpowerfor industrialdevelopment.Flyashgeneratedfromthe combustionofcoalpresentlycomprisesabout900 milliontonseveryyear.Withthecommencingof superpowerthermalpowerplantsandtheincreasing useoflowgradecoalswithhighashcontents,the productionofashcurrentlyisaboutonethousand million tons per annum in India alone. ENVIRONMENTAL CONCERN OF FLY ASHES Managementanddisposalofsuchhugequantitiesof fly ash arebound to create tremendous environmental stressunlesstheutilizationrateofflyashisnot increasedbyasignificantorderofmagnitude.Itcan causeallthethreeenvironmentalrisks-air,surface waterandgroundwaterpollution.Airpollutionis causedbydirectemissionoftoxicgasesfromthe power plants as well as wind-blown ash dust from ash mound/pond.Theair-bornedustcanfallonsurface watersystemorsoilandmaycontaminatethe water/soil system. The wet systemof disposal in most powerplantscausesdischargeofparticulateash directlyintothenearbysurfacewatersystem.The long storageof ash in ponds under wetcondition and humidclimatecancauseleachingoftoxicmetals fromashandcontaminatetheunderlyingsoiland ultimatelythegroundwatersystem.Asflyisfineand light,itcaneasilyspreadbywindintotheregions leewardsidesoftheashdumps.Intheseregionsfly ash will be everywhere onthe beds, diningtables and of course in human eyes, ears, and lunges.Thehealthhazardsandenvironmentalimpacts resultfromthemobilizationoftoxicelementsfrom ash.Presenceofflyashinlungesmaycauseserious lungdiseaseslikesilicosis,fibroses,bronchitis, pneumonitis,etc.Mobilizationofvariouselements from the ash into the environment depends on climate, soils, indigenous vegetation and agricultural practices. Bulkquantitiesofflyashareusuallystoredwet rather than dry so that fugitive dust is minimized. The resultingimpoundments(ponds)aretobetypically large and stable for long periods, but any breach of their damsorbundingwill be rapid andon amassive scale.Sometraceelementsincoalarenaturally radioactive.Theseradioactiveelementsinclude uranium (U), thorium (Th), and theirnumerous decay products,includingradium(Ra)andradon(Rn)etc. However,thevastmajorityofcoalsandmostofthe flyashesarenotsignificantlyenrichedinradioactive elements, or in associated radioactivity. TYPES OF ASH Fly Ash Itconsistsofinorganic,incombustiblematterpresent in the coal that has been fused during combustion into aglassy,amorphousstructure.Flyashmaterialgets solidified while suspended in the exhaust gases and is collectedintheelectrostaticprecipitators.Thelighter one,whichgoesupthechimneyandgetscollected eitherinmechanicalorelectrostaticprecipitators,is knownasflyash.Someportionofflyashescapes alongwithhotgasesthroughchimney.Sincethe particles are solidified while suspended in the exhaust, theyaregenerallysphericalinshapeandrangein sizes from 0.5 micron to 100 micron. Fly ash is such a complexmaterialthatitscharacterizationindetailis quitedifficult.Withthetypeofflyashitsphysical, chemicalandengineeringpropertiesvaryduetothe following factors. Typeofcoalused:Flyashes,producedfrom burninganthraciteorbituminouscoalshave pozzolanicproperties. Flyashes producedfrom ligniticorsubbituminouscoalshave cementitiouspropertiesinadditiontothe pozzolanic ones.Thetreatmenttowhichthecoalhasbeen subjected prior to combustion. The method of combustion. Furnace temperature Amount of air circulation Collection and storage places adopted RECENT ADVANCES IN CIVIL ENGINEERING-2011 14 Bottom AshBottom ash refers to the non-combustible constituents ofcoalwithtracesofcombustiblesembeddedin forming clinkers and sticking to the hot side walls of a coal-burningfurnaceduringitsoperation.The clinkersfallbythemselvesintothewateror sometimes by manual poking, and get cooled. Pond Ash Flyashandbottomashareoftensluicedintoponds andlagoons.Suchheterogeneousmaterialscollected inpondarereferredtoaspondash.Bottomash accountsfornearly20%andflyashfor80%ofthe total ash produced.PROPERTIES OF FLY ASH Ashpropertiesmayalsovarywithinthesameboiler atvarious times in response tovaryingdemands.The chemicalandmineralogicalcompositionsand physical and morphological properties of fly ashes are summarized in the following sections. Chemical Composition Theelementalcompositionofflyashishighly variableandisdirectlyrelatedtothesourceofcoal, itspretreatmentandtheoperationoftheplantwhile burningthecoal.Flyashisanaluminosilicateglass consistingoftheoxidesofSi,Al,FeandCawith minoramountsofMg,Na,K,ZnandSandvarious trace elements. Theprincipal constituents of flyashes aresilicondioxide(SiO2),aluminumoxide(AI2O3), ironoxide(Fe2O3),calciumoxide(CaO)andcarbon content.AshalsocontainssmalleramountsofMgO, TiO2,Na2OandK2O;andverysmallquantitiesof other 20 to 50 elements.The concentrationassociated with the ash maybe either adsorbedon the surfaceof particleorincorporatedintomatrix(Natuschetal., 1974).TherangeofchemicalcompositionofIndian coal ashes is reported in Table 1. Table1RangeofchemicalcompositionofIndian coal ashesCompoundsFly AshPond AshBottom Ash SiO238-6337.7-75.123-73 Al2O327-4411.7-53.313-26.7 TiO20.4-1.80.2-1.40.2-1.8 Fe2O33.3-6.43.5-34.64-10.9 MnO0-0.5bd-0.6bd-0.3 MgO0.01-0.50.1-0.80.1-0.7 CaO0.2-80.2-0.60.1-0.8 K2O0.04-0.90.1-0.7bd-0.56 Na2O0.07-0.430.05-0.31bd-0.3 LOI0.2-3.40.01-20.90.61-51.6 bd: below detection; LOI: loss on ignition Mineralogy The mineralogy of fly ash depends to a great extent on itschemicalcomposition.Thephasesofminerals whichexistinflyasharegovernedbytheimpurities oforiginalcoalsandcombustiontemperature (Hubbardand Dhir, 1985). Themineralogy of flyash alsodependsonthecombustionmethods.Theboiler operatingathightemperaturestendstohavealarge amountofcrystallinephase.Thusthedrybottomfly ash has higher content of crystalline phase than that of thewetbottomone.Thisalsoresultsinlower pozzolanic activity of the dry bottomfly ash than that ofthewetbottomflyashcomparingatthesame particlesize.Flyashcanbemineralogicallydivided into three major categories. 1.Amorphous Glass 2.Mullite (3Al2O3.2SiO2) Quartz (SiO2) andMagnetiteSpinel(Includesmagnetite(Fe3O4), hematite(Fe2O3),ferrite,and-Fe2O3(El-Mogaziet al.,1988;Liemetal.,1983).Therangeof mineralogicalcompositionandtheglasscontent found are summarized in Table 2. Table 2 Mineralogical composition of fly ashes MineralRange, weight percent Quartz2-10 Mullite5-15 Hematite1-3 Magnetite0.5-3 Glass content50-90 Categorytwoandthreearecrystallinephases. Theycollectivelyaccountfor25to30%of composition(Liemetal.,1983).Amorphousglass materialpredominatesinmostoftheflyashsamples (Tazakietal.,1989),anditgivesflyashits pozzolanic properties. The finest fraction is the richest inglass.Theproportionsofcrystallineminerals- quartz,mullite,magnesiteandhematiteinhigh calcium (CaO > 15%) fly ashes are much smaller than inlow(CaO95 % efficiency can be achieved. High operational cost. (Source: Bhatnagar and Sillanpa, 2011) Table 3: A comparative view of nitrate removal efficiencies from ground water MethodsNitrate Removal Efficiency (%) Chemical methods>60-70 Biological methods>99 AdsorptionVaries with the different adsorbents Ion-exchange~90 Reverse osmosis>95 (Source: Bhatnagar and Sillanpa, 2011) RECENT ADVANCES IN CIVIL ENGINEERING-201147 Table 4: Different autotrophic denitrification systems aVNR: Volumetric nitrate removal ratio (Source: Rocca et al. 2007) Table 5: Different solid carbon supported denitrification systems (Rocca et al., 2007) Fig 1: Potential permeable reactive