Research Article Real-Time Dry Beach Length Monitoring for Tailings Dams … · 2019. 7. 31. ·...

Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2013 Article ID 935371 7 pageshttpdxdoiorg1011552013935371

Research ArticleReal-Time Dry Beach Length Monitoring for Tailings DamsBased on Visual Measurement

Jun Hu1 Shan Hu1 Fei Kang2 and Jianhua Zhang3

1 School of Mining Engineering University of Science and Technology Liaoning Anshan Liaoning 114051 China2 Faculty of Infrastructure Engineering Dalian University of Technology Dalian Liaoning 116024 China3 College of Computer Science and Technology Zhejiang University of Technology Hangzhou Zhejiang 310023 China

Correspondence should be addressed to Jun Hu kdhj1977126com

Received 18 August 2013 Accepted 4 October 2013

Academic Editor Xiao-Wei Ye

Copyright copy 2013 Jun Hu et al This is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

The length of dry beach is an important factor that influences the safety of tailings dams However there still is no accurate andreliablemethod that can convenientlymeasure the length of dry beach In this paper the authors focus ondeveloping a novelmethodfor dry beach length determinationTheproposedmethod can effectivelymeasure the dry beach length through an ordinary cameraand four marking rods placed on the dry beach Experimental results show that the proposed method can conveniently measurethe dry beach length with high accuracy and therefore it can be adopted as an effective method in tailings dam real-time healthmonitoring

1 Introduction

Increased awareness of the economic and social effects ofaging deterioration and extreme events on civil infrastruc-ture has been accompanied by recognition of the need foradvanced structural healthmonitoring and damage detectiontools [1] Structural health monitoring is a technology thatallows the estimation of the structural state and detection ofstructural change that affects the performance of a structure[2 3] Recently many advanced measurement methods suchas global positioning systems (GPS) [4 5] wireless sensornetworks [6 7] and fiber Bragg grating sensing [8] have beenadopted for health monitoring of engineering structuresMeanwhile various signal processing methods such as parti-cle filtering algorithm [9] wavelet thresholding [10] particleswarm optimization [11 12] artificial bee colony algorithm[13] and neural networks [14] are developed to deal with thedata obtained by the sensor systems to reflect the status ofstructures

Tailings pond is formed by damming at valley intercep-tion or damming at all sides which is used to store tailingsand other industrial waste produced by metal and nonmetalmines Tailings dams are a particular type of dambuilt to storemill and waste tailings from mining activities and a tailings

dam is an important part of a tailings pond which refers tothe initial dam and later fill dam Tailings storage facilitiesare complex geotechnical structures A number of singularcharacteristics make tailings dams more vulnerable thanother types of retention structures Many tailings dam failurecases have been reported [15] The failure of a tailings dammay cause grave disasters such as floods [16] and groundwaterpollution [17] Therefore in the design and operation stagesof a tailings project the stability of the tailings dam thecontrol capacity for flood and the reliability of the drainageand safety monitoring facilities should be fully evaluated[18] Some works of the literature have focused on the staticand dynamic stability analysis of tailings structures [19ndash22]Reinforced terraced fields method for fine tailings disposalwas proposed in [23] Statistical source identificationmethodfor groundwater downward the tailings dam was studiedin [24] considering that identifying the possible sources ofpotential harmful metals in groundwater systems plays acrucial role in evaluating the potential risks to residents andlocal plant cover

Recently the safety monitoring of tailings dams has be-come a hot topic Security monitoring items of tailings damsinclude displacement saturation line reservoir level and drybeach [25] Sjodahl et al [26] proposed an internal erosion

2 Mathematical Problems in Engineering

process and anomalous seepage detectionmethod for tailingsdams based on resistivitymeasurements Securitymonitoringsystems for tailings dams based on global positioning systemare discussed in [27 28] Several online monitoring strategiesfor tailings dams also have been researched and developed[25 29ndash31] Blight et al [32] proposed a freshly depositedsurfaces measurement method for two platinum tailingsdams

The length of dry beach is an important index affectingthe safe operation of the tailings pond because it not onlyaffects the height of the tailings dam saturation line and thesafety of the tailings dam but also decides the flood controlability of the dam The shortage of the beach length willseriously affect the safety of the tailings ponds With theinjecting of the tail slurry the dry beach length changes in realtime Researching and developing the real-time automaticmeasurement method for the dry beach length monitoringbecomes an essential content of the tailings dam safetymonitoring system

