Methodology for Implementing Monitoring Data into Probabilistic Analysis of Existing Embankment Dams eng application/pdf 7495057 bytes Divac, Ljubo Marjanović, Miloš Divac, Dejan Pujević, Veljko info:eu-repo/semantics/article embankment dam; safety assessment; monitoring data; probabilistic analysis; Monte Carlo simulation; finite element method; shear strength reduction; factor of safety; reliability analysis; Bayesian updating 2025-06-17 Applied Sciences volume: 15 number: 6786 http://creativecommons.org/licenses/by/4.0/legalcode Monitoring data provide valuable information on embankment dam behavior but are typically not integrated into a classical probabilistic safety assessment. This paper introduces a Bayesian-inspired methodology to directly integrate actual dam monitoring records into a Monte Carlo probabilistic safety assessment using a finite element framework, without recalibrating the original input parameters ‘distributions. After the baseline (unweighted) set of simulations is generated, the method assigns a weight coefficient to each simulation outcome based on the likelihood of matching monitoring data, effectively updating the baseline probabilistic analysis results. Therefore, such “weighted” analysis produces an updated probability distribution of the dam’s factor of safety (FS) that reflects both prior uncertainty of model parameters and actual monitoring data. To illustrate the approach, a case study of a rockfill triaxial test specimen is analyzed: a baseline probabilistic analysis yields a mean FS ~1.7, whereas the weighted analysis incorporating monitoring data reduces the mean FS to ~1.5 and narrows the variability. The weighted analysis suggests less favorable conditions than the baseline projections. This methodology offers a transparent, computationally tractable route for embedding monitoring evidence into reliability calculations, producing more reflective safety estimates of actual dam behavior. MDPI, Basel, Switzerland https://unilib.phaidrabg.rs/o:8417 doi:10.3390/app15126786 ISSN: 2076-3417