barrier application of the HAD (Rocca et al. 2007) Fig.2: Origin of different adsorbents of nitrate removal from water (Source: Bhatnagar and Sillanpa, 2011) ISBN 978-81-921121-0-7 RECENT ADVANCES IN CIVIL ENGINEERING-2011 48 SWELLING BEHAVIOUR OF EXPANSIVE SOIL MIXED WITH LIME AND FLY ASH AS ADDITIVES Dhirendra Kumar Former Post Graduate student, Deptt. of Civil Engg, Institute of Technology, Banaras Hindu University, Varanasi. Suresh Kumar Assistant Professor, Deptt. of Civil Engg, Institute of Technology, Banaras Hindu University, Varanasi Bala Ramudu Paramkusam Assistant Professor, Deptt. of Civil Engg, Institute of Technology, Banaras Hindu University, Varanasi ABSTRACT:Itiswellknownthatexpansivesoilsundergolargeamountsofheavingandshrinkingduetoseasonal moisture changes. The main aim of this research is to determine the optimum lime content (OLC), optimum fly ash content (OFAC)toreducetheswellingpressureoftheexpansivesoil.Forthisinthepresentstudytheevaluationofthe swelling behavior of expansive soil (black cotton soil) with the addition of lime (1%, 2%, 3%, 4%, 5%) and fly ash (5%, 10%, 15%, 20%, 25%) at different percentages at maximum dry density (MDD) and optimum moisture content (OMC) state as well as at 2% OMC. The swelling pressure of mixed soil at each percentage of lime, and fly ash, and finally the optimum content of lime, fly ash, determine on the basis of minimum swelling pressure of mixed soil. It was found that both lime and fly ash reduced the swelling pressure,however, the addition of limereducedthe swelling pressureto a greater degree than thefly ash. Even though it takes much less percentage of lime than percentage of fly ash to reduce the swelling pressure of a highly expansive soil, it may be less expensive to utilize fly ash, which is a waste product of electric power production plants. INTRODUCTION Expansivesoilsalsocalledswellingsoilsundergo harmfulvolumechangescorrespondingtoalterin moisturecontent.Thealternateswellingand shrinkageofexpansivesoilsinalternatewetanddry seasonscauseseverecrackinginlightlyloaded structuresfoundedonthemsuchasfoundations, pavements,canalbedsandlinings.Accordingto Jones and Holtz 1973, the damage caused due to these soils is more than any other natural hazards, including earthquakes and floods. Theexpansivesoils,whicharespreadoverextensive areasofIndiainstates,likeRajasthan,Madhya Pradesh,Gujarat,AndhraPradesh,Karnatakaand Tamilnaduposedseriousproblemsforbuildingsand roads. The lightweight structures are severely affected duetohighswellingpressureexertedbythesesoils. Such type of large scale distress, due to expansive and shrinkingnatureofexpansivesoil,canbeprevented byeither obstructing thesoilmovementand reducing theswellingpressureofsoilormakingthestructure sufficiently resistant to damage from soil movement. There aresomany additives such as lime, cement, fly ash,ricehusk,gypsum;geosyntheticsetc.are availabletoimprovethephysicochemicalproperties ofclaysoilsinordertopermanentlystabilizethem. Butflyashandlimeareeasilyavailableandmore economicalsoit isusedinlargeextentasadditiveto improvethestrengthofexpansivesoil.Someofthe researchersworkedonthestabilizationofthe expansivesoilanddiscussedinfollowing.Katti, (1979)workedontheswellingsoilcausesduetoits expansivenatureduetowater,cracksareformedin buildings, lining etc. He gave the various suggestions to overcome the problem due to expansive soils using cohesive non-swelling layer (CNS) layer. Choudhary (1994)studiedtheinfluenceofflyashonthe characteristicsofexpansivesoilandheconcludethat additionofflyashisobservedtoincreasethe maximumdryunitweightanddecreasetheoptimum moisturecontentuptocertainflyashcontentcalled