Conventional instruments for dry beach length measure-ment are hard to obtain accurate results in real time Twomethods commonly used are described as follows In thefirst approach controlling the water level as low as possiblebefore the rainy season to keep enough dry beach lengthand setting red flags on the dry beach every 50 meters fromthe highest secondary dam based on the specific conditionsof the dry beach In the flood season the dry beach lengthcan be determined by cruise visual check In the secondapproach the dry beach slope is assumed to be uniform andthe length of dry beach can be calculated by the differenceof reservoir water level elevation and the beach elevationThe disadvantage of the previous two approaches is obviousThe first method cannot achieve automatic measurement Asfor the second method the measurement error can be verylarge because the dry beach slope is not evenly made Therealso exist automatic noncontact measurement technologyand equipment for dry beach length measurement suchas deploying multiple automatic altimeters in the center ofponds to measure the dry beach elevation automatically Analtimeter is consisted of grating measuring system wirelessdata transmission modules solar panels battery and soforth But as a result of constant injection of slurry andchanging of beach surface altimeters need to bemade regularlayout again otherwise they will be buried and cannot worknormally

In this paper a novel online measurement method fordry beach length is proposed based on photogrammetricmonitoring The main work is divided into two parts Thefirst part refers to the measurement preparation work andthe second part refers to dry beach length calculationby the vision measurement technology Preparation worksmainly include camera calibration and place markers atthe scene of the measurement In the vision measurementprocess firstly an image taken by the camera is isolatedas the dry beach area and the reservoir water surfacearea through image processing Then the locations of themarkers in the image are extracted and finally the drybeach length is calculated according to the parametersextracted

2 Basic Principles of PhotogrammetricMonitoring for Dry Beach Length

Pinhole imaging model is shown in Figure 1 [33] The focallength of the camera is set to 119891 and the origin of coordinatesis set as the optical center of the camera The coordinates ofthe point in119876 space are (119883 119884 119885) and its corresponding pointson the imaging plane are (119909 119910 119891) According to the principleof pinhole imaging the following equation can be obtained

119909 =119891119883

119885 119910 =

119891119884

119885 (1)

Assume that two points in 119876 space are (1198831 1198841 1198851)

(1198832 1198842 1198852) and their coordinates in the image plane are

(1199091 1199101) and (119909

2 1199102) respectively In 119876 space the distance

between two points can be calculated as

119863 = radic(1198831

minus 1198832)2

+ (1198841

minus 1198842)2

+ (1198851

minus 1198852)2

(2)

When 1198851

asymp 1198852

= 119885 from (1) and (2) the following equa-tions can be obtained

119863 =119885radic(119909

1minus 1199092)2

+ (1199101

minus 1199102)2

119891 (3)

119891 =119885radic(119909

1minus 1199092)2

+ (1199101

minus 1199102)2

119863 (4)

119885 =119863119891

radic(1199091

minus 1199092)2

+ (1199101

minus 1199102)2

(5)

When the space distance between two points distancesfrom two points to the camera and the image of the twopoints in the image plane are known the camera focal lengthcan be calculated from (4) According to (5) when the focallength of the camera the actual size of the measured objectand the size in the image plane of the measured object arealready known the distance from the camera to the objectcan be calculated

The method adopted is assisted by four marking rods indry beach area It should ensure that the four marking rodsform a rectangle and keep the camera lens in the middle ofthe rectangle formed by the marking rods Because the actualdry beach surface can be approximated as a plane you candetermine the water line by the points of four bars and thedry beach is approximately thought to be in the same planeFigure 2 shows the layout of the marker rods and the cameraThe red points represent the marking rods and the blue lineis called water line which represents the boundary of the drybeach and the water surface LP1 LP2 RP1 RP2 dry beachand thewater line are in the same plane By the points LP1 andLP2 we can define line LL and the line LL intersects with thewater line at point CL By the points RP1 and RP2 we candefine line RL and the line RL intersects with the water lineat the point CR The distance between CL and CR is equal tothe distance between RP1 and LP1

According to the invariance of X-ray imaging a point inspace is still a point on the imaging plane and a straight line

Mathematical Problems in Engineering 3

Image p

lane

Center of projection

i

j

k

f

Optical axis

Q = (X Y Z)

q = (x y f)O

Figure 1 Pinhole imaging model

The camera

LP2

LP1

RP2

RP1

CRCL

L2

LL RL

BB

Dry beach

The water line

Figure 2 Measurement schemes

in space is still a straight line on the imaging planeThereforetwo points in the space corresponds to two image pointswhich determine a straight line on the imaging plane [34]

