Deformation prediction is an important part of concrete dam safety monitoring. In recent years, the random forest (RF) algorithm has attracted more and more attention in the field of dam safety monitoring because of its fast speed and strong generalization ability. However, the performance of RF is easily affected by many factors, such as the drift of measured value in displacement and the inappropriate setting of parameters of RF. To solve the above problems, the indicator variable model (IVM) is used to identify and eliminate the drift of measured values in this paper, and the sand cat swarm optimization (SCSO) is applied to optimize RF for the first time. On the grounds of this, a deformation prediction system of a concrete dam based on the IVM and RF algorithm optimized by SCSO is proposed. The case study shows that IVM can correct the interference of monitoring data accurately, and the maximum error rate is less than 3%; in the aspect of parameter optimization of RF, the results of the SCSO algorithm are obviously better than those of the TAE method and PSO algorithm, and the corresponding OOB error is the minimum; in terms of prediction performance, compared with TAE-RF, PSO-RF, LSTM and SVM, SCSO-RF has higher accuracy and stronger stability, and its SSE and MSE are reduced by at least 91%, MAE and RMSE are reduced by at least 71%, and R² is very close to 1. The results of study provide a new method for the automatic online evaluation of dam safety performance.

Cracking and compression damage of concrete face slabs directly influence the leakage condition and operational safety of concrete-faced rockfill dams (CFRDs). During operation, however, underwater face-slab damage is difficult to perceive and localize in a timely manner. Rockfill deformation is a dominant factor affecting the damage state of concrete face slabs. Accordingly, this paper proposes a diagnostic method for underwater damage zones in concrete face slabs of CFRDs by capturing the intrinsic relationship between seepage and deformation observations. First, Random Forest (RF) is combined with SHapley Additive exPlanations (SHAP) to reduce the dimensionality of the influencing factor set for seepage discharge, which is constructed from monitoring data on rockfill settlement, face-slab joint deformation, and environmental loads. A RIME-optimized Light Gradient Boosting Machine model is then developed to establish the seepage discharge–deformation mapping relationship. The model-calculated seepage discharge is subsequently compared with a seepage-discharge control threshold determined by a bootstrap-assisted empirical quantile method to identify abnormal deformation periods. Based on SHAP analysis of the seepage–deformation coupling relationship during these abnormal periods, underwater damage zones in concrete face slabs are diagnosed by identifying key abnormal deformation locations that contribute significantