Interval early warning method for state of engineering structures based on structural health monitoring data
Jiahui Liu
Department of Mechanical Engineering, Tongji University, Shanghai, China
Search for more papers by this authorCorresponding Author
Xianrong Qin
Department of Mechanical Engineering, Tongji University, Shanghai, China
Correspondence
Xianrong Qin, Department of Mechanical Engineering, Tongji University, Shanghai 201804, China.
Email: [email protected]
Search for more papers by this authorYuantao Sun
Department of Mechanical Engineering, Tongji University, Shanghai, China
Search for more papers by this authorQing Zhang
Department of Mechanical Engineering, Tongji University, Shanghai, China
Search for more papers by this authorJiahui Liu
Department of Mechanical Engineering, Tongji University, Shanghai, China
Search for more papers by this authorCorresponding Author
Xianrong Qin
Department of Mechanical Engineering, Tongji University, Shanghai, China
Correspondence
Xianrong Qin, Department of Mechanical Engineering, Tongji University, Shanghai 201804, China.
Email: [email protected]
Search for more papers by this authorYuantao Sun
Department of Mechanical Engineering, Tongji University, Shanghai, China
Search for more papers by this authorQing Zhang
Department of Mechanical Engineering, Tongji University, Shanghai, China
Search for more papers by this authorFunding information: Science and Technology Commission of Shanghai Municipality, Grant/Award Number: 19DZ1161203
Summary
In order to carry out early warning for the abnormal state of engineering structures in time and optimize early warning interval (WI), a new construction of WI method based on relevance vector machine (RVM) and particle swarm optimization (PSO) under the framework of lower upper bound estimation (LUBE) is proposed. First, extract time-domain features of structural monitoring data and then two RVM models are trained using time-domain features to construct lower and upper bound of WI. Kernel parameters of RVM are optimized by PSO algorithm to minimize the loss function of LUBE and obtain the best optimal evaluation indices of WI. The LUBE based on PSO-RVM interval warning method is verified through a numerical simulation and a case study on structural monitoring data of quayside container crane (QCC), and the performance of WI is compared with other prediction methods. The results indicate that the proposed construction of WI method can effectively improve the quality of WI, realize early warning damage, and provide better performance for early WI.
Open Research
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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