Diagnosis of embankment dam distresses using Bayesian networks. Part II. Diagnosis of a specific distressed dam
Based on prior information on common characteristics of dam distresses extracted from the dam distress database described in a companion paper, this paper attempts to extend the technique of Bayesian networks to the diagnosis of a specific distressed dam. The diagnosis is conducted by combining two sources of information, i.e., global-level knowledge from the database and project-specific evidence. Based on results of the diagnosis, key distress factors for a specific dam can be identified and suitable remedial measures can be suggested. Further, the Bayesian network analysis is conducted to evaluate the effectiveness of the adopted remedial measures. A case study on the diagnosis of a distressed embankment dam, Chenbihe Dam, with seepage problems is presented to illustrate the methodology. In this case study, the observed leakage rates, seepage exit locations, and boundary conditions of the embankment are used as project-specific evidence.