optimum fly ash content and the trend is reversed for flyashcontentexceedingtheoptimumflyash content. Bell (1996) presented a study using the linear shrinkagetestwithmontmorilloniteanddifferent percentagesofadditivelimeandshowedthatthe shrinkage decreased with additional lime addition, but that it was not alinear decrease.Erdal Cokca (2001) studiedtheeffectoffly-ashonexpansivesoiland experimentalfindingsconfirmedthattheplasticity index,activityandswellingpotentialofthesamples decreasedwithincreasingpercentstabilizerand curingtimeandtheoptimumcontentoffly-ashin decreasingtheswellpotentialwasfoundtobe20%. Pandian et.al. (2002) studied the effect of two types of fly ashes Raichur fly ash (class F) and Neyveli fly ash (classC)ontheCBRcharacteristicsoftheblack cottonsoil.PhanikumarandSharma(2004)asimilar studywascarriedoutandtheeffectofflyashon engineeringpropertiesofexpansivesoilthroughan experimentalprogramme.Al-Rawasetal.(2005) studytheeffectoflime,cement,Combinationsof limeandcement,Sarooj (articialpozzolan)and heat treatmentontheswellingpotentialofAl-Khod expansive soil. In the present research the efforts have beenputontostudytheeffectoflimeandflyash additiononswellingbehaviorofanexpansivesoil withanobjectivetounderstandthecomparative efficiency of these two additives. MATERIALS AND METHOD Anexpansivesoil(S)obtainedfromNTPCMouda, Nagpurwasusedandflyash(FA)obtainedfrom

RECENT ADVANCES IN CIVIL ENGINEERING-2011 49 Anparathermalpowerplant.Thephysicalproperties oftheexpansivesoilandflyashareshowninthe table 1 & 2. Thegrain size distribution curvesof both expansivesoilandflyashshowninfig.1&2.The lime used in this investigation was purchased from the localmarket.Thelime(L)wasquicklime(CaO) whichwasallowedtoslakewithadditionofwater producedhydratedlime(Ca(OH)2).Expansivesoil has been mixed with different proportionof lime, and flyashatOMC(23%),atdryconditioni.e.2%less than OMC (21%), at wet conditioni.e.2%more than OMC(25%)toevaluateitsswellingpressure.The preparation of mix and designation shown in table 3. Table 1: Physical properties of expansive soil PropertiesValues Liquid limit (%)68 Plastic limit (%)20 Shrinkage limit (%)7.05 Gravel (%)0 Sand (%)6 Silt (%)35 Clay (%)59 Maximum dry density (g/cc)1.52Optimum Moisture content (%)23 Specific gravity2.70 UCS (kg/cm2)4.0Shear stress or cohesion (kPa)60.20Angle of internal friction (o)21.5 Coefficient of permeability (cm/sec)1.5x10-8

Differential Free Swell (%)64 Swelling Pressure (kPa)48.2 soil classification (IS)CH Table 2: Physical properties of fly ash PropertiesValue Specific gravity2.14 Sand particle (%)22Silt particle (%)78Clay particle (%)0.0 Maximum dry density (g/cc)1.104 Optimum Moisture content (%)28% For thepreparationof samplefirst expansive soil and wateraddedatOMCatdifferentpercentageof additives(limeandflyash)andmixthemintray properly and compact in a swelling pressuremould as perIS:2720:2002PartXLI.Laterpreparedsample withassemblywerekeptinwaterdrumandtested usingswellingpressureapparatus(constantvolume method) and recorded the initial and final proving ring readingsaftertheprovingringreadingbecome constant.Inthecaseofexpansivesoiltheswelling pressurewasevaluatedbasedonreadingconstant after10days.Forothertestsincaseoflimemixed soilandflyashmixedsoilsthereadingbecome constantafter4daysandcorrespondingswelling pressure values reported. Table 3: Mix designation and description Mixdesignation Description M0LExpansive Soil at OMC + 0 % lime M1LExpansive Soil at OMC + 1 % lime M2LExpansive Soil at OMC + 2 % lime M3LExpansive Soil at OMC + 3 % lime M4LExpansive Soil at OMC + 4 % lime M5LExpansive Soil at OMC + 5 % lime M0FAExpansive Soil at OMC + 0 % fly