The principle can be described as follows

Theorem 1 Supposing that P1 and P2 are two points in thespace and p1 and p2 are two points in the camera image planethe image of the space straight line between P1 P2 is necessarilythe straight line between p1 and p2 on the imaging plane

Figure 3 is a picture corresponding to the layout ofFigure 2 In this figure l1 and l2 are corresponding to LP1and LP2 in Figure 2 and r1 and r2 are corresponding toRP1 and RP2 in Figure 2 According to Theorem 1 l1 and l2determine the line corresponding to the line LL and r1 andr2 determine the line corresponding to the line RL in theschematic diagram Therefore CL and CR correspond to theimaging points cl and cr respectively Because LL and RLare parallel to each other the distance between CL and CRis equal to the distance between LP1 and RP1 This distancemust be determined and measured when setting up markingrods To calculate the distance between the camera and theboundary line the critical problems are determination of thecamera focal length and calculation of the pixel coordinatesof cl and cr Through (4) the camera focal length can becalculated and through image processing technique the pixeldistance between cl and cr can be obtainedThen the distance

Figure 3 Layout of the mark poles

Figure 4 International chess calibration plate

between the camera and the boundary can be calculated from(5)

3 Measurement Steps

31 Camera Intrinsic Parameter Calibration In image mea-surement process and machine vision applications in orderto determine the interrelation between the object surfacegeometry in three-dimensional space with the correspondingpoints in the image it is necessary to establish the cameraimaging geometric model and the geometric model is deter-mined by the camera parameters

These parameters must be obtained through experimentsand calculation and the process of solving these parametersis called camera calibration Both in image measurement andmachine vision applications camera parameter calibrationwork is a critical factor that influences the calibration resultsCalibration precision and stability of the calibration algo-rithm directly affect the results accuracy of camera workTherefore completing the camera calibration is a premiseto proceed to the next steps and improving the calibrationaccuracy is the key of scientific research work

The distortion of the camera in the process of imaging isusually unavoidable and radial distortion is the main part ofcamera lens distortion Taking optical axis as the center andits image coordinate as the reference point radial distortionis proportional to the square of the distance from the pointto the reference point Only considering the second order oflens distortion radial distortion can be described as

119906 minus 1199060

= (1199061015840 minus 1199060) (1 + 119896

1199061199032)

V minus V0

= (V1015840 minus V0) (1 + 119896V119903

2) (6)


(a) (b)

Figure 5 Example of image segmentation by 119896-means clustering algorithm (a) original image (b) image after segmentation

where (1199061015840 V1015840) is the ideal arthroscopic image coordinatewithout distortion (119906 V) is the actual image coordinate(1199060 V0) is the image coordinates of the optical axis center 119903 is

the distance between the image point to the reference pointand 119896

119906 119896V are the second-order distortion coefficients along

the directions 119906 and V Camera distortion parameters can beobtained by calibration to the camera intrinsic parameters

This paper adopts the camera calibration method pro-posed by Zhang [35]The plane calibrationmethod proposedby Zhang is a kind of method between the traditionalcalibration methods and self-calibration methods It avoidssome disadvantages of the traditional methods such as thehigh requirement for equipment and complicated opera-tion Meanwhile like the self-calibration method it has theadvantage of high accuracy The steps for camera calibrationprocedure are described as follows (1) Make a pane of achess board template with the size 20 cm times 20 cm for eachcell and stick it on a flat surface as shown in Figure 4 (2)Take several template images fromdifferent angles (3) Detectthe feature points in the international chess calibration plate(4) Calculate the inner and outer parameters of the camera(5) Calculate the distortion coefficient (6) Optimization andrefinement The camera distortion parameters are obtainedafter camera calibration and the distortion parameters canbe used to correct the images taken by the camera

32 Dry Beach and the Water Surface Areas Extraction Dueto the characteristics of dry beach area images differ fromthe characteristics of thewater surface area imagesThereforewe can take advantages of regional segmentation method toextract the dry beach area andwater surface area in the digitalimage The edge of the water surface area is the boundaryof the dry beach and the water The intention of digitalimage segmentation is to segment the areas with differentparticular meanings These areas are mutually disjoint andeach area meets the consistency of a particular area Areassegmented by an excellent segmentation algorithm shouldsatisfy the following conditions (1) The partition of theimage area should satisfy the requirement for uniformityand connectivity The uniformity refers to the pixels whichshould satisfy all the pixels in the same kind of similaritycriterion based on the texture color and other characteristicsConnectivity refers to there exist paths for connecting anytwo points in the area (2) There are notable differencesbetween adjacent segmented regions (3) The boundary of

segmentation regions should be neat and at the same timepositioning accuracy of the edges should be ensured

