Artificial Neural Network for Vertical Displacement Prediction of a Bridge from Strains (Part 1): Girder Bridge under Moving Vehicles
A real-time prediction method using a multilayer feedforward neural network is proposed for estimating vertical dynamic displacements of a bridge from the longitudinal strains of the bridge when vehicles pass across it. A numerical model for an existing five-girder bridge spanning 36 m proved by act...
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MDPI AG
2019-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/9/14/2881 |
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doaj-bbe82a874f4f4be499bd48d8df9d97a62020-11-25T01:52:33ZengMDPI AGApplied Sciences2076-34172019-07-01914288110.3390/app9142881app9142881Artificial Neural Network for Vertical Displacement Prediction of a Bridge from Strains (Part 1): Girder Bridge under Moving VehiclesHyun Su Moon0Suyeol Ok1Pang-jo Chun2Yun Mook Lim3Department of Civil and Environmental Engineering, College of Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, KoreaHyundai Engineering & Construction, 75 Yulgok-ro, Jongno-gu, Seoul 03058, KoreaInstitute of Engineering Innovation, School of Engineering, the University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 790-8577, JapanDepartment of Civil and Environmental Engineering, College of Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, KoreaA real-time prediction method using a multilayer feedforward neural network is proposed for estimating vertical dynamic displacements of a bridge from the longitudinal strains of the bridge when vehicles pass across it. A numerical model for an existing five-girder bridge spanning 36 m proved by actual experimental values was used to verify the proposed method. To obtain a realistic vehicle distribution for the bridge, vehicle type and actual headways of moving vehicles were taken, and the measured vehicle distribution was generalized using Pearson Type III theory. Twenty-five load scenarios were created with assumed vehicle speeds of 40 km/h, 60 km/h, and 80 km/h. The results indicate that the model can reasonably predict the overall displacements of the bridge (which is difficult to measure) from the strain (which is relatively easy to measure) in the field in real time.https://www.mdpi.com/2076-3417/9/14/2881vertical displacementslongitudinal strainsartificial neural networkmoving vehiclePearson Type III |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Hyun Su Moon Suyeol Ok Pang-jo Chun Yun Mook Lim |
| spellingShingle |
Hyun Su Moon Suyeol Ok Pang-jo Chun Yun Mook Lim Artificial Neural Network for Vertical Displacement Prediction of a Bridge from Strains (Part 1): Girder Bridge under Moving Vehicles Applied Sciences vertical displacements longitudinal strains artificial neural network moving vehicle Pearson Type III |
| author_facet |
Hyun Su Moon Suyeol Ok Pang-jo Chun Yun Mook Lim |
| author_sort |
Hyun Su Moon |
| title |
Artificial Neural Network for Vertical Displacement Prediction of a Bridge from Strains (Part 1): Girder Bridge under Moving Vehicles |
| title_short |
Artificial Neural Network for Vertical Displacement Prediction of a Bridge from Strains (Part 1): Girder Bridge under Moving Vehicles |
| title_full |
Artificial Neural Network for Vertical Displacement Prediction of a Bridge from Strains (Part 1): Girder Bridge under Moving Vehicles |
| title_fullStr |
Artificial Neural Network for Vertical Displacement Prediction of a Bridge from Strains (Part 1): Girder Bridge under Moving Vehicles |
| title_full_unstemmed |
Artificial Neural Network for Vertical Displacement Prediction of a Bridge from Strains (Part 1): Girder Bridge under Moving Vehicles |
| title_sort |
artificial neural network for vertical displacement prediction of a bridge from strains (part 1): girder bridge under moving vehicles |
| publisher |
MDPI AG |
| series |
Applied Sciences |
| issn |
2076-3417 |
| publishDate |
2019-07-01 |
| description |
A real-time prediction method using a multilayer feedforward neural network is proposed for estimating vertical dynamic displacements of a bridge from the longitudinal strains of the bridge when vehicles pass across it. A numerical model for an existing five-girder bridge spanning 36 m proved by actual experimental values was used to verify the proposed method. To obtain a realistic vehicle distribution for the bridge, vehicle type and actual headways of moving vehicles were taken, and the measured vehicle distribution was generalized using Pearson Type III theory. Twenty-five load scenarios were created with assumed vehicle speeds of 40 km/h, 60 km/h, and 80 km/h. The results indicate that the model can reasonably predict the overall displacements of the bridge (which is difficult to measure) from the strain (which is relatively easy to measure) in the field in real time. |
| topic |
vertical displacements longitudinal strains artificial neural network moving vehicle Pearson Type III |
| url |
https://www.mdpi.com/2076-3417/9/14/2881 |
| work_keys_str_mv |
AT hyunsumoon artificialneuralnetworkforverticaldisplacementpredictionofabridgefromstrainspart1girderbridgeundermovingvehicles AT suyeolok artificialneuralnetworkforverticaldisplacementpredictionofabridgefromstrainspart1girderbridgeundermovingvehicles AT pangjochun artificialneuralnetworkforverticaldisplacementpredictionofabridgefromstrainspart1girderbridgeundermovingvehicles AT yunmooklim artificialneuralnetworkforverticaldisplacementpredictionofabridgefromstrainspart1girderbridgeundermovingvehicles |
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