A NEW APPROACH FOR SUBWAY TUNNEL DEFORMATION MONITORING: HIGH-RESOLUTION TERRESTRIAL LASER SCANNING

Size: px

Start display at page:

Download "A NEW APPROACH FOR SUBWAY TUNNEL DEFORMATION MONITORING: HIGH-RESOLUTION TERRESTRIAL LASER SCANNING"

Beatrice Patrick
5 years ago
Views:

1 A NEW APPROACH FOR SUBWAY TUNNEL DEFORMATION MONITORING: HIGH-RESOLUTION TERRESTRIAL LASER SCANNING L Jan a, Wan Youchuan a,, Gao Xanjun a a School of Remote Sensng and Informaton Engneerng, Wuhan Unversty,129 Luoyu Road, Wuhan, Chna, @qq.com,( ychwan, Commsson V, WG V/3 KEY WORDS:Terrestral Laser Scannng, deformaton montorng, subway tunnel, hgh precson, movng least squares ABSTRACT: Wth the mprovement of the accuracy and effcency of laser scannng technology, hgh-resoluton terrestral laser scannng (TLS) technology can obtan hgh precse ponts-cloud and densty dstrbuton and can be appled to hgh-precson deformaton montorng of subway tunnels and hgh-speed ralway brdges and other felds. In ths paper, a new approach usng a ponts-cloud segmentaton method based on vectors of neghbor ponts and surface fttng method based on movng least squares was proposed and appled to subway tunnel deformaton montorng n Tanjn combned wth a new hgh-resoluton terrestral laser scanner (Regl VZ-400). There were three man procedures. Frstly, a ponts-cloud conssted of several scannng was regstered by lnearzed teratve least squares approach to mprove the accuracy of regstraton, and several control ponts were acqured by total statons (TS) and then adjusted. Secondly, the regstered ponts-cloud was resampled and segmented based on vectors of neghbor ponts to select sutable ponts. Thrdly, the selected ponts were used to ft the subway tunnel surface wth movng least squares algorthm. Then a seres of parallel sectons obtaned from temporal seres of fttng tunnel surfaces were compared to analyss the deformaton. Fnally, the results of the approach n z drecton were compared wth the fber optcal dsplacement sensor approach and the results n x, y drectons were compared wth TS respectvely, and comparson results showed the accuracy errors of x, y, z drectons were respectvely about 1.5mm, 2mm, 1mm. Therefore the new approach usng hgh-resoluton TLS can meet the demand of subway tunnel deformaton montorng. 1. Introducton Wth the development of laser scannng technque, ts accuracy keeps enhancng. The sngle-pont accuracy of phase laser scanner has reached 1mm, and whch of the VZ-400 scanner from Regl Company has reached 2mm. Because of ths mprovement, n the hghly precse deformaton montorng felds, more projects trend to use laser scanner. If combnng TLS and hghly precse TS, the errors of laser scanner wll be corrected and the observaton accuracy wll be further mproved. Ths combnaton can be appled to some hghly precse deformaton montorng n such felds as brdges, tunnels, hgh buldngs, dams and so on. Addtonal measurements of deformaton montorng are carred out by explorng sngle-pont trangulaton to measure the coordnates of control ponts n dfferent tme. Ths montorng method costs long observaton tme and ntense labor, s poor n automatc aspects and s constraned by the layout of the control ponts, resultng n that t can only reflect the local deformaton not the entre deformaton and the observaton accuracy s easly nfluenced by observaton condtons. TLS can quckly acqure the entre pont cloud nformaton of the objects whose deformaton wll be montored n a short tme and then through processng data the entre deformaton of these montored objects can be analyzed. So ths approach has become hot n deformaton montorng. At present, some nternal scholars have started such researches, but most of them are focused on the applcaton of TLS to the deformaton montorng of those projects that requre not very hgh accuracy such as landsldes, ground subsdence, debrs flow, buldngs and so on. TLS has been appled to the deformaton montorng of landsldes (XU et al., 2010), n whch TLS can get the nformaton of many natural objects on the landsldes as montored ponts, combnng t wth the data of these ponts acqured by GPS and TS to montor the deformaton. Ths research ddn t gve the defnte accuracy. The buldng deformaton was studed by applyng pont cloud data acqured by TLS to ft the surface of the buldng (CAI et al., 2010). The applcaton of TLS technque to the deformaton montorng s wdely studed abroad. Stuart Gordon analyzed the accuracy of the deformaton of surfaces montored by TLS through comparng the results of two tests. The comparson results showed that n the vertcal drecton the accuracy of TLS s 6 to 12 tmes hgher than the nomnal accuracy of nstruments and the hghest accuracy can reach 0.5 mm, whch has met the hghly precse requrement of the deformaton montorng. Whle these researches were carred out n the deal condtons, t could not present the results generated n general condtons (Gordon et al., 2004). H.S. Park appled TLS to the deformaton montorng of buldngs and mproved the accuracy through establshng deformaton modals by the measured data (Park et al., 2007). G. Btell also studed the deformaton montorng of landsldes by usng ths technque and contrasted the results wth the results montored by photogrammetry approaches. The contrast results showed that TLS can quckly and effcently montor the deformaton of landsdes, ts accuracy s precse and t needs less control ponts compared wth photogrammetry. TLS approach s smple, useful and effcent for ths applcaton (Btell et al., 2004). A Correspondng author. 223

