Special lssues

Advanced Data Mining in Bridge Structural Health Monitoring

Submission Deadline: 31 December 2024 Submit to Special Issue

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


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


Monitoring Data, Machine Learning, Condition Assessment, Time Series Analysis, Smart Bridges, Structural Performance, Anomaly Detection, Early Warning

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