Application of Neural Networks in Bridge Health Prediction based on Acceleration and Displacement Data Domain

Transcription

1 Proceedigs of the Iteratioal MultiCoferece of Egieers ad Computer Scietists 213 Vol I,, March 13-15, 213, Hog Kog Applicatio of Neural Networks i Bridge Health Predictio based o Acceleratio ad Displacemet Data Domai Rei Suryaita ad Azla Ada, Member, IAENG Abstract The health coditio of the bridge ca be predicted through sesors readig i bridge moitorig. The sesors measure the acceleratio ad displacemet of bridge respose. The data is set to the local server through the data acquisitio. Iterpretatio of the data applied eural etwork i the localized server system. This paper aims to defie performace of the acceleratio ad displacemet data domai as iput i applied eural etworks. The architecture of eural etworks model used a iput layer, oe ad two hidde layers with euros ad a output layer. The iput layer cosists of time-acceleratio domai ad time-displacemet domai of the bridge due to earthquake loads. Meawhile, the output layer cosists of bridge coditio level which is determied usig fiite-elemet aalysis software. The traiig activatio used Gradiet Descet Back-propagatio ad activatio trasfer fuctio used Log Sigmoid fuctio. The bridge coditio is categorized i a rage to 3, which idicates the extet of bridge health coditio ragig from safe to high-risk level. The case study is 3 spas of box-girder s bridge subject to four earthquakes loads. The results showed that the predictio of bridge health coditio based o displacemet data domai with oe hidde layer is more acceptable compared with based o acceleratio. The compariso obtais the recommedatio of the best of data readig from the sesors to predict the bridge health coditio. The applicatio eural etworks i the bridge health predictio ca help the authorities to kow the coditio of the bridge due to earthquake at moitorig time, as the repair ad maiteace of bridges ca be performed as early as possible before the bridge was damaged. Idex Terms eural etworks, acceleratio, displacemet, bridge health coditio. N I. INTRODUCTION EURAL etworks are computer processes that attempt to imitate the workig process of huma brai. The activity of eural etworks associated with the use of itelliget. The learig mechaisms i eural etworks exist to acquire the kowledge. The architectural model of eural etworks has bee classified as various types based o their traiig activatio. The multi perceptro layers architectures are usually selected to solve may problems usig eural etworks. The eural etworks have the ability Mauscript received December 24, 212; revised Jauary 28, 213. Rei Suryaita is with the Civil Egieerig Departmet - Uiversity of Riau, Jl. HR Subratas Km.12,5 Pekabaru 28293, Riau - Idoesia phoe: reisuryaita@ yahoo.co.id Azla Ada is with Faculty of Civil Egieerig Uiversity Tekologi Malaysia, 8131 Skudai Johor Bahru, Malaysia azlaada@yahoo.com.my. ISSN: (Prit); ISSN: (Olie) to model the o-liear relatioship betwee a set of iput variable ad the correspodig outputs without the eed for predefied mathematical equatios. Furthermore, eural etworks do ot eed prior kowledge of the ature to the relatioship betwee the model iputs ad correspodig outputs. Compariso to traditioal methods, eural etworks tolerate relatively imprecise, oisy or icomplete data. Approximate results are less vulerable to outliers, have better bee filterig capacity ad more adaptive. This eables eural etworks to overcome the limitatios of the existig methods ad successful i be applied o may problems withi the field of Civil Egieerig. The eural etworks have bee applied i Civil Egieerig sice the past decades. Referece [1] ivestigated the use of eural etwork i some Civil Egieerig system. The traiig ad testig process utilize actual field data as the iput. The target output is the theoretical solutio of the problem beig aalyzed. The results showed that the eural etworks are reliable as well as the other covetioal methods. Some other researchers