The image segmentation method based on 119896-means clus-tering algorithm is adopted Because the clustering algorithmis concise and efficient it is widely used in all kinds ofclustering problems Given a set of data points and thenumber of clusters 119896 119896-means algorithm can classify the datapoints into 119896 clusters based on a certain distance functionThe parameter 119896 is specified by the userThe steps of 119896-meansclustering algorithm for image segmentation are described asfollows

(1) Transform the camera image from RGB color space to119871times119860times119861 spaceThe 119871times119860times119861 space of the image can describethe regional characteristics better than RGB color space andthe values of 119860 and 119861 in the 119871 times 119860 times 119861 space are regarded asthe characteristic values for each point in the image

(2) Set the number of clusters to be 119896 = 3 and initializethe cluster centers When camera is adopted to monitorthe length of dry beach the camera image area is mainlycomposed of dry beach water and other areasTherefore thenumber of clusters is set to be 119896 = 3 when using the 119896-meansclustering algorithm for image segmentation

(3) Calculate the distance between each pixel and theclustering center Each pixel is classified in accordance withthe principle of minimum distance to the nearest clusteringcenter The clustering centers are calculated as mean valuesof cluster samples in each cluster

(4) Repeat step (3) until the clustering centers no longerchange

Figure 5 is an example of image segmentation by 119896-meansclustering algorithm In Figure 5(a) the original image oftailings areas is taken by a camera The 119896-means clusteringalgorithm for image segmentation is adopted to extractdifferent regions In Figure 5(b) the black part represents thedry beach area the gray part represents the water surfacearea and the white part represents the other area It can beseen that the water surface area can distinguish different partsclearly So the pixels of the water surface area can be adoptedto extract the boundary

33 Marking Rods Extraction Marking rods are adoptedin the visual measurement to determine two parallel linesThen the distance from the camera to the dry beach can bemeasured by using the principle of that the distances betweentwo parallel lines are equal The positions of the markingrods should be extracted from the image accurately We use


Table 1 Image templates of four marking rods

Marking rods 1 2 3 4

Sign

the method of digital image template matching to locate theposition of marking rods in the image The image template isa small image already known Templatematching is amethodused to search the goals in a big image We can find the targetand determine its location via calculating under the conditionthat the target has the same size direction and imagewith thetemplate

The principle of image template matching is describedas follows Firstly the image template 119879 (119898 times 119899 pixels) istranslated into the search graph 119878 (119882 times 119867 pixels) and thearea that is covered by the image template is called subgraph119878119894119895 The indexes 119894 and 119895 are the coordinates in the search

graph 119878 The search scope is 1 le 119894 le 119882 minus 119898 and 1 le119895 le 119867 minus 119899 By comparing the similarity between 119879 and 119878

119894119895

we can complete the template matching process Similaritybetween image template 119879 and subgraph 119878

119894119895can be measured

as

119863 (119894 119895) =119898

sum119901=1

119899

sum119902=1

10038161003816100381610038161003816119878119894119895 (119901 119902) minus 119879 (119901 119902)10038161003816100381610038161003816 (7)

The procedure for images template matching has thefollowing steps (1) Extract the required image templatesTable 1 shows the image templates of four marking rods(2) Calculate the similarity 119863(119894 119895) between images and thetemplate by (7) (3) Find the minimum similarity 119863(119894 119895) and(119894 119895) is the position of the marker rod that we want to find

The installation diagram of high-definition camera isshown in Figures 6 and 7 Figure 8 shows the positions ofmarking rods detected by template matching method

34 Dry Beach Length Calculation The water surface areaand coordinates of marking rods can be obtained by regionalsegmentation of the image and identification ofmarking rodsAs shown in Figure 9 the white color represents the watersurface area the red dots represent the marking rods andthe green lines are two parallel lines determined by markingrods The intersection points of the two parallel lines withthe water surface area are cl and cr as shown in Figure 3After calculating the distance between the two pixels by using(5) we can calculate the dry beach length which equals thedistance from the boundary to the camera