2 pont cloud segmentaton method to extract sutable pont from the pont cloud acqured by TLS was employed to montor the deformaton of a 100 m long tunnel and mprove the accuracy of deformaton montorng (Lndenbergh et al., 2005). A deformaton of a cylndrcal tunnel was montored by frstly fttng the cylndrcal model and then explotng the statstcal tests method to statstcally analyze the deformaton montorng data of the tunnel (Van Goslga et al., 2006). Accordng to these researches some problems exsted n the deformaton montorng of tunnels by usng TLS: frstly, because of lacks of some necessary control strateges, the regstraton accuracy of the pont cloud data acqured n a few adjacent statons n the tunnel s a lttle low, so t needs to be mproved; secondly, TLS can acqure magnanmty pont cloud data causng that t s dffcult to search out the homologous pont n the scannng data acqured n dfferent tme, so there stll s not a good approach to montor dynamcally just by usng the orgnal pont cloud data. To solute the above problems, ths paper proposed some solutons whch combned the data acqured by TLS, TS and levels to regster the data of adjacent statons and mprove the regstraton accuracy. 2. The algorthm based on the normal vectors of pont cloud Because of the large amount of pont cloud data, there s much redundancy nformaton. Hence t s crucal to quckly extract relable feature nformaton from such dense pont cloud data. Ths paper proposed a pont cloud segmentaton algorthm whch can retan the feature of pont cloud based on the varance of normal vectors of pont cloud. Ths algorthm extracts the features of the pont cloud accordng to the angle varance of the normal vectors of the pont cloud, retans more ponts n those places where the feature s obvous but retans fewer ponts where the feature s not obvous. In the pont cloud data, the normal vector of a pont P s defned as a normal vector of a tangent plane that goes through P on a curved surface F(x,y,z)=0 conssted of P and ts adjacent ponts. The key to construct the curved surface based on the adjacent ponts s to fnd the dstrbuton of the trangular surface conssted of the adjacent ponts of P. Then the dstrbuton of the normal vector of P can be calculated by these trangular surfaces. Fgure 1 llustrates the normal vectors of ponts on the curved surfaces. condton). Then the frst trangle s found. At the same tme ths pont s removed from ths array, t wll not partcpate n the next calculaton. Step 3 - Calculate Q s n whch drecton of Q, f t s n the clockwse drecton, then contnue searchng the next trangle n the clockwse drecton; Otherwse, the search wll be carred on n the antclockwse drecton. Step 4 - Then change the start edge to PQ and traverse the array n the drecton whch s gven n the thrd step to fnd the next pont that satsfes these condtons to construct the next trangle. Keep on ths teraton calculaton untl all the trangular surfaces that satsfed the condtons are found and store them n an ndex array. In ths algorthm, the tangent plane of a pont P s defned as a surface whch s ftted by the N ponts closest to P, and a vertcal lne gong through ths pont of the tangent plane s recorded as the normal vector of ths pont. Because all the ponts adjacent to P may not be located n one plane completely, t s necessary to convert ts tangent plane to mnmze the value descrbed n Eq.1. mn ( p p )( p p ) p Nd ( p ) Another llustraton of Eq.1 s to calculate a symmetrcal sem-defnte matrx as descrbed n Eq.2. For a pont n 3D space, the sze of the matrx s 3 3. Cv ( p p )( p p ) p Nd ( p ) T Here, Nd(p) s a set of ponts adjacent to P. Calculate three egenvalues of Cv and ther responsble egenvectors. Then the egenvector whch s responded to the mnmum postve egenvalue can reflect approxmately as the vertcal lne of the tny tangent plane or the normal vector of ths pont. The angle of the normal vectors of adjacent ponts can be calculated by Eq.3: Cos n( p) n( p ) n( p) n( p ) T (1) (2) (3) Fgure1. The normal vectors of ponts on the curved surfaces Step 1 - Fnd the closest pont to P, and assume t s Q, jon P and Q to compose an edge of the frst trangular surface. Step 2 - Traverse other k ponts closest to P (except Q), assume the traversal pont s Q, calculate each angle QPQ. Set an angle threshold and select the Q that corresponds to the shortest edge from those angles whch satsfes the condton as one pont of the frst trangular surface (PQ are the ponts on the shortest edge among those edges whch satsfes the Where, n(p) and n(p ) represent the normal vector of P and p respectvely. The hgher the value of cosθ s, the more smlar two vectors are; the lower the value of cosθ s, the bgger the dfference of these two ponts s and they may be the ponts located on the edge of the features. Then through settng some sutable thresholds pont cloud can be dvded nto dfferent segments. The concrete mplementaton steps are llustrated as followng: Step 1 - Frst select a pont P, and fnd K ponts p (=1, 2,, K) closest to P; Step 2 - Calculate the angles of the normal vectors n(p )between P and ths K ponts p (=1, 2,, K) respectvely; Step 3 - If the value of cosθ s greater than 0.95, label ts correspondng pont p and classfy t and P nto the same class; Step 4 - If all the ponts p (=1, 2,, K) are n the same class wth P, then select a pont farthest away from P as the next P to contnue the above three steps. Among the K ponts, f there s any pont whch s n other classes dfferent from the class whch P s n, t wll be the start pont n the next teraton. 224