iterested to develop the eural etworks algorithm despite their presetly basic form at solvig direct mappig problems. Therefore, curretly the total of the applied eural etworks i the Civil Egieerig studies have icreased[2]. Eve i the bridge egieerig field, may civil egieerig researchers had applied the eural etwork i the latest research such as [3], [4], ad [5]. However, there is a little discussio about which the best of both acceleratio ad displacemet values for iput data i eural etworks, especially for bridge health predictio. Accordigly, the aim of this paper is to defie performace of the acceleratio ad displacemet data as a iput domai i bridge health predictio due to a earthquake. As of, the civil egieer ca make a recommedatio to bridge authorities for the choice of optimal sesors. II. BRIDGE HEALTH CONDITIONS AFTER EARTHQUAKE Bridge s structure eeds to be observed periodically i the real time. I bridge health moitorig, the damage of the bridge ca be kow ad detected early through data readig by the sesors. The acceleratio ad displacemet data readig was set to the local server through data acquisitio. Iterpretatio of data readig used the eural etworks i the localized server system. Several researchers observed the acceleratios ad displacemets as the iput data i eural etworks, such as [4], ad [6]. Referece [4] used eural etworks to observe a bridge uder dyamic load, especially geeral traffic load. The objective of the research is to estimate the bridge displacemet which correspods to the

2 Proceedigs of the Iteratioal MultiCoferece of Egieers ad Computer Scietists 213 Vol I,, March 13-15, 213, Hog Kog strai of the bridge. O the other had [6] studied the acceleratio based approach usig eural etworks. The objective of research is to predict the displacemet of buildig respose uder earthquake excitatio. The iputs data are the acceleratio, velocity ad displacemet at groud ad several stories of buildig. The others researchers ivestigated the applicatio of eural etworks i existig bridge evaluatio such as [7], ad detected a bridge damage such as [8] used frequecies ad mode shapes as the iput data. Studies about the applicatio of eural etworks o bridge structures uder seismic have bee coducted by [3], [9], ad [1]. I structural dyamic, the respose of the bridge due to earthquakes commoly is derived from (1) M ]{ Y& } + [ C]{ Y& } + [ K]{ Y} = [ M ]{ u& } (1) [ g Fig. 1. The Fig.1 displayed the peak groud acceleratios (PGA) of the earthquakes are.1539g (1.51 m/s 2 ) for Sa Ferado earthquake,.8677g (8.51 m/s 2 ) for New Zealad earthquake,.4731g (4.64 m/s 2 ) for Lomaprieta earthquake, ad.383g (3.73 m/s 2 ) for Laders earthquake. The acceptace criteria of pier damage based o structural performace levels i FEMA 356 [14]. The criteria are Immediate Occupacy (IO), Life Safety (LS) ad Collapse Prevetio (CP). The IO is defied as the structure still safe to occupy which oly very limited structural damage has occurred after a earthquake. The risk of life-threateig ijury is expected very low. The LS is defied as some structural elemet ad compoet are severely damaged but the risk of life-threateig ijury is expected low. The CP is defied as the structure is o the verge of partial or total collapse ad sigificat risk of ijury may exist. where [M], [C] ad [K] respectively is matrix of mass, dampig ad stiffess. Meawhile Y &, Y &, ad Y idividually is vector of acceleratio, velocity, ad displacemet of a bridge respose. Vector u& & is acceleratio of earthquake g excitatio. By usig the ucouplig procedure, the modal equatio of th mode ca be writte as (2). Y&& + 2 Y& + Y = u& (2) 2 ξω ω 1 φ g Displacemet for each mode show as (3) y( t) = φ Y ( t) (3) where ξ, ω, ad φ respectively are dampig ratio, frequecy ad umber of mode shape. The acceleratio is geerated by secod time derivative of displacemet. The displacemet values of a bridge respose describe the performace of the bridge uder a earthquake loadig. I bridge moitorig, both of acceleratio ad displacemet values