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Title:: Evaluation of technical condition of medium and small spanbridge based on machine learning

Author(s):: QIAO Peng1; LIANG Zhiqiang1; XU Kai1; ZHONG Chengxing2; 1; QIN Fengjiang3; (1. School of Civil Engineering, Changan University, Xian 710061, Shaanxi, China; 2. China Railway EryuanEngineering Group East China Survey and Design Co.Ltd, Hangzhou 310004, Zhejiang, China;3. School of Civil Engineering, Chongqing University, Chongqing 400045, China)

Keywords:: bridge engineering; evaluation of bridge technical condition; machine learning; medium and small span bridge; intelligent evaluation

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Abstract:: Aiming at the problem that the evaluation of highway bridge technology condition based on specification method needs to determine the weight of each factor artificially, an intelligent evaluation method of bridge technology condition based on machine learning was proposed. The technical condition evaluation index system was established with reference to the bridge inspection and evaluation specification, and the bridge condition database was established based on the inspection data of 388 mediumandsmallspan bridges, in Shanxi Province. The machine learning algorithm was used to construct the nonlinear mapping model of the evaluation value of each component of the bridge and the evaluation value of the bridge condition, and the optimal parameters of the algorithm were searched by drawing the learning curve and grid search. At the same time, the evaluation performance of five widely used machine learning algorithms, classification and regression tree (CART), support vector machine (SVM), random forest (RF), improved gradient boosting tree (XGBoost) and adaptive boosting (AdaBoost) were compared. The results show that the ensemble algorithm has better effect than the single algorithm, and in the case of the few training sets the prediction accuracy of each algorithm is more than 85%, especially the prediction accuracy of AdaBoost algorithm for bridge technical condition reaches 93%, indicating that AdaBoost algorithm can be better used to evaluate the bridge technical condition in the region. The method in this paper can not only make use of the data value accumulated in the inspection report, but also avoid the use of tedious calculation formulas and fixed weights to calculate the overall technical condition of the bridge based on the inspection specifications. It can be used as an auxiliary evaluation method and provide reference for improving the evaluation method of bridge technical conditions. Using this method, we can also establish bridge component condition database to realize the condition prediction of beams, piers and other components, and further reduce the impact of grading deduction defects in highway bridge evaluation specifications and artificially determined factor weights on the grade evaluation of overall bridge technical conditions. 7 tabs, 8 figs, 28 refs.

Evaluation of technical condition of medium and small spanbridge based on machine learning(PDF)

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

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