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Title:: A method for discriminating damage level in vehiclebridge piercollision based on deep belief network

Author(s):: FENG Wei1; ZHANG Zhaojin2; SHAO Haipeng1; (1. School of Highway, Changan University, Xian 710064, Shaanxi, China； 2. Department of ElectronicInformation Engineering, Wuwei Occupational College, Wuwei 733000, Gansu, China)

Keywords:: bridge engineering; bridge pier safety; structural damage level; vehicle collision; deep learning

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DOI:: -

Abstract:: In order to solve the problem that the damage level of bridge pier was not easy to be monitored in real time and early warning, under the deep learning theory framework, the method of discrimination the damage level in the bridge pier was studied. The field theory was used to analyze the dynamics and energy conversion relationship during the collision of a car with a bridge pier, and a formula to calculate the damage field potential of the bridge pier was derived. The concept of risk assessment was used to define the damage levels based on different damage field potential values, and the damage level discrimination model for the vehiclebridge pier collision was established. The DBN can accurately extract the essential characteristics of the data and the Softmax classifier can effectively discriminate the multilevel probability. Hence, these two were combined to establish the damage level discrimination model for vehiclebridge pier collision. The relevant experimental and simulation data from existing research were input to the network model formulated, the parameters of the network structure were debugged, and then the results were compared with those obtained from the traditional discrimination methods based on machine learning. The results show that among the three network structures designed in this study, the deep belief network (DBN) model has the highest accuracy when the network depth is seven layers. The mean absolute error and the mean relative error are 0.37 and 14.8%, respectively. Compared to the artificial neural network model, the DBN model adopts the deep neural network structure, the discrimination level result is more stable, and the mean absolute error and the mean relative error are reduced by 1.34 and 11.5%, respectively. Compared to the random forest model, the DBN model can automatically extract the characteristic attributes of the data, the deviation of the obtained discrimination level result is lower, not exceeding 0.5 level, and the mean absolute error and the mean relative error are reduced by 0.46 and 4.7%, respectively. Hence, the DBN model is more efficient and accurate than the random forest model for the multidamage level discrimination problem. 6 tabs, 8 figs, 36 refs.

A method for discriminating damage level in vehiclebridge piercollision based on deep belief network

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

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