ca be resulted from measuremet by sesors were istalled. The acceleratio ad displacemet values ca also be produced from fiite-elemet aalysis usig a computer program. The costructio era of a bridge is a good idicator of likely performace, with higher damage levels expected i older costructio tha i ewer costructio [11]. The more ages of the bridge structure, the loger loadig to have bee accepted. Therefore, bridge structure moitorig is ecessary doe as periodically i order to kow the bridge health coditio at the give time. Accordig to [12], damage of bridge structure is ormally defied as the itetioal or uitetioal chages i material ad geometric properties of the bridge, icludig chages i boudary or supportig coditios ad structural coectivity, which adversely affect the curret or future serviceability of the bridge. Damage ca occur uder large trasiet loads such as strog motio earthquakes ad ca also be accumulated icremetally over log periods of time due to factors such as fatigue ad corrosio damage. I this paper bridge health coditio is focused o the o liear behavior of piers due to earthquakes. The aalysis of the simulatio model used the fiite-elemet software. Respose acceleratio data is adopted from [13] as show i Fig. 1. Respose acceleratio of earthquake data from PEER [13]. III. NEURAL NETWORK IN BRIDGE HEALTH PREDICTION This study used the Neural Network Back Propagatio (BPNN) algorithms. The best performaces of BPNN deped o the selectio of suitable iitial weight, learig rate, mometum, etworks architecture model ad activatio fuctio. The weight describes the acceleratio or retardatio of the iput sigals. The architecture model for this system has umber of iput euros, oe ad two hidde layers with euros ad a output. The iput etworks cosist of time-acceleratio domai ad timedisplacemet domai of the bridge seismic respose aalysis. The umbers of iput correspod to the umbers of sesor o the bridge moitorig. Meawhile the output layer is the level of a bridge health coditio due to a earthquake, which is resulted by fiite-elemet aalysis software. The architecture model of eural etworks illustrates i Fig. 2. Fig. 2. The architecture model of eural etworks with 2 hidde layers i the system. The traiig fuctio used Gradiet Descet Backpropagatio to miimize the sum squared error (E) betwee ISSN: (Prit); ISSN: (Olie)

3 Proceedigs of the Iteratioal MultiCoferece of Egieers ad Computer Scietists 213 Vol I,, March 13-15, 213, Hog Kog the output value of eural etwork ad the give target values. The total error is defied as (4). 1 2 E = ( t j a j ) (4) 2 j J where t j deotes target value, a j deotes activatio value of output layer, ad J is set of traiig examples. The steps are repeated util the mea-squared error (MSE) of the output is sufficietly small. The fial output is geerated by a o liear filter Φ caller activatio fuctio or trasfer fuctio. The trasfer fuctio for this model used Log Sigmoid fuctio, which has a rage of [,1] to obtai the output. This fuctio is differetiable fuctio ad suitable used i BPNN multilayer as show i (5). aet, j a = 1 (1 + e ) (5) j where a l et, j = w ij a i ] i = 1 [ + θ j Each i represets oe of the uits of layer l coected to uit j ad ɵ j represets the bias. The weight, w ij of etworks has adjusted to reduce the overall error. The updated weight o the lik coectio the i th ad j th euro of two adjacet layers is defied as, W = η E / W ) (6) ij ( ij where, η is the learig rate parameter with rage to 1 ad E / W ij is the error gradiet with referece to the weight. I this study, iput data has ormalized by a liear ormalizatio equatio as follows: ' z = z z ) ( z z ) (7) i ( i mi max mi where is the ormalized iput values, z i the origial data, z max ad z mi, respectively, the maximum ad miimum values. IV. A CASE STUDY The bridge simulatio model covered 3 spas of box girder cocrete. The legths of the 3 spas are 79m, 11m, ad 79m respectively. The 2 sesors were assumed to be istalled o the top of piers as show i Fig. 3. The sesors measure the