A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Diagnosis, Prognosis, and Maintenance Decision Making for Civil Infrastructure: Bayesian Data Analytics and Machine Learning
Due to the aging of civil infrastructure and the associated economic impact, there is an increasing need to continuously monitor structural and non-structural components for system life-cycle management, including maintenance prioritization. For complex infrastructure, this monitoring process involves different types of data sources collected at different timescales and resolutions, including but not limited to abstracted rating data from human inspections, historical failure record data, uncertain cost data, high-fidelity physics-based simulation data, and online high-resolution structural health monitoring (SHM) data. The heterogeneity of the data sources poses challenges to implementing a diagnostic/prognostic framework for decision making for life-cycle actions such as maintenance. Using quoin blocks components of a miter gate as an example, this chapter presents a holistic Bayesian data analytics and machine learning (ML) framework to demonstrate how to integrate various data sources using Bayesian and ML methods for effective SHM, and Prognostics and Health Management (PHM). In particular, this chapter discusses how Bayesian data analytics and ML methods can be applied to (1) diagnosis of bearing loss-of-contact degradation in quoin blocks; (2) optimized sensor placement for SHM on the gate; (3) fusion of various data sources for effective PHM; and (4) deciding maintenance strategies by considering the behavioral aspect of human decision making under uncertainty.
Diagnosis, Prognosis, and Maintenance Decision Making for Civil Infrastructure: Bayesian Data Analytics and Machine Learning
Due to the aging of civil infrastructure and the associated economic impact, there is an increasing need to continuously monitor structural and non-structural components for system life-cycle management, including maintenance prioritization. For complex infrastructure, this monitoring process involves different types of data sources collected at different timescales and resolutions, including but not limited to abstracted rating data from human inspections, historical failure record data, uncertain cost data, high-fidelity physics-based simulation data, and online high-resolution structural health monitoring (SHM) data. The heterogeneity of the data sources poses challenges to implementing a diagnostic/prognostic framework for decision making for life-cycle actions such as maintenance. Using quoin blocks components of a miter gate as an example, this chapter presents a holistic Bayesian data analytics and machine learning (ML) framework to demonstrate how to integrate various data sources using Bayesian and ML methods for effective SHM, and Prognostics and Health Management (PHM). In particular, this chapter discusses how Bayesian data analytics and ML methods can be applied to (1) diagnosis of bearing loss-of-contact degradation in quoin blocks; (2) optimized sensor placement for SHM on the gate; (3) fusion of various data sources for effective PHM; and (4) deciding maintenance strategies by considering the behavioral aspect of human decision making under uncertainty.
Diagnosis, Prognosis, and Maintenance Decision Making for Civil Infrastructure: Bayesian Data Analytics and Machine Learning
Structural Integrity
Cury, Alexandre (editor) / Ribeiro, Diogo (editor) / Ubertini, Filippo (editor) / Todd, Michael D. (editor) / Vega, Manuel A. (author) / Hu, Zhen (author) / Yang, Yichao (author) / Chadha, Mayank (author) / Todd, Michael D. (author)
Structural Health Monitoring Based on Data Science Techniques ; Chapter: 3 ; 45-73
Structural Integrity ; 21
2021-10-24
29 pages
Article/Chapter (Book)
Electronic Resource
English
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