ash M5FAExpansive Soil at OMC + 5 % fly ash M10FAExpansive Soil at OMC + 10 % fly ash M15FAExpansive Soil at OMC + 15 % fly ash M20FAExpansive Soil at OMC + 20 % fly ash M25FAExpansive Soil at OMC + 25 % fly ash D0LExpansive Soil at -2% OMC + 0 % lime D4LExpansive Soil at -2% OMC + 4 % lime D0FAExpansive Soil at -2% OMC + 0% fly ash D20FAExpansive Soil at -2% OMC + 20 % fly ash W0LExpansive Soil at +2% OMC + 0 % lime W4LExpansive Soil at +2% OMC + 4 % lime W0FAExpansive Soil at +2% OMC + 0 % fly ash W20FAExpansive Soil at +2% OMC + 20 % fly ash RESULTS AND DISCUSSION AtOMC,theswellingpressureofblackcottonsoil withlimeat0%,1%,2%,3%,4%,and5%bydry weightofsoilare48.3,38.35,25.3,10.35,6.9and 19.55kN/m2respectively,whichareshowninthe table4.Theswellingpressureofblackcottonsoil with fly ash at 0%, 5%, 10%, 15%, 20%, and 25%by dryweightofsoilare48.3,44.27,38.35,33.35,27.6 and 52.9 KN/m2 respectively, which are shown in the table 4. Atdrycondition,theswellingpressureofsoilat without additive, 4% lime, and 20% fly ash are 70.15, 11.5,and40.25kN/m2respectivelywhichareshown intable4.Atwetcondition,theswellingpressureof soil at without additive, 4% lime, and 20% fly ash are 20.7,5.75,36.8kN/m2respectivelywhichareshown intable4.Fig3-5showthevariationsinswelling pressurevaluesofexpansivesoil,withoutadditives and with additives as per the mix designations. Fromtheaboveresultitisfoundthattheswelling pressure of soilgenerally decreases when the additive content increases. It is also observed that lime is more effectivethanflyash.Thedecreaseintheswelling pressuremaybeexplainedduetothepozzolanicand cationexchangereactionoccurredbetweenthesoil andadditive.The reactions in lime and soil may createthenewmineralsasthecementatious component CSH(calciumsilicatehydrate),andCAH (calciumaluminatehydrate).Thesenewproducts increasethestrength,reducetheswellpotentialand change the soil classifications. CONCLUSIONSIthasbeenfoundthatfromtheexperimental programmetheswellingpressureisdecreasingupto an optimum content of additives further increase in

RECENT ADVANCES IN CIVIL ENGINEERING-2011 50 1E-3 0.01 0.1 1 10405060708090100Clay=60.0 %Silt=34.0 %Sand=6.0 %Percentage Finner in ( % ) -----Grain Size in( mm) -----Fig. 1 Grain size distribution curve of soil Fig. 2 Grain size distribution curve of fly ash Table 4: Swelling pressure values of soil at OMC (23%) and OMC with lime and Fly ash Mix Swelling Pressure ( kN/m2) Mix Swelling Pressure ( kN/m2) M0L48.3M20FA27.6 M1L38.35M25FA52.9 M2L25.3D0L70.15 M3L10.35D4L11.5 M4L6.9D0FA70.15 M5L19.55D20FA40.25 M0FA48.3W0L20.7 M5FA44.27W4L5.75 M10FA38.35W0FA20.7 M15FA33.35W20FA36.8 0 2 4 6 8 1001020304050607080AT WET CONDITIONAT OMCAT DRY CONDITIONSwelling Pressure in KN/m2 --Time in days ---Fig. 3 Comparison of swelling pressure of expansive soil with time at dry side of OMC, at OMC, and at wet side of OMC 0 1 2 3 4 50102030405060SP=19.55SP=6.9SP=10.35SP=25.3SP=38.35SP=48.3Swelling Pressure (KN/m2)Percentage of limeFig. 4 Variation of swelling pressure of soil with lime at OMC 0 5 10 15 20 25010203040506070SP=52.9SP=27.6SP=33.35SP=38.35SP=44.27SP=48.3Swelling Pressure (KN/m2)Percentage of fly ash Fig 5. Variation of swelling pressure of soil with fly ash at OMC

