200712103
Transcript of 200712103
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REMOTE SENSING AND GIS BASED PAVEMENT PERFORMANCE PREDICTION MODEL USING
ARTIFICIAL NEURAL NETWORK
UNDER THE GUIDENCE, Dr.C.UDHAYA KUMAR ASST. PROFESSOR IRS, ANNA
UNIVERSITY
BY, DEVI
PRIYADARISINI.K ROLL NO: 200712103
M.E. GEO INFORMATICS
IRS, ANNA UNIVERSITY.
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INTRODUCTIONPavement surface are a major component of Infrastructure.
The existing route system has become structurally inadequate.
GIS can add tremendous functionality to a pavement management condition program.
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ObjectivesTo map the present condition of the
pavement.To develop pavement performance
predicition model. To incorporate the model with GIS and
create graphical outputs.To validate the model.To predict and map future condition.
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Scope of WorkTo monitor and maintain the major
infrastructure asset ,highway.The knowledge of future pavement
performance is essential to PMS.GIS environment to support the
pavement management decision making using several application.
Which would help in predict the pavement performance accurately.
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Study AreaLocation : ChennaiStretches : Sadar Patel Road – 3000m
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Data to be UsedNon – Spatial Data
Spatial DataStructure of the
pavement.Surface Parameter
CracksPotholesRut DepthSkid Residence
Soil CharacteristicsSlope
Quick bird – 0.6m
SOI Toposheets : 66C4, C8
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Calculation of PSIThe PSI value is calculated by using the
following equation : PSI = 20.715 – 6.676 *log (R) – 0.0283 * D
Where, R – Unevenness Index of the pavement
surface D – Total Surface distress.
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Pavement Condition Scale
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Present PSI Scale Along S.P Road
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Present Condition Map
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Development of ANN Structure
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Comparison of ANN architecture of the model
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Comparison of ANN and PSI for PPP for testing set
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Comparison of ANN and PSI for PPP for testing set
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Predicted PSI value along SP Road
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Future condition map
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Pavement Condition Distribution Graph
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Future Condition Distribution
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ConclusionDevelopment and use of PMS using Pavement
attribute database in GIS environment.Different types of operation can be performed.The developed ANN model can be used for
several Pavement Management decision.The ANN model developed gives out better
results than the PPP to the AASHTO panel data.Effective decision making in Pavement
Management System.Analysis and budget allocation for
rehabilitation purpose.
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Future RecommendationApplying the PSI and ANN model concept
criterion to setup maintenance priorities, maintenance cost and pavement management programs.
Adapting GPS and GIS based vision systems for the purpose of distresses data collection and measurements.
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ReferencesBarry White and Alex Rocie, “GIS and Pavement Management”, Transportation Engineering
ASCE. San Diego, “Development of GIS based Illinois Highway pavement management”, ESRI user
conference. Deva Pratap, Kiran Kumar, “Highway Information System and Management using GIS”, Gis
development. Michael T. Me Nernery and Thomas Row, “GIS need assessment for TxDOT pavement
information system”, US Dept. of Transport.Andres L.Bako, Zoltan Hervath, “Decision Supporting Model for Highway Maintaince”,
Journal of Infrastructure System, ASCE. Gerardo W.Flintish , Randy Dymond and Jhon Collua, “Pavement Management Application
Using GIS”, NCHRP 2005, pp 1-25.Serdal Terzai, “Modeling the pavement serviceability ratio of flexible highway pavement by
ANN”, ELSEVIER, Construction and Building Materials 17 (2007) 577–582
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Contd…Mohammed Taleb Obaidat , Sharaf A. Al-kheder,
“Integration of geographic information systems and computer vision systems for pavement distress classification”, ELSEVIER, Construction and Building Materials 20 (2006) 657–672
J.A.Prozji, S.M.Madanat, “Developing of Pavement Performance models combing Experimental field data”, Journal of Infrastructure System, ASCE.
Samer Madana, Jorge A.Prozzi and Michael Han, “Effect of performance model accuracy on optimal pavement design”, Journal of Infrastructure System, ASCE 2006 August.
Abul Hamid Modh Isa, Law Tick Hwa, Dadarcy Mohamed
Ma’soen, “Pavement performance models for federal roads", Journal of Infrastructure System, ASCE, 2007, April
Bosurgi G., “Artificial Neural Networks for Predicting Road Pavement Conditions”, 4th International SIIV Congress – PALERMO (ITALY), 12-14 September 2007.
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