Step 5 - Cluster the pont cloud data whch have been classfed already agan, and then resample these pont cloud. The orgnal pont cloud data acqured n one staton s llustrated n Fgure 2.

1 Test flow 3. Experments and analyss Fgure 2. Orgnal pont cloud data In ths test, the deformaton of a 100m long subway tunnel whch s located n the conjuncton part between the Tanjn No.

1 subway tunnel led to ts deformaton. When the deformaton exceeds defnte extent, t wll affect the safety of the subway. Therefore, t s sgnfcant to montor the deformaton of ths secton of the subway.

3 Step 5 - Cluster the pont cloud data whch have been classfed already agan, and then resample these pont cloud. The orgnal pont cloud data acqured n one staton s llustrated n Fgure 2. The pont cloud data processed by the clusterng and segmentaton algorthm s shown n Fgure 3 where dfferent colors represent dfferent classes. Fgure 4 llustrates the pont cloud after resampled. Fgure 4. Pont cloud data after resampled 3.1 Test flow 3. Experments and analyss Fgure 2. Orgnal pont cloud data In ths test, the deformaton of a 100m long subway tunnel whch s located n the conjuncton part between the Tanjn No.1 subway tunnel and the Tanjn west ralway staton was montorng. Because the west ralway staton s n constructon, the excavatng and constructon n the place above the No.1 subway tunnel led to ts deformaton. When the deformaton exceeds defnte extent, t wll affect the safety of the subway. Therefore, t s sgnfcant to montor the deformaton of ths secton of the subway. Three dfferent montorng methods by optc fbre dsplacement meter (OFDM), the TS and TLS have been adopted nto the deformaton montorng of ths secton of the subway tunnel. The deformaton montorng flow by usng TLS s llustrated n Fgure 5. Fgure 3. Pont cloud data processed by clusterng and segmentaton Set the control ponts and staton ponts Acqure the pont cloud data by TLS n dfferent tme Acqure the coordnates of the control ponts measured by total statons n dfferent tme Acqure the elevatons of the control ponts measured by levels n dfferent tme Regster and flter the data precsely Check the control ponts to reduce the errors Adjust these data acqured by total statons and levels n dfferent tme Smplfy the pont cloud based on ther normal vectors Construct the Delaunay network of the pont cloud and analyze the deformaton Comparson and analyss Analyze the deformaton of the sgnal pont Analyze the deformaton montorng through the data measured by OFDM Fgure 5. The deformaton montorng flow The entre process of the deformaton montorng conssted of three parts: data acquston, data processng and comparson and analyss of the test results. Data processng manly ncluded the regstraton and fltraton of the pont cloud data, 225

the tangent planes extracton of the pont cloud data and the constructon of the model of the entre tunnel, and the adjustment of the data acqured by TS and levels. 3.

