Missing Structural Health Monitoring Data Recovery Based on Bayesian Matrix Factorization

Shouwang Sun, Sheng Jiao, Qi Hu, Zhiwen Wang, Zili Xia, Youliang Ding () and Letian Yi
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Shouwang Sun: YunJi Intelligent Engineering Company Limited, Shenzhen 518000, China
Sheng Jiao: YunJi Intelligent Engineering Company Limited, Shenzhen 518000, China
Qi Hu: Zhongshan City Construction Group Company Limited, Zhongshan 528402, China
Zhiwen Wang: YunJi Intelligent Engineering Company Limited, Shenzhen 518000, China
Zili Xia: Hong Kong-Zhuhai-Macao Bridge Authority, Zhuhai 519060, China
Youliang Ding: Key Laboratory of C&PC Structures of the Ministry of Education, Southeast University, Nanjing 210096, China
Letian Yi: Key Laboratory of C&PC Structures of the Ministry of Education, Southeast University, Nanjing 210096, China

Sustainability, 2023, vol. 15, issue 4, 1-15

Abstract: The exposure of bridge health-monitoring systems to extreme conditions often results in missing data, which constrains the health monitoring system from working. Therefore, there is an urgent need for an efficient data cleaning method. With the development of big data and machine-learning techniques, several methods for missing-data recovery have emerged. However, optimization-based methods may experience overfitting and demand extensive tuning of parameters, and trained models may still have substantial errors when applied to unseen datasets. Furthermore, many methods can only process monitoring data from a single sensor at a time, so the spatiotemporal dependence among monitoring data from different sensors cannot be extracted to recover missing data. Monitoring data from multiple sensors can be organized in the form of matrix. Therefore, matrix factorization is an appropriate way to handle monitoring data. To this end, a hierarchical probabilistic model for matrix factorization is formulated under a fully Bayesian framework by incorporating a sparsity-inducing prior over spatiotemporal factors. The spatiotemporal dependence is modeled to reconstruct the monitoring data matrix to achieve the missing-data recovery. Through experiments using continuous monitoring data of an in-service bridge, the proposed method shows good performance of missing-data recovery. Furthermore, the effect of missing data on the preset rank of matrix is also investigated. The results show that the model can achieve higher accuracy of missing-data recovery with higher preset rank under the same case of missing data.

Keywords: matrix factorization; missing recovery; Bayesian inference; structural health monitoring (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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