文献类型：期刊文献

英文题名：Compressed sensing with smooth L0 constraints for moving force identification from bridge response measurements

作者：Liang, Yi Hou, Zhilong Yu, Ling

第一作者：Liang, Yi;梁意

通信作者：Yu, L[1]

机构：[1]Guizhou Inst Technol, Sch Civil Engn, Guiyang 550003, Peoples R China;[2]Jinan Univ, Sch Mech & Construct Engn, Guangzhou 510632, Peoples R China

第一机构：贵州理工学院土木工程学院

通信机构：corresponding author), Jinan Univ, Sch Mech & Construct Engn, Guangzhou 510632, Peoples R China.

年份：2025

卷号：36

期号：1

外文期刊名：MEASUREMENT SCIENCE AND TECHNOLOGY

收录：;EI(收录号：20250917962346);Scopus(收录号：2-s2.0-85218628736);WOS:【SCI-EXPANDED(收录号:WOS:001337544200001)】；

基金：This work was jointly supported by the National Natural Science Foundation of China (52178290 and 51678278), the Youth Science and Technology Talent Growth Project of the Department of Education of Guizhou Province (Qianjiaoji [2024] No. 167), and the Guizhou Province Science and Technology Plan Project (Qiankehe Platform KXJZ [2024] No. 020).

语种：英文

外文关键词：structural health monitoring; vehicle-bridge interaction; compressed sensing; moving force identification; smoothed L0 norm

摘要：Compressed sensing (CS), as an emerging information sampling technique, has been successfully applied in the field of moving force identification (MFI). However, existing MFI CS models often fail to obtain the optimal sparse solutions and frequently underestimate the amplitude of local impact forces. To effectively address this issue, a new CS method is proposed for MFI based on smooth L0 norm constraints and bridge response measurements. Firstly, a smooth function is used to approximate the L0 norm, establishing a noise CS reconstruction model for MFI. The introduction of the smoothing function can locally convexify the original MFI problem and enhance the smoothness and differentiability of the objective function, making the optimization problem easier to solve. Subsequently, the Polak-Ribiere-Polyak formula is adopted to point the descent direction of the new objective function, and the sparse solution is iteratively advanced through the conjugate gradient algorithm. Finally, the applicability and feasibility of the proposed method is confirmed by numerical simulations and vehicle-bridge interaction tests, respectively. The results show that the proposed method can accurately identify moving forces from limited measurements of bridge responses. Compared with existing methods, it can provide more precise sparse solutions with higher robustness to measurement noises, and address the issue of underestimating on the amplitude of local impact forces, which is expected to enhance the performance and in-situ applicability of MFI.