Time-dependent system reliability analysis of a high concrete-faced rockfill dam by integrating multi-source monitoring information

This paper presents a time-dependent reliability analysis method for concrete-faced rockfill dams (CFRDs) by integrating multiple failure modes and multi-source monitoring data via Bayesian networks. Initially, two sub-Bayesian networks are constructed to fuse dam parameters, two related failure modes, and three types of monitoring data. Subsequently, the prior failure probabilities of the dam system for each period are calculated through the time-variant response relationships among network nodes. These response relationships introduce a time-variant term to quantify the effects of water level and creep. Finally, various types of monitoring data are utilized to update parameter distribution, resulting in the posterior failure probabilities. The proposed method is applied to 233-meter-high Shuibuya CFRD. The results indicate that Bayesian networks offer a more comprehensive and reliable assessment. Water level induces periodic variations in system reliability, while creep drives the long-term trend by increasing slabs' failure probabilities. The failure probabilities of dam system increase over the initial ten years and stabilize as creep converges. The slabs’ failure probabilities vary from location. Seepage failure probability is primarily dominated by the most critical slab. Utilizing multi-source monitoring data can reduce uncertainties, mitigate the interference of localized abnormal data, and identify potential failure locations. This approach supports enhanced dam safety management.