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

arXiv: https://arxiv.org/abs/2103.07382 · HTML: https://mlsanity.com/paper/2103.07382/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2021.

## Built on by (most cited first, 10 of 10)

- [Bridging POMDPs and Bayesian decision making for robust maintenance planning under model uncertainty: An application to railway systems](https://mlsanity.com/paper/2212.07933/) (2022)
- [Neural modal ordinary differential equations: Integrating physics-based modeling with neural ordinary differential equations for modeling high-dimensional monitored structures](https://mlsanity.com/paper/2207.07883/) (2022)
- [Quantifying the value of information transfer in population-based SHM](https://mlsanity.com/paper/2311.03083/) (2023)
- [Conditional deep generative models as surrogates for spatial field solution reconstruction with quantified uncertainty in Structural Health Monitoring applications](https://mlsanity.com/paper/2302.08329/) (2023)
- [A decision framework for selecting information-transfer strategies in population-based SHM](https://mlsanity.com/paper/2307.06978/) (2023)
- [LLM-Based Test Oracles: Source-of-Authority Taxonomy -- A Systematic Literature Review](https://mlsanity.com/paper/2607.05031/) (2026)
- [Information Avoidance and Overvaluation in Sequential Decision Making under Epistemic Constraints](https://mlsanity.com/paper/2106.04984/) (2021)
- [Improving decision-making via risk-based active learning: Probabilistic discriminative classifiers](https://mlsanity.com/paper/2206.11616/) (2022)
- [Towards risk-informed PBSHM: Populations as hierarchical systems](https://mlsanity.com/paper/2303.13533/) (2023)
- [Transformer-Based Approach to Optimal Sensor Placement for Structural Health Monitoring of Probe Cards](https://mlsanity.com/paper/2509.07603/) (2025)

## Abstract (arXiv)

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.