acceleratio ad displacemet values of the bridge respose. acceptability. The bridge model i this study has bee simulated to receive four excitatios of earthquake i Fig. 1. Thereby, resposes of bridge structure due to some earthquakes have applied as iput i the traiig process. The damage of structure elemet from fiite-elemet aalysis is described i Fig. 4. The criteria of bridge damage is based o stadard of Federal Emergecy Maagemet Agecy [14]. Iitial of B is described as operatio level, which states trasitio from safe level to IO level. The IO is immediate-occupacy; LS is life-safety, ad CP is collapseprevetio. The level before damage is described with S (safe level). Fig.4 illustrates the poit of high risk damage at the top of piers (CP level). Fig. 4. Damage level of bridge model due to the excitatio of Lomaprieta, 1989 earthquake Fig.5 ad Fig.6 show the respose of the bridge model due to Lomaprieta earthquake. The acceleratio ad displacemet respose of the bridge is measured durig secods at the poit where sesor1 ad sesor2 will be located. The damage level occurred after 9.25 secods. This level cosists of IO level (1 st idex), LS level (2 d idex) ad CP level (3 rd idex) respectively at 9.26, 11.5, ad 11.5 secods. The time before 9.25 secods is categorized a safe level (zero idex). The maximum acceleratio values of bridge respose are m/s 2 at sesor1 ad m/s 2 at sesor2 (Fig. 5). The maximum displacemet value at sesor1 is.486m, whereas at sesor2 is.985m (Fig. 6). Acceleratio (m/s 2 ) Time (sec) Sesor1 Sesor2 Safe level = idex Damage level = 1 st, 2 d, ad 3 rd idex Fig. 5. The acceleratio respose of bridge model due to the excitatio of Lomaprieta 1989 earthquake. Fig. 3. The 3 spas of box girder bridge model The bridge model i Fig. 3 has bee aalyzed usig the fiite-elemet software. The o liear time history aalysis has bee applied i the model so the behavior ad coditio o the model due to earthquake ca be kow as a detail at the give time. Accordig to FEMA 356, time history aalysis shall be performed with at least three time-histories data sets of groud motio. Sice three time history data sets are used i the aalysis of structure, the maximum value of each respose parameter shall be used to determie desig Displacemet (m) Sesor1 Sesor Safe level = idex Damage level = - Time (sec) 1 st, 2 d, ad 3 rd idex Fig. 6. The displacemet respose of bridge model due to the excitatio of Lomaprieta 1989 earthquake. ISSN: (Prit); ISSN: (Olie)

4 Proceedigs of the Iteratioal MultiCoferece of Egieers ad Computer Scietists 213 Vol I,, March 13-15, 213, Hog Kog The architecture of eural etwork method i this study is show i Fig. 2. The study used 1 ad two hidde layers to fidig the best result for predictio of bridge coditio. The architectures model for 1 hidde layer has 5 euros for iput, 5 euros for hidde ad 4 euros for output layer. The topology of the eural etwork euros is Whereas the architectures model for 2 hidde layers has 5 euros for iput layer, 5 euros for 1 st hidde ad 5 euros for 2 d hidde layer ad 4 euros for output layer. The topology of euros ca be writte as The euros for iput layer cosist of time, acceleratio ad displacemet from sesor1 ad sesor2. The output layer is a damage level of the bridge which is categorized ito 4 idexes. The idexes are (zero) for safety level (S), 1 (oe) for IO level, 2 (two) for LS level ad 3 (three) for CP level. Oe of the excitatios is the Lomaprieta earthquake, which has 234 data for iput ad output data as show i Table I. The safety level has bee described by 186 data durig 9.25 secods for S= output idex, 42 data durig 2.5 secods for IO=1 output idex, 2 data durig.5 secods for LS=2 output idex, ad 4 data durig.15 secods for CP=3 output idex. TABLE I THE EXAMPLE OF INPUT DATA FROM LOMAPRIETA EARTHQUAKE No. of INPUT DATA TIME ACC1 DISPL1 ACC2 DISPL2 OUTPUT 1.E E-5.E E-4 S = E E E E-4 S = E E E E-4 IO= E E-3 1.7E E-3 IO= E E E E-3 IO= E+ 2.74E E E-3 IO= E E-2-8.2E E-3 LS= E+ 2.26E E E-3 LS= E+ 3.64E E E-3 CP= E+ 4.69E E-2 4.7E-3 