Diagnosis and prediction of the leakage in highway tunnel based on damage accumulation model
AI Qing1, WANG Kun2, JIANG Xiaomo3,4, YUAN Yong2, WANG Hui1, DU Shouji1, HUANG Xingchun1
1. School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; 2. College of Civil Engineering, Tongji University, Shanghai 200092, China; 3. Department of Engineering Mechanics, Dalian University of Technology, Dalian 116024, Liaoning, China; 4. Key Laboratory of Digital Twins for Industrial Equipment of Liaoning Province, Dalian University of Technology, Dalian 116024, Liaoning, China
Abstract: The quantitative relationship between the seepage defects and several influencing factors was studied based on the various types of inspection data of a highway tunnel in Yunnan Province, China. Based on the survival analysis method, a Weibull damage accumulation model was established for predicting the leakage of tunnel developing with influencing factors and time. The reasonability of the physical meanings of model parameters was discussed, and followed by the validation of the accuracy of model prediction. The research found that the order of importance for the influencing factors of seepage defects were: the existence of cavity behind the lining, the grade of surrounding rock, whether neighboring the deformation joint, and operating time, respectively; in addition, according to the model prediction, the leakage of tunnel could continually increase over time, so it was suggested to adopt maintenance measures such as water sealing treatment. This research provided a new method for diagnosing the major influencing factors and predicting the future development of tunnel defects.
艾青, 王琨, 姜孝谟, 袁勇, 王辉, 杜守继, 黄醒春. 基于损伤累积模型的公路隧道渗漏水病害诊断与预测[J]. 隧道与地下工程灾害防治, 2021, 3(1): 37-47.
AI Qing, WANG Kun, JIANG Xiaomo, YUAN Yong, WANG Hui, DU Shouji, HUANG Xingchun. Diagnosis and prediction of the leakage in highway tunnel based on damage accumulation model. Hazard Control in Tunnelling and Underground Engineering, 2021, 3(1): 37-47.
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2021, Vol. 3 
