文献类型：期刊文献

英文题名：An efficient semi-analytical framework for moving load identification with arbitrary boundary conditions

作者：Hou, Zhilong Hao, Hong Liang, Yi Pan, Chudong

第一作者：Hou, Zhilong

机构：[1] Guangdong Provincial Key Laboratory of Earthquake Engineering and Applied Technology, Earthquake Engineering Research and Test Center, Guangzhou University, Guangzhou, China; [2] Key Laboratory of Earthquake Resistance, Earthquake Mitigation and Structural Safety, Ministry of Education, Guangzhou University, Guangzhou, 510006, China; [3] Center for Infrastructural Monitoring and Protection, School of Civil and Mechanical Engineering, Curtin University, Perth, Australia; [4] School of Civil Engineering, Guizhou Institute of Technology, Guiyang, 550003, China; [5] School of Civil Engineering and Transportation, Guangzhou University, Guangzhou, 510006, China

第一机构：Guangdong Provincial Key Laboratory of Earthquake Engineering and Applied Technology, Earthquake Engineering Research and Test Center, Guangzhou University, Guangzhou, China

通信机构：Guangdong Provincial Key Laboratory of Earthquake Engineering and Applied Technology, Earthquake Engineering Research and Test Center, Guangzhou University, Guangzhou, China

年份：2026

卷号：360

外文期刊名：Engineering Structures

收录：EI(收录号：20261720590741);Scopus(收录号：2-s2.0-105036726081)

语种：英文

外文关键词：Analytical models - Axles - Bridges - Computational efficiency - Data mining - Modal analysis - Orthogonal functions - Structural health monitoring - Time domain analysis - Vehicles

摘要：Existing moving load identification (MLI) methods encounter critical challenges in achieving a balance among modeling accuracy, computational efficiency, and applicability when formulating governing equations of vehicle-bridge interactions. To address these issues, an efficient semi-analytical framework for MLI based on vehicle-induced bridge responses is proposed in this study. Firstly, by incorporating the modal information of element nodes under arbitrary boundary conditions, a semi-analytical expression of orthogonal mode shape functions for the bridge is derived through the Schmidt orthogonalization technique. Secondly, the moving vehicle load is represented as a sequence of basis functions within short time intervals, and the bridge’s dynamic response is analytically calculated using Duhamel’s convolution integral, from which a semi-analytical vehicle-bridge governing equation applicable to arbitrary boundary conditions is formulated. Finally, to evaluate the effectiveness of the proposed method, numerical validations are performed using Euler-Bernoulli beam and truss bridge models with elastically supported boundary conditions. Experimental investigations are further conducted under laboratory conditions for typical scenarios involving varying axle loads, axle spacings, and vehicle speeds. The results show that the proposed MLI method is free from time-step constraints and effectively eliminates discretization errors. Besides, compared with the time-domain and state space methods, the proposed method achieves superior identification accuracy and noise robustness while maintaining low computational complexity, which offers a scalable and efficient solution for high-precision MLI problem and bridge health monitoring under arbitrary boundary conditions. ? 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.