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Bayesian Decision-Making Process Including Structural Health Monitoring Data Quality for Bridge Management

  • Structural Engineering
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KSCE Journal of Civil Engineering Aims and scope


The article introduces a decision-making framework and process for including the data quality (DQ) of structural health monitoring (SHM) for bridge management. The decision-making process relies on Bayesian and utility theories. Maintenance of existing bridges can benefit from SHM to obtain data on the bridge condition, which helps suggest maintenance decisions. Thus, the data quality plays a crucial role in preventing bridge-strengthening action when unnecessary and not intervening when needed. However, no management strategy or decision-making process has integrated the data quality yet. Aiming to fill those gaps, (Makhoul, 2022) offered data quality indicators and metrics. Then, this article extends the work to provide an updated general assessment procedure for existing bridge structures and a decision-making process to embed the SHM DQ. The decision-making process for SHM data quality uses the Bayesian and utility theory and considers uncertainties. It selects the optimal decision and evaluates the value of DQ assessment for monitoring strategies. Finally, a monitored bridge is used as a case study to apply the process, and data quality variation effect on the decision is analyzed. Results and comparisons are offered, and accordingly, the bridge owner is recommended to invest right from the start in good DQ for SHM.

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I acknowledge Politecnico di Milano, which financed this work through the Seal of Excellence project and the CYBERES research program. I acknowledge the researchers of KU Leuven, mainly Prof. Guido De Roeck, along with his research group, for the exceptional effort and the data offered for assisting the community of scientists. I acknowledge Prof. Eloi Figueiredo and Marcus Omori Yano for transmitting the Z24 data. I thank the support of the Institut de Recherche, ESTP, 28 Avenue du Président Wilson, F-94230, Cachan, France, for offering the opportunity to finalize this article.

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Correspondence to Nisrine Makhoul.

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Makhoul, N. Bayesian Decision-Making Process Including Structural Health Monitoring Data Quality for Bridge Management. KSCE J Civ Eng 28, 2818–2835 (2024).

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