CP= E+ 4.86E-2 1.9E-1 9.9E-4 CP= E+ 4.6E E E-3 CP=3 Note: ACC1 ad DISPL1 are acceleratio ad displacemet values at Sesor1. ACC2 ad DISPL2 are acceleratio ad displacemet values at Sesor2. The total umbers of iput ad output data are 189, which is resulted by fiite-elemet aalysis due to four earthquakes excitatio. The eural etworks used 7% data for traiig, 15% data for testig ad 15% data for validatio process. The parameters to idicate the ed of traiig are the mea square error (MSE), maximum of epochs ad learig rate (Lr). The MSE with.1 performace goal has bee used i the etworks, whereas the maximum umber of epoch used 5, ad learig rate used.1. The etworks have bee examied by the computer with specificatio Itel Core i5-241m, the power of processor is 2.3 GHz with turbo boost up to 2.9 GHz ad memory 4 GB. The results from the models with oe hidde layer are show i Fig. 7 ad Fig. 8, while the models with 2 hidde layers are show i Fig.9 ad Fig. 1. The MSE of eural etworks of a model based o acceleratio data domai is see i Fig.7. The MSE of traiig, testig ad validatio process have the same tred lie alog the 1 th to ISSN: (Prit); ISSN: (Olie) 3 th iteratio. The error of the validatio process icrease sice after the 3 th epoch. There was overfittig at the process. The etwork begis to over-fit the data, sice the MSE of the validatio set will typically begi to rise. The discrepacy of the MSE validatio idicates the architectures of the model usuitable for acceleratio data domai with oe hidde layer. Mea Square Error (MSE) MSE Mea MSE Traiig MSE Testig MSE Validatio Fig. 7. The Meas Square Error of eural etwork model for 1 hidde layer of acceleratio domai Fig. 8 illustrates all MSE i the eural etworks model based o displacemet have the same tred lie ad i tue sice 1 th epoch. The error o all processes decreases alog the iteratios. The result idicates the architectures model for 1 hidde layer ca be accepted ad used for predict the damage level based o the displacemet data domai. Mea Square Error (MSE) MSE Mea MSE Traiig MSE Testig MSE Validatio Fig. 8. The Meas Square Error of Neural Network model for 1 hidde layer of displacemet domai Mea Square Error (MSE) MSE Mea MSE Traiig MSE Testig MSE Validatio Fig. 9. The Meas Square Error of Neural Network model for 2 hidde layer of acceleratio domai The eural etworks model based o acceleratio data domai with two hidde layers is show i Fig. 9. The figure illustrates all MSE models have the same tred after 1

5 Proceedigs of the Iteratioal MultiCoferece of Egieers ad Computer Scietists 213 Vol I,, March 13-15, 213, Hog Kog iteratios. The MSE values of testig process are higher tha other MSE values. The error ito the testig process is ot used durig the traiig process, but it is used to compare the differet models. Fig.1 shows the MSE of the model based o displacemet data domai. The MSE of validatio has the fluctuatio alog the iteratios before 25 epochs. The fluctuatio describes the etworks have ot bee coverget yet. It meas the acceleratio data domai more acceptable rather tha the displacemet data domai for two hidde layers model. Mea Square Error (MSE) MSE Mea MSE Traiig MSE Testig MSE Validatio Fig.1. The Meas Square Error of Neural Network model for 2 hidde layer of displacemet domai The compariso of acceleratio ad displacemet data domai for 1 ad 2 hidde layers model has bee observed i Table II ad Table III. The comparisos are the average of mea square error (MSE mea), regressio (R mea) ad ruig time (CPU time). The best performace of MSE value is the smallest of MSE, because it meas the smallest of the error occurred i the calculatio. However the best regressio value is the highest oe close to 1. The regressio with value close to 1 defies the predictio value almost 1% close to the actual oe. The best performace of CPU time is the shortest time to process the calculatio i cetral processig uit (CPU). The CPU time is measured i secods. The CPU time is depedet with CPU s computatioal power ad specificatio of the