RECENT ADVANCES IN CIVIL ENGINEERING-2011 51 additives it leads to increase in swelling pressure. The following are the conclusions 1.Onincreasingthelimecontenttheswelling pressureofsoildecreasessteadilytoalowest valueat4%andthenitincreasesslightly. ThereforetheoptimumLimecontent(OLC)is equal to 4%. 2.Onincreasingtheflyashcontenttheswelling pressureofsoildecreasessteadilytoalowest valueat20%andthenitincreasesrapidly. Therefore the optimum fly ash content (OFAC) is equal to 20%. 3.Theswellingpressureofthesoilincaseofdry conditionismorethanthewetconditioni.e.the swellingpressureofsoilisdecreaseswith increase in the moisture content. 4.Atoptimumlimecontent,limeretardsthe swellingpressureofsoilindryaswellasinwet condition. 5.Atoptimumflyashcontent,flyashhaslessrole to retards the swelling pressureof soil in dry and wet condition. REFERENCES Al-Rawas,A.A.,Hago,A.W.,andAl-Sarmi,H. (2005).Effectoflime,cementandsarooj (artificialpozzolona)ontheswellingpotentialof anexpansivesoilfromOman.Buildingand Environment, Elsevier, Vol.40, 681-687. Bell,F.G.(1996).Limestabilizationofclayminerals andsoils.EngineeringGeology.Vol.42,223-237.Choudhary,A.K.(1994)InfluenceofFlyAshonthe CharacteristicsifExpansiveSoil.Indian GeotechnicalConfrence-1994onDevelopments inGeotechnicalEngineering,Warangal,India, 215-218. Erdal Cokca (2001).UseofclassCfly ashes for the stabilizationofanexpansivesoil.ASCE,Journal ofGeotechnicalandGeoenvironmental Engineering. Vol. 127, 568-573. Jones, D.E. and Holtz, W.G. (1973). Expansive Soils - thehiddendisaster.CivilEngineering.Vol.43 No. 8. 49-51. Katti, R.K. (1979). Search for solutions to problems in blackcottonsoils.IndianGeotechnicalJournal, Vol. 9, No. 1. IS:2720-1977(Reaffirmed2002)PartXLI. MeasurementofSwellingPressureofSoil, Bureau of Indian Standards (BIS), New Delhi. Pandian,N.S.Krishna,K.C.andLeelavathammaB. (2002). EffectofFlyAsh ontheCBR Behaviour ofSoils,IndianGeotechnicalConference, Allahabad,Vol.1, 183-186. Phanikumar B.R., & Radhey S.Sharma(2004). Effect offlyashonengineeringpropertiesofexpansive Soil.JournalofGeotechnicaland GeoenvironmentalEngineering,Vol.130,No.7, 764-767. ISBN 978-81-921121-0-7 RECENT ADVANCES IN CIVIL ENGINEERING-2011 52 USE OF MULTIPLICATIVE DECOMPOSITION METHOD FOR BUS TRAVEL TIME PREDICTION UNDER HETEROGENEOUS TRAFFIC CONDITIONS S.Vasantha Kumar Ph.D Research Scholar, Dept. of Civil Engineering, IIT Madras, Chennai-36. Email: vasanth_research@yahoo.co.in Lelitha Vanajakshi Assistant Professor, Dept. of Civil Engineering, IIT Madras, Chennai-36. Email: lelitha@iitm.ac.in. ABSTRACT: The provision of expected arrival time of the next bus which requires the prediction of bus travel timeisoneofthekeyareasofadvancedpublictransportationwhichaimsforacongestionfreetrafficonthe urban roads by increasing the public transport usage. One of the popular tools used for such prediction is using time series methods.However, mostof the reported studies explored the use of advanced time series tools such asBox-JenkinsAutoregressive(AR),Movingaverage(MA)orcombinationofARandMAmodels(ARMA, ARIMA etc.). But there are classical methods in time series analysis like multiplicative decomposition, additive decomposition etc. developed long before the Box-Jenkins models, which are powerful and even now popular in manyareassuchasfinancialforecasts.However,therearenotmanyreportedstudieswhichexploredthe usefulness of the decompositionmethodsforthe problemof bustravel timeprediction.Thepresent studytries toexploretheusefulnessofmultiplicativedecompositionmethodforbustraveltimepredictionunder heterogeneous traffic conditions. The developed model is tested for its accuracy using 41 actual bus trips data in a particular transit route representing a typical heterogeneous traffic environment in India. The results are found to be encouraging and the developed model is transferable to a similar environment.INTRODUCTION Theproblemoftrafficcongestionandwaystotackle itisaquestionofseriousconcernwhichneedstobe answered in most of the metropolitan cities around the world and similar situation exists inmost urbanareas ofIndia.Theuseofpersonalvehicleswithoutany