Figure 6 Installation diagram

Figure 7 High-definition camera

4 Experimental Results

Three experiments are performed to verify the proposedmethod for dry beach length measurement In the threeexperiments the distance between the camera and waterline is set at 150m 200m and 300m This is achieved bymoving the camera and marking rods The measurementresults of the experiments are shown in Table 2 It can be seenthat when the distance is small the error is also small and


Table 2 The measurement results

The actualdistance (m)

The measureddistance (m) Relative error

150 1478 146200 1946 27300 3097 32

Figure 8 Extraction of marking rods

Figure 9Water surface area and parallel lines determined bymark-ing rods

the error will increase with the rising of distance Whenthe actual distance is 300m the relative error is only 32Therefore it still satisfies the requirement of measurementaccuracy

5 Conclusion

Thedry beach length of a tailings dam is hard to be accuratelymeasured automatically In this paper we propose a methodfor measuring real-time length of dry beach length by usingan ordinary camera assisted with marking rods When usingthe method we should pay special attention to two items(1) In order to meet the requirement of the measurementmodel the fourmarking rods should be placed as a rectangleMeanwhile the optical axis of the camera should be placed atthe axis of the rectangular Then the assumption 119885

1= 1198852in

(1) can be satisfied (2) In the paper only two marking rodsare used to determine a straight line but in order to improvethe measuring reliability and robustness more marking rodscan be used to determine the straight line After extractingthe positions of marking rods from the image a curvefitting method can be applied to determine the straight lineExperimental results show that the proposed method has theadvantages of convenient accurate and reliable performanceand the cost of measurement equipments is low

Acknowledgments

This research was supported by National Natural ScienceFoundation of China under Grant nos 51274053 51109028

the State ldquoTwelve Fiverdquo Science and Technology SupportProjects under Grant no 2013BAB02B00 the Natural ScienceFoundation of Liaoning Province under Grant no L2011040the State Scholarship Fund of China to pursue study in USAas a visiting scholar under Grant no 201208210208 andScientific Research Foundation of University of Science andTechnology Liaoning

References

[1] P C Chang A Flatau and S C Liu ldquoReview paper healthmonitoring of civil infrastructurerdquo Structural Health Monitor-ing vol 2 no 3 pp 257ndash267 2003

[2] Y Q Ni XW Ye and JM Ko ldquoMonitoring-based fatigue relia-bility assessment of steel bridges analytical model and applica-tionrdquo Journal of Structural Engineering vol 136 no 12 pp 1563ndash1573 2010

[3] Y Q Ni X W Ye and J M Ko ldquoModeling of stress spectrumusing long-term monitoring data and finite mixture distribu-tionsrdquo Journal of Engineering Mechanics vol 138 no 2 pp 175ndash183 2011

[4] T H Yi H N Li andM Gu ldquoRecent research and applicationsof GPS-based monitoring technology for high-rise structuresrdquoStructural Control andHealthMonitoring vol 20 no 5 pp 649ndash670 2013

[5] T H Yi H N Li and M Gu ldquoWavelet based multi-stepfiltering method for bridge health monitoring using GPS andaccelerometerrdquo Smart Structures And Systems vol 11 no 4 pp331ndash348 2013

[6] J P Lynch and K J Loh ldquoA summary review of wireless sensorsand sensor networks for structural health monitoringrdquo Shockand Vibration Digest vol 38 no 2 pp 91ndash130 2006

[7] S N Pakzad G L Fenves S Kim and D E Culler ldquoDesignand implementation of scalable wireless sensor network forstructuralmonitoringrdquo Journal of Infrastructure Systems vol 14no 1 pp 89ndash101 2008

[8] X W Ye Y Q Ni and J H Yin ldquoSafety Monitoring of railwaytunnel construction using FBG sensing technologyrdquo Advancesin Structural Engineering vol 16 no 8 pp 1401ndash1410 2013

[9] T H Yi H N Li and M Gu ldquoCharacterization and extrac-tion of global positioning system multipath signals using animproved particle-filtering algorithmrdquo Measurement Scienceand Technology vol 22 no 7 Article ID 075101 2011

[10] T H Yi H N Li and X Y Zhao ldquoNoise smoothing forstructural vibration test signals using an improved waveletthresholding techniquerdquo Sensors vol 12 no 8 pp 11205ndash112202012

[11] F Kang J J Li and S Liu ldquoCombined data with particle swarmoptimization for structural damage detectionrdquo MathematicalProblems in Engineering vol 2013 Article ID 416941 10 pages2013