computer. The both Table II ad Table III show the MSE value decreases sice the epoch icreases. However CPU time icreases sice the epoch icreases. O the other had regressio value icreases close to 1 sice the epoch icreases. TABLE II Compariso of acceleratio ad displacemet domai for 1 hidde layer s Acceleratio Displacemet MSE Mea R Mea CPU Time MSE Mea R Mea CPU Time Table II shows the all average of regressios (R-mea) is above 8% for displacemet data domai with 1 hidde layer. The table describes the displacemet data domai has the smaller MSA values ad the higher R-mea values rather tha the acceleratio data domai. The best of MSE ad R- mea value are.531 ad at 5 epochs for displacemet data domai. The values are 3.89% smaller tha MSE of acceleratio data domai ad 1.37% higher tha R mea of acceleratio data domai. The process time eeds 2.46% shorter tha the acceleratio data domai. Aalog with Table II, the compariso of the acceleratio ad displacemet data domai for 2 hidde layers has bee show i Table III. Table III displays the all average of regressios (R-mea) is above.81% for displacemet data domai with 2 hidde layers. Similar with Table II, the table displays the displacemet data domai has the smaller MSA values ad the higher R-mea values rather tha the acceleratio data domai. TABLE III Compariso of acceleratio ad displacemet domai for 2 hidde layer s Acceleratio Displacemet MSE Mea R Mea CPU Time MSE Mea R Mea CPU Time The best of MSE ad R-mea value are.512 ad.831 at 5 epochs for displacemet data domai. The values are 4.12% smaller tha MSE of acceleratio data domai ad.94% higher tha R mea of acceleratio data domai. However, the process time eeds 2.39% loger tha the acceleratio data domai. The result shows the eural etworks model for 1 hidde layer model is suitable for the displacemet data domai because the results display the values of the MSE mea are smaller ad the regressio values (R mea) are higher close to 1 ad the CPU time is shorter tha acceleratio data domai. The smaller MSE mea defies the error occurred i calculatio to predict the bridge damage is smaller. Whereas the eural etworks model for 2 hidde layers model is usuitable with the displacemet data domai. Although the model has the MSE mea values are smaller ad regressio values (R mea) are higher close to 1, however the process time is loger tha acceleratio data domai. Therefore for predictio damage level o bridge moitorig is recommeded to use 1 hidde layer with displacemet data domai. V. CONCLUSION The bridge health system used several sesors to detect the behavior of a bridge such as bridge deformatio ad damage. The sesors coected to data logger ad set the iformatio data such as displacemet ad acceleratio to the local server. The data is used as iput by eural etworks withi the server system. The architecture of eural etwork method i this study used oe ad two hidde layers. The results deote the models with oe hidde layer for acceleratio data domai had the discrepacy of the MSE validatio. The problem idicates the architectures of the model usuitable for acceleratio data domai with oe hidde layer. While the eural etworks model based o displacemet with a hidde layer have the same tred lie ad i tue sice the 1 th epoch. The error o all processes decreases alog the iteratios. ISSN: (Prit); ISSN: (Olie)

6 Proceedigs of the Iteratioal MultiCoferece of Egieers ad Computer Scietists 213 Vol I,, March 13-15, 213, Hog Kog The eural etworks model based o acceleratio data domai with two hidde layers illustrates all MSE models have the same tred after 1 iteratios. The MSE values of testig process are higher tha others MSE values. The error durig the testig process is ot used durig the traiig process, but it is used to compare the differet models. While the MSE validatio of the eural etwork based o displacemet data domai with two hidde layers had the fluctuatio alog the iteratios before 25 epochs. The fluctuatio describes the etworks has ot reached covergece. The compariso of acceleratio ad