restriction during procurement stageorusage stage is themajorreasonfortrafficcongestionespeciallyin India. The total private vehicle population in Chennai, India isincreasedfrom 10lakhsin 1999 to almost 32 lakhs in 2011 asshown inFig.1. (THEHINDU dated March 2, 2010 & Times of India dated July 11, 2011). Thisaccountforabout300percentriseinprivate vehiclepopulationinthelast12yearsandeveryday 1700newvehiclesarebeingregisteredinChennai (Times of Indiadated July 11, 2011). Theincreaseof privatevehiclesontheurbanroadsnotonlycauses trafficcongestionbutleadstomoreaccidents.For example,around700peopledieinroadaccidents every year in Chennai and another 5000 gets involved inmishapsacrossthecity(DeccanChronicledated December25,2009).AsshowninFig.1,duringthe sameperiod,thepublictransitbuseswerenot increasedmuchandthenumberswerestillreduced from4235in2009to3421in2011.The transportationplannerssuggestsaminimumof5000-6000 buses in order to meet the demand of 5.2 million passengersusingpublictransitserviceseverydayin Chennai(TimesofIndiadatedJuly11,2011).In Bangalore, 6100buses arebeing used everyday fora populationofatleast7lakhslessthanChennai. Though it is important to increase the fleet size which helpstoreduceovercrowdingduringpeakhours,this isnottheonlysolutiontomakepublictransporta moreattractiveoption.Morereliableserviceswith increasedcomfortandconveniencewilldefinitely attract more users topublic transport buses. Provision of expected arrival time informationofbuses through displayboardsatbusstops/web,providingrealtime information on passenger loading, etc. provided either pre-triporen-routecanleadtomorereliabilitythus makingpublictransitahassle-freemodeforurban commuters. RECENT ADVANCES IN CIVIL ENGINEERING-2011 53 Fig.1. Growth of privatevehiclesand public transport buses in Chennai during 1999 and 2011 Theexperienceindevelopedcountriesshowsthat, withtheintroductionofservicesofthesekinds,the increaseinpublictransportridershipwillbeevident. Forexample,aftertheintroductionofWashington MetroandTransportforLondon,thepublic transportridershipwentupby18percent(THE HINDUdatedOctober21,2010).EveninChennai, afterthe introductionof Air-Conditionedbuses,more than24percenthadshiftedfromcarsandtwo-wheelers(THEHINDUdatedMay10,2011).Ifone wantstodevelopabusarrivaltimepredictionbased ononlythetimechart/schedulelistprovidedbythe MetropolitanTransportCorporation(MTC)of Chennai,apredictionerrorof30-40minutesis observedwhichisunacceptablefromauser perspective(THEHINDUdatedJuly26,2011).The Googletransit,awebsitewhichaimstopromote publictransporthasbeenrecentlylaunchedfor ChennaiandHyderabad.Itprovidesthelistofbus servicesbetweentheuser-definedoriginand destinationandapproximatetraveltimebypublic transitinthatroute.ThemajordrawbackofGoogle transitisthat,thetraveltimesarenotbasedonreal timedataandsothereliabilityofthetraveltime informationisaquestion.Inordertomakethetravel time/arrivaltimeinformationmorereliable,the predictionmodelusedbehindshouldbeaccurateand shouldtaketherealtimedataasinput.Thepresent study tries tofocusonthese issuesandtry to develop prediction model using one of the classical time series methodsnamely,multiplicativedecomposition techniqueforbustraveltimepredictionusingreal timeGlobalPositioningSystem(GPS)datafrom publictransitbusesoperatinginaparticulartransit routeof Chennai. Theobjective is tofind an accurate predictionmethodologywhichcanovercomethe disadvantagesofthepopularlyusedprediction methodssuchasBox-JenkinsARIMAmodel, machinelearningtechniquesetc.Forexample,Box-JenkinsARIMAmodelhasmanydisadvantagessuch asstationarityandspecializedsoftwarerequirement, transferabilityissues,difficultyinunderstandingetc. Machinelearningtechniquesaredatadrivenand