[12] F Kang J Li and Q Xu ldquoDamage detection based on improvedparticle swarm optimization using vibration datardquo Applied SoftComputing Journal vol 12 no 8 pp 2329ndash2335 2012

[13] F Kang J Li and Q Xu ldquoStructural inverse analysis byhybrid simplex artificial bee colony algorithmsrdquo Computers andStructures vol 87 no 13-14 pp 861ndash870 2009

[14] X Wang F Kang J J Li and X Wang ldquoInverse parametricanalysis of seismic permanent deformation for earth-rockfilldams using artificial neural networksrdquo Mathematical Problemsin Engineering vol 2012 Article ID 383749 19 pages 2012


[15] M Rico G Benito A R Salgueiro A Dıez-Herrero andH G Pereira ldquoReported tailings dam failures A review ofthe European incidents in the worldwide contextrdquo Journal ofHazardous Materials vol 152 no 2 pp 846ndash852 2008

[16] M Rico G Benito and A Dıez-Herrero ldquoFloods from tailingsdam failuresrdquo Journal of Hazardous Materials vol 154 no 1ndash3pp 79ndash87 2008

[17] R S Sharma and T S Al-Busaidi ldquoGroundwater pollution dueto a tailings damrdquo Engineering Geology vol 60 no 1ndash4 pp 235ndash244 2001

[18] T Wang Y Zhou Q Lv Y Zhu and C Jiang ldquoA safety assess-ment of the new Xiangyun phosphogypsum tailings pondrdquoMinerals Engineering vol 24 no 10 pp 1084ndash1090 2011

[19] G Yin G Li Z Wei L Wan G Shui and X Jing ldquoStabilityanalysis of a copper tailings dam via laboratory model tests aChinese case studyrdquoMinerals Engineering vol 24 no 2 pp 122ndash130 2011

[20] A T Ozer and L G Bromwell ldquoStability assessment of an earthdam on siltclay tailings foundation a case studyrdquo EngineeringGeology vol 151 pp 89ndash99 2012

[21] M T Zandarın L A Oldecop R Rodrıguez and F ZabalaldquoThe role of capillary water in the stability of tailing damsrdquoEngineering Geology vol 105 no 1-2 pp 108ndash118 2009

[22] X Jianbin F Jing L Wenlian et al ldquoDynamic stability of thetailings dam under high intensity seismic load coupled with theeffect of seepagerdquo Disaster Advances vol 5 no 4 pp 816ndash8212012

[23] Z Wei G Yin G Li J G Wang L Wan and L ShenldquoReinforced terraced fields method for fine tailings disposalrdquoMinerals Engineering vol 22 no 12 pp 1053ndash1059 2009

[24] M Kargar N Khorasani M Karami G Rafiee and R NasehldquoStatistical source identification of major and trace elementsin groundwater downward the tailings dam of Miduk CopperComplex Kerman Iranrdquo EnvironmentalMonitoring and Assess-ment vol 184 no 10 pp 6173ndash6185 2012

[25] G Yu C Song J Zou et al ldquoApplications of online monitoringtechnology for tailings dam on digital minerdquo Transactions ofNonferrous Metals Society of China vol 21 supplement 3 pps604ndashs609 2011

[26] P Sjodahl T Dahlin and S Johansson ldquoUsing resistivitymeasurements for dam safety evaluation at Enemossen tailingsdam in southern Swedenrdquo Environmental Geology vol 49 no2 pp 267ndash273 2005

[27] J Hu and X Liu ldquoDesign and implementation of tailings damsecurity monitoring systemrdquo in Proceedings of the 1st Inter-national Symposium on Mine Safety Science and Engineering(ISMSSE rsquo11) vol 26 pp 1914ndash1921 October 2011

[28] W Li and C Wang ldquoGPS in the tailings dam deformationmonitoringrdquo in Proceedings of the 1st International SymposiumonMine Safety Science and Engineering (ISMSSE rsquo11) vol 26 pp1648ndash1657 October 2011

[29] Q Li Y Wang and G Li ldquoTailings dam breach disaster on-line monitoring method and system realizationrdquo in Proceedingsof the 1st International Symposium on Mine Safety Science andEngineering (ISMSSE rsquo11) vol 26 pp 1674ndash1681 October 2011

[30] E Sun X Zhang and Z Li ldquoThe internet of things (IOT) andcloud computing (CC) based tailings dammonitoring and pre-alarm system inminesrdquo Safety Science vol 50 no 4 pp 811ndash8152012