displacemet data domai for oe ad two hidde layers model has bee cocluded based o MSE mea value, regressio mea value ad CPU time of the etwork model. Both comparisos show the MSE mea value decreases sice the epoch icreases. However, CPU time icreases whe the epoch icreases. Whereas regressio value icreases close to 1 sice the epoch icreases. The average of regressios (R-mea) for displacemet data domai with 1 ad two hidde layers is above 8%. The value deotes the damage values from the displacemet data domai has bee predicted 8% close to the actual damage values. Coversely, the process time for two hidde layers eeds the loger time tha the acceleratio data domai. Therefore, the bridge health predictio based o displacemet domai data for oe hidde layer is more accurate rather tha the acceleratio data domai with 1 ad two hidde layers. Accordig to the results, the eural etworks method based o the displacemet data has the best performace sice uses oe hidde layer i the system. The reaso describes the displacemet is derived from secod time to geerate the acceleratio. The displacemet has simpler physic quatity rather tha acceleratio so the coverget is approached faster. Actually, most bridge moitorig system use the accelerometer sesors to measure the acceleratio of bridge respose, because the accelerometer sesor is simpler to istall i the field. Furthermore, the acceleratio from accelerometer sesors ca be modified directly to coduct the displacemet value before etry ito the eural etworks system server. Cosequetly, the moitorig system is recommeded to be used i the eural etworks with oe hidde layer based o displacemet domai. The implemetatio of the itelliget eural etwork method for the bridge seismic moitorig system ca help the bridge authorities to predict the stability ad health coditio of the bridge structure at ay give time. [4] Ok, S., W. So, ad Y.M. Lim, "A study of the use of artificial eural etworks to estimate dyamic displacemets due to dyamic loads i bridges." Joural of Physics: Coferece Series, (1). [5] Cheg, J. ad Q.S. Li, "Artificial eural etwork-based respose surface methods for reliability aalysis of pre-stressed cocrete bridges." Structure ad Ifrastructure Egieerig, (2): p [6] Qia, Y. ad A. Mita, "Acceleratio-based damage idicators for buildig structures usig eural etwork emulators." Structural Cotrol & Health Moitorig, (6): p [7] Che, M., "A Neural Network Approach for Existig Bridge Evaluatio Based o Grid." 28 Iteratioal Symposium o Itelliget Iformatio Techology Applicatio, Vol I, Proceedigs, 28: p [8] Mehrjoo, M., et al., "Damage detectio of truss bridge joits usig Artificial Neural Networks." Expert Systems with Applicatios, (3): p [9] Jeg, C.H. ad Y.L. Mo, "Quick seismic respose estimatio of prestressed cocrete bridges usig artificial eural etworks." Joural of Computig i Civil Egieerig, (4): p [1] Chakraverty, S., T. Marwala, ad P. Gupta, "Respose Predictio of Structure System Subject to Earthquake Motios usig Artificial Neural Network." Asia Joural of Civil Egieerig (Buildig ad Housig), 26. VOL. 7, NO. 3. [11] Che, W.-F. ad L. Dua, eds. Bridge Egieerig Seismic Desig. 23, CRC Press: Florida. 442 pp. [12] Wog, K.-Y., "Desig of a structural health moitorig system for log-spa bridges." Structure ad Ifrastructure Egieerig, 27. 3(2): p [13] PEER. Pacific Earthquake Egieerig Research Groud Motio Database march 211; Available from: [14] FEMA356, Prestadard ad Commetary for The Seismic Rehabilitatio of Buildigs. 2, Federal Emergecy Maagemet Agecy. Refereces [1] A. T. Goh, B., "Some Civil Egieerig Applicatios of Neural Networks." Structural ad Buildig Boord Structural Poel Paper 1543, Proc. Ist Ciu. Bldgs. 1994,14, Nov., [2] Flood, I., "Towards the ext geeratio of artificial eural etworks for civil egieerig." Advaced Egieerig Iformatics, (1): p [3] Kerh, T., C. Huag, ad D. Guaratam, "Neural Network Approach for Aalyzig Seismic Data to Idetify Potetially Hazardous Bridges." Mathematical Problems i Egieerig, 211: p ISSN: (Prit); ISSN: (Olie)