requirehugedatabaseforbetterperformance,which maynotbeavailableinmanycasessuchasthe presentstudy.Thepresentstudyovercomesthese difficultiesbyusingthemultiplicativedecomposition method,whichiseasytounderstand,simpleto executeusingjustspreadsheetprogram,andisnot site-specific. Moreover, the input time series need not bestationary.Hence,thepresentstudytriesto concentrateonwhethertheadvantagesof multiplicativedecompositiontechniquecanbe utilizedforbustraveltimepredictionunder heterogeneous traffic conditions. Thefollowingsectiongivesabriefreviewof importantstudiesintheareaofbusarrivaltime prediction.Thedatacollectionisdetailednext followedby the detailedmethodologyand results and discussion. LITERATURE REVIEW A brief reviewof thestudiesin the areaofbus travel timepredictionusingdatadriventechniquesand model based approaches are given below. Thedatadriventechniquesincludesregression,time seriesandmachinelearningmethodssuchasneural networkandthemodelbasedapproachmainlyusing Kalmanfiltering technique. Bo et al. usedonemonth weekdaysbustripdatafordevelopingalinear regressionmodelforbusarrivaltimepredictionin DalianCity,China.Jeongdevelopedfiveregression modelsusingdistanceandscheduleadherenceas independentvariables.TheGPSdatacollectedover sixmonthsfromHoustonmetrobusesofTexaswere usedastheinputdata.Patnaiketal.usedsixmonths weekdaydatafromAutomaticPassengerCounting [APC] systems, divided into eight categories based on differenttimeperiodsofthedayasinputand developedtheregressionmodel.Thetimeperiodis usedasadummyvariableintheregressionmodel. Ramakrishna et al. usedonly 25 trips as the inputfor developingtheregressionandartificialneural network(ANN)modelunderheterogeneoustraffic conditions. Bhandari used sevenmonths of AVL data of transit buses in New Jersey for developing an Auto Regressive(AR)timeseriesmodelforbusarrival timeprediction.SimilartoPatnaiketal.,theinput dataofbustripswereclassifiedbasedonseven categoriesoftimeperiodsusingstartingtimeofeach trip.Chienetal.usedthesimulateddatafromthe morningpeaktodevelopanANNbasedbusarrival timepredictionmodel.Vanajakshietal.usedonly precedingtwobusesdataforpredictingthenext vehicletraveltimeinamodelbasedapproachusing Kalmanfilteringtechnique(KFT)under heterogeneoustrafficconditions.Daileyetal.used KFT with input of bus trips data collected on different days at the same time of the day. Fromtheabovestudies,itcanbeseenthat,mostof thepredictionmodelsweredevelopedfor homogeneoustrafficconditionsandonlyfewstudies wereconcentratedonheterogeneoustraffic conditions.Also,theuseofclassicaltimeseries methodslikemultiplicativedecompositionwasnot attemptedyetforbusarrivaltimeprediction.This clear gap leads to themotivation for the present study whichaimstodevelopapredictionmodelusing multiplicativedecompositiontechniquewhichcanbe usedforbustraveltimepredictionunder heterogeneous traffic conditions. DATA COLLECTION AND EXTRACTION Thestudystretchselectedforthepresentstudywas routenumber5C,whichconnectstheParrysbus RECENT ADVANCES IN CIVIL ENGINEERING-2011 54 depotinthenorthernpartofChennai,andthe TaramanibusdepotinthesouthernpartofChennai city.Theaveragetimeheadwaybetweenthebuses was15-30minutes.Thetotalroutelengthis15km andtheapproximatetraveltimetocoverthetotal stretch is 60 minutes during peak hour and 40 minutes duringoff-peakhour.Thereare21busstopsand14 signalizedintersectionsinthisroute.Theselected roadstretchisatypicalrepresentationofanurban roadinIndia.Thetotalroutecomprisesroadlinksof differentcategorieslikemajorarterials,andcollector streets with varying volume levels. Sevenpublictransitbusesinroutenumber5Care fitted with GPS units and the data report