[31] ZHOuyan ldquoResearch development and application of surfacedisplacement automatic monitoring system of tailings damrdquoAdvanced Materials Research vol 422 pp 304ndash309 2012

[32] G Blight A Copeland P Jardine et al ldquoMeasurements onfreshly-deposited surfaces of two platinum tailings damsrdquo Jour-nal of the Southern African Institute of Mining and Metallurgyvol 112 no 11 pp 911ndash917 2012

[33] G J Zhang Visual Measurement Science press Beijing China2008

[34] R Hartley and A Zisserman Multiple View Geometry inComputer Vision Cambridge University Press Cambridge UK2004

[35] Z Zhang ldquoFlexible camera calibration by viewing a plane fromunknown orientationsrdquo in Proceedings of the Seventh IEEEInternational Conference on Computer Vision (ICCV rsquo99) vol1 pp 666ndash673 September 1999

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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of


process and anomalous seepage detectionmethod for tailingsdams based on resistivitymeasurements Securitymonitoringsystems for tailings dams based on global positioning systemare discussed in [27 28] Several online monitoring strategiesfor tailings dams also have been researched and developed[25 29ndash31] Blight et al [32] proposed a freshly depositedsurfaces measurement method for two platinum tailingsdams

The length of dry beach is an important index affectingthe safe operation of the tailings pond because it not onlyaffects the height of the tailings dam saturation line and thesafety of the tailings dam but also decides the flood controlability of the dam The shortage of the beach length willseriously affect the safety of the tailings ponds With theinjecting of the tail slurry the dry beach length changes in realtime Researching and developing the real-time automaticmeasurement method for the dry beach length monitoringbecomes an essential content of the tailings dam safetymonitoring system

Conventional instruments for dry beach length measure-ment are hard to obtain accurate results in real time Twomethods commonly used are described as follows In thefirst approach controlling the water level as low as possiblebefore the rainy season to keep enough dry beach lengthand setting red flags on the dry beach every 50 meters fromthe highest secondary dam based on the specific conditionsof the dry beach In the flood season the dry beach lengthcan be determined by cruise visual check In the secondapproach the dry beach slope is assumed to be uniform andthe length of dry beach can be calculated by the differenceof reservoir water level elevation and the beach elevationThe disadvantage of the previous two approaches is obviousThe first method cannot achieve automatic measurement Asfor the second method the measurement error can be verylarge because the dry beach slope is not evenly made Therealso exist automatic noncontact measurement technologyand equipment for dry beach length measurement suchas deploying multiple automatic altimeters in the center ofponds to measure the dry beach elevation automatically Analtimeter is consisted of grating measuring system wirelessdata transmission modules solar panels battery and soforth But as a result of constant injection of slurry andchanging of beach surface altimeters need to bemade regularlayout again otherwise they will be buried and cannot worknormally

In this paper a novel online measurement method fordry beach length is proposed based on photogrammetricmonitoring The main work is divided into two parts Thefirst part refers to the measurement preparation work andthe second part refers to dry beach length calculationby the vision measurement technology Preparation worksmainly include camera calibration and place markers atthe scene of the measurement In the vision measurementprocess firstly an image taken by the camera is isolatedas the dry beach area and the reservoir water surfacearea through image processing Then the locations of themarkers in the image are extracted and finally the drybeach length is calculated according to the parametersextracted

2 Basic Principles of PhotogrammetricMonitoring for Dry Beach Length

Pinhole imaging model is shown in Figure 1 [33] The focallength of the camera is set to 119891 and the origin of coordinatesis set as the optical center of the camera The coordinates ofthe point in119876 space are (119883 119884 119885) and its corresponding pointson the imaging plane are (119909 119910 119891) According to the principleof pinhole imaging the following equation can be obtained

119909 =119891119883

119885 119910 =

119891119884

119885 (1)

Assume that two points in 119876 space are (1198831 1198841 1198851)

(1198832 1198842 1198852) and their coordinates in the image plane are

(1199091 1199101) and (119909

2 1199102) respectively In 119876 space the distance

between two points can be calculated as

119863 = radic(1198831

minus 1198832)2

+ (1198841

minus 1198842)2

+ (1198851

minus 1198852)2

(2)

When 1198851

asymp 1198852

= 119885 from (1) and (2) the following equa-tions can be obtained

119863 =119885radic(119909

1minus 1199092)2

+ (1199101

minus 1199102)2

119891 (3)