4 the tangent planes extracton of the pont cloud data and the constructon of the model of the entre tunnel, and the adjustment of the data acqured by TS and levels. 3.2 Data collecton and regstraton Four groups of test data were acqured n four dfferent tme perod. The pont cloud data nformaton of subway tunnel s shown n Table 6. Accordng to these data the deformaton of the subway tunnel can be montored. In the experment a Vz-400 scanner and the Leca TS30 were used to acqure test data. The angle observaton accuracy of TS30 can reach 0.5s, and ts dstance observaton accuracy n the prsm pattern can get to the 0.6mm+1ppm, whch n the speculum pattern s 1mm+1ppm. Because the accuracy of the TS s hgher than TLS and hghly precse levels were also adopted to acqure hghly precse elevaton of these control ponts, ths combnaton can correct the coordnate errors of those control ponts measured by TLS. In the experment 4 targets at least were placed as control ponts n each staton. In the process of the data acquston and processng, owng to nfluence of the errors of the nstruments themselves, the subjectve factors of the observers and the atmospherc temperature factor, there are devatons between the observed data and the real data. Only through adoptng strct requrement n every step to mprove the accuracy can the errors of the observed data be mnmzed. Especally for those control data measured by TS and levels, generally the same control pont need to be measured repeatedly and these repeat observatons would be adjusted to calculate the fnal results. Date Perod of scannng tme Number of scannng statons Data sze Scannng range of sngle staton ( ) Scannng resoluton of sngle staton ( ) Scannng tme n sngle staton Number of ponts n sngle staton Feb.17 23:30(P.M)-3:30(A.M) 4 4*680.5M 100* Fed.18 23:30(P.M)-3:30(A.M) 4 3*680.5M 100* Feb.19 23:30(P.M)-3:30(A.M) 4 3*680.5M 100* Feb.22 23:30(P.M)-3:30(A.M) 4 4*680.5M 100* Table 6. The pont cloud data nformaton of the subway tunnel The data of control ponts measured by TS and levels were adopted to regster the overlapped data acqured from dfferent statons. Ths method can greatly mprove the regstraton accuracy. Table 7 shows the regstraton accuracy of each staton. If wthout the control of the data acqured by usng TS and levels, the standard devaton of regstraton s about 4mm, whle f adopted them, the error s controlled wthn 1mm. The regstraton accuracy s mproved sgnfcantly. Because t nfluences the followng data processng, for whch t s crucal to mprove the regstraton accuracy. Avg.radal Devaton Avg.theta Devaton Avg.ph Devaton Standard Devaton Scan Scan Scan Table 7. Regstraton accuracy between the adjacent statons 3.3 Analyss of the deformaton of the subway tunnel The pont cloud data of the subway tunnel acqured n dfferent tme perod were contnued to be smplfed after regstered and pre-processed. Then the number of pont was reduced from 1 mllon to 200 thousand whch meant the number of remanng ponts was only about 20% of the number of orgnal ponts. However they stll retaned the features of orgnal pont cloud and mproved the speed of the constructon of the trangulaton. Drect constructon of Delaunay trangulaton retaned the orgnal nformaton of the tunnel and they can reflect the curve surface condton of the tunnel. Hence the analyss result of the deformaton would be more accurate. Through establshng the models of the pont cloud data acqured by scannng more than twce respectvely, the deformaton of the tunnel can be analysed contnually. Two datasets of the same place were supermposed together to compare ther dfference whch can reflect the tendency of the deformaton of the tunnel. Ths reflecton s vsual but there stll lacks an exact standard to measure the deformaton. Snce the dfference of the elevatons between two models can reflect the concrete tendency and degree of deformaton more exactly, t would provde more enough proof to support the determnaton. A model of the tunnel after one scannng s llustrated n Fgure 8. An ntegrated model constructed by supermposng two models whch were constructed respectvely by two dfferent pont cloud datasets of the same tunnel acqured n dfferent tme s llustrated n Fgure 9, n whch the data of blue color represents the model acqured n the frst tme and the orange data s the second model. From ths Fgure, t s obvously vsual that the elevaton n the second model s lower than the frst model n the roof, whch means there s some deformaton n the roof. Then a curve on behalf of the dfference between the elevatons of the two models s llustrated n Fgure 10. It reflects the deformaton of about 70m subway tunnel, n whch the most serous deformaton located n a place of the tunnel between 50 m and 60 m and the dfference reached 0.11 m. Fgure 8. A model of the tunnel after one scannng 226

