Guest Editors
Prof. Yuan Ren, Southeast University, China.
Email: magren@126.com
Dr. Hwa Kian Chai, University of Edinburgh, United Kingdom.
Email: Hwakian.Chai@ed.ac.uk
Dr. Ziyuan Fan, Zhejiang Sci-Tech University, China.
Email: fanzy1216@163.com
Prof. Xiaoling Liu, Ningbo University, China.
Email: liuxiaoling@nbu.edu.cn
Prof. Xiang Xu, Southeast University, China.
Email: xxuseu@126.com
Summary
In past decades, great progress in sensing technologies, communication systems and computing algorithms promoted profoundly applications of structural health monitoring (SHM) systems in bridges, especially large span bridges. Monitoring objectives usually include operational environments, external loadings and structural responses. The main purposes of bridge SHM is to monitor service condition, assess structural performance, and detect anomalies, guiding maintenance and management with the goal of ensuring bridge integrity. Data processing and deep mining play a key role in pursuing this goal, which involve both the theory and applications. Data from multiple sources should meanwhile perform effective fusion. Problems raised during service periods with the utilization of SHM data may also provide significant conclusions for bridge design and construction. In addition, some faults caused by sensors of the SHM system can be diagnosed by data mining. It avoids unnecessary further inspection. In recent years, with the rapid development of data analyzing techniques, including the hot artificial Intelligence and machine learning, many novel methods are proposed to explore data relationships and hidden structural information based on massive bridge SHM data. On the one hand, the adoption of many new techniques and intelligent sensors improves the accuracy and timeliness of collected SHM data, on the other hand, it brings challenges in data acquisition, storage, processing, analysis as well. Thus, the main objective of the special issues is to report advanced data mining methods in bridge SHM and its applications based on latest technique innovations.
The specific topics include but not limited to:
Big data theory for bridge SHM
Data acquisition and storage technique
Novel bridge SHM data analysis method
Intelligent sensors
Fusion of multi-source data
Data based sensor fault diagnosis
Data aided bridge design and construction
Structural performance analysis and evaluation
Bridge condition assessment
Maintenance strategy for bridges based on SHM data
Data based anomaly detection for bridges
Case study and application of bridge SHM data
Keywords
Monitoring Data, Machine Learning, Condition Assessment, Time Series Analysis, Smart Bridges, Structural Performance, Anomaly Detection, Early Warning
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