119891 =119885radic(119909

1minus 1199092)2

+ (1199101

minus 1199102)2

119863 (4)

119885 =119863119891

radic(1199091

minus 1199092)2

+ (1199101

minus 1199102)2

(5)

When the space distance between two points distancesfrom two points to the camera and the image of the twopoints in the image plane are known the camera focal lengthcan be calculated from (4) According to (5) when the focallength of the camera the actual size of the measured objectand the size in the image plane of the measured object arealready known the distance from the camera to the objectcan be calculated

The method adopted is assisted by four marking rods indry beach area It should ensure that the four marking rodsform a rectangle and keep the camera lens in the middle ofthe rectangle formed by the marking rods Because the actualdry beach surface can be approximated as a plane you candetermine the water line by the points of four bars and thedry beach is approximately thought to be in the same planeFigure 2 shows the layout of the marker rods and the cameraThe red points represent the marking rods and the blue lineis called water line which represents the boundary of the drybeach and the water surface LP1 LP2 RP1 RP2 dry beachand thewater line are in the same plane By the points LP1 andLP2 we can define line LL and the line LL intersects with thewater line at point CL By the points RP1 and RP2 we candefine line RL and the line RL intersects with the water lineat the point CR The distance between CL and CR is equal tothe distance between RP1 and LP1

According to the invariance of X-ray imaging a point inspace is still a point on the imaging plane and a straight line


Image p

lane


i

j

k

f

Optical axis

Q = (X Y Z)

q = (x y f)O


The camera

LP2

LP1

RP2

RP1

CRCL

L2

LL RL

BB

Dry beach

The water line









3 Measurement Steps




119906 minus 1199060

= (1199061015840 minus 1199060) (1 + 119896

1199061199032)

V minus V0

= (V1015840 minus V0) (1 + 119896V119903

2) (6)


(a) (b)



















Sign




119894119895



as

119863 (119894 119895) =119898

sum119901=1

119899

sum119902=1

10038161003816100381610038161003816119878119894119895 (119901 119902) minus 119879 (119901 119902)10038161003816100381610038161003816 (7)












150 1478 146200 1946 27300 3097 32




5 Conclusion


1= 1198852in


Acknowledgments



References












































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










Image p

lane


i

j

k

f

Optical axis

Q = (X Y Z)

q = (x y f)O


The camera

LP2

LP1

RP2

RP1

CRCL

L2

LL RL

BB

Dry beach

The water line









3 Measurement Steps




119906 minus 1199060

= (1199061015840 minus 1199060) (1 + 119896

1199061199032)

V minus V0

= (V1015840 minus V0) (1 + 119896V119903

2) (6)


(a) (b)



















Sign




119894119895



as

119863 (119894 119895) =119898

sum119901=1

119899

sum119902=1

10038161003816100381610038161003816119878119894119895 (119901 119902) minus 119879 (119901 119902)10038161003816100381610038161003816 (7)












150 1478 146200 1946 27300 3097 32




5 Conclusion


1= 1198852in


Acknowledgments



References












































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










(a) (b)



















Sign




119894119895



as

119863 (119894 119895) =119898

sum119901=1

119899

sum119902=1

10038161003816100381610038161003816119878119894119895 (119901 119902) minus 119879 (119901 119902)10038161003816100381610038161003816 (7)












150 1478 146200 1946 27300 3097 32




5 Conclusion


1= 1198852in


Acknowledgments



References












































Volume 2014




Journal of











Journal of


Function Spaces






Algebra












Sign




119894119895



as

119863 (119894 119895) =119898

sum119901=1

119899

sum119902=1

10038161003816100381610038161003816119878119894119895 (119901 119902) minus 119879 (119901 119902)10038161003816100381610038161003816 (7)












150 1478 146200 1946 27300 3097 32




5 Conclusion


1= 1198852in


Acknowledgments



References












































Volume 2014




Journal of











Journal of


Function Spaces






Algebra













150 1478 146200 1946 27300 3097 32




5 Conclusion


1= 1198852in


Acknowledgments



References












































Volume 2014




Journal of











Journal of


Function Spaces






Algebra






































Volume 2014




Journal of











Journal of


Function Spaces






Algebra









Research Article Real-Time Dry Beach Length Monitoring for Tailings Dams … · 2019. 7. 31. ·...

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Transcript of Research Article Real-Time Dry Beach Length Monitoring for Tailings Dams … · 2019. 7. 31. ·...