Diagnosis, Prognosis, and Maintenance Decision Making for Civil Infrastructure: Bayesian Data Analytics and Machine Learning

Abstract

Due to the aging of civil infrastructure and the associated economic impact, there is an increasing need to continuously monitor structural and nonstructural components for system life cycle management, including maintenance prioritization. For complex infrastructure, this monitoring process involves different types of data sources collected at different time scales 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.

A Probabilistic Computational Framework for the Prediction of Corrosion-Induced Cracking in Large Structures

“…The interface between metal and electrolyte is separated with a layer of non-penetrable passive film except the center semicircle area, which is the initial pit assumed. Normal stress, shear stress with an average magnitude of 50 MPa, and combinations therein are applied to the model because these stress status are common in a miter gate (a type of large civil infrastructure that serves as the underlying structure of interest in this work) in service according to a validated simulation [38] as Figure 9 shows. The right surface is fixed.…”

The modeling of risk perception in the use of structural health monitoring information for optimal maintenance decisions

“…The works by Fauriat et al [28] and Lin et al [29] utilize the Value of Information as a metric to guide the inspection policies such that the cost acquired over the life of the structure is minimal. Vega et al [30] discuss the application of data analytics and machine learning to maintenance decision-making for civil infrastructure. Yang et al [31,32] discuss optimal sensor design with the target of obtaining measurement data based on which a maintenance policy could be effectively implemented.…”