Value of information from vibration-based structural health monitoring extracted via Bayesian model updating

TL;DR

This paper develops a detailed Bayesian decision analysis framework to quantify the value of information from vibration-based structural health monitoring, demonstrating its application on a deteriorating bridge to optimize maintenance decisions.

Contribution

It introduces a comprehensive Bayesian framework that models data generation, processing, and reliability updating for SHM systems, enabling quantitative decision support.

Findings

Framework quantifies the benefit of long-term SHM data.

Numerical results demonstrate optimality assessment of SHM systems.

Sequential Bayesian updating improves damage detection accuracy.

Abstract

Quantifying the value of the information extracted from a structural health monitoring (SHM) system is an important step towards convincing decision makers to implement these systems. We quantify this value by adaptation of the Bayesian decision analysis framework. In contrast to previous works, we model in detail the entire process of data generation to processing, model updating and reliability calculation, and investigate it on a deteriorating bridge system. The framework assumes that dynamic response data are obtained in a sequential fashion from deployed accelerometers, subsequently processed by an output-only operational modal analysis scheme for identifying the system's modal characteristics. We employ a classical Bayesian model updating methodology to sequentially learn the deterioration and estimate the structural damage evolution over time. This leads to sequential updating of the structural reliability, which constitutes the basis for a preposterior Bayesian decision analysis. Alternative actions are defined and a heuristic-based approach is employed for the life-cycle optimization. By solving the preposterior Bayesian decision analysis, one is able to quantify the benefit of the availability of long-term SHM vibrational data. Numerical investigations show that this framework can provide quantitative measures on the optimality of an SHM system in a specific decision context.