5 Fgure 9. Comparson of two models constructed by two datasets acqured n dfferent tme Fgure 10. The curve of the dfference of the elevatons between two models The results montored by the method proposed n ths paper were respectvely compared to the results montored by TS and OFDM, as the Table 11 llustrates. Pont ID Observatons of the OFDM Observatons of the TS Observatons of the TLS Dfferences of the observatons between the TLS and the OFDM Dfferences of the observatons between the TLS and the TS J J J J J J J J J J Table 11. Comparson of three knds of results montored by dfferent methods The comparson n the table 3 llustrates that there were small dfferences between the ponts coordnates measured by TLS and the TS that was because the pont cloud data acqured by TLS was corrected by the coordnates of those control ponts measured by the TS, whle the dfferences between TLS and OFDM were a lttle bg and the maxmum error reached 3.4 mm. Although ths error dd not exceed the constraned error of 5mm, t was too bg for deformaton. The montorng result of OFDM s more precse, but due to ths measurement needed to put the nstrument nsde body of the tunnel whch cost too much and caused destructon of the tunnel, t s not an optmal method. If the accuracy of the deformaton montorng by TLS can be mproved further, the TLS wll be a better choose for deformaton montorng. 4. Concluson At present, the applcaton of employng TLS on the deformaton montorng of the subway tunnel s stll n the stage of researchng. Ths paper analyzed the problems that exsted n ths applcaton ncludng low effcency, low 227

6 accuracy and hard to transform the coordnates as well as other problems, then proposed a method of mprovng the accuracy of regstraton accuracy and solvng the problem of the transformaton of the coordnates by combnng the TLS, the TS and the level. In addton, the pont cloud was smplfed va a segmentaton algorthm based on ther normal vectors meanwhle ther features were stll reserved. At last a few models of the tunnel constructed by the pont cloud data acqured n dfferent tme were exploted to montor ts whole deformaton. A few experments based on these theores were carred out to analyze the accuracy and feasblty of the applcaton of montorng the deformaton of the subway tunnel by TLS. In contrast wth OFDM, the results show that the accuracy of TLS stll needs to be mproved to apply to the deformaton montorng of the subway tunnel. Ths problem n these felds needs to solve based on more researches. References XU, J., WANG, H., LUO, Y., WANG, S., YAN, X., Deformaton montorng and data processng of landslde based on 3D laser scannng [J]. Rock and Sol Mechancs, 31(7), pp CAI, L., WU, K., ZHANG, S., Applcaton of pont cloud plan fttng to deformaton montorng usng 3D laser scanner. Scence of Surveyng and Mappng, 35(5), pp Gordon, S., Lcht, D., Stewart, M., Franke, J., Modelng pont clouds for precse structural deformaton measurement. Internatonal Archves of Photogrammetry and Remote Sensng 35. Park, H., Lee, H., Adel, H., Lee, I., A new approach for health montorng of structures: terrestral laser scannng. Computer Aded Cvl and Infrastructure Engneerng 22, pp Btell, G., Dubbn, M., Zanutta, A., Terrestral laser scannng and dgtal photogrammetry technques to montor landslde bodes. Internatonal Archves of Photogrammetry, Remote Sensng and Spatal Informaton Scences, 35(Part B5), pp Lndenbergh, R., Pfefer, N., Rabban, T., Accuracy analyss of the Leca HDS3000 and feasblty of tunnel deformaton montorng. In: Proceedngs of the ISPRS Workshop, Laser scannng, Vol. XXXVI(3/W3), Enschede, The Netherlands, pp Van Goslga, R., Lndenbergh, R., Pfefer, N., Deformaton analyss of a bored tunnel by means of terrestral laser scannng. Proceedngs of Internatonal Archves of Photogrammetry, Remote Sensng and Spatal Informaton Scences, 36( Part 5), pp

Parallelism for Nested Loops with Non-uniform and Flow Dependences

Parallelism for Nested Loops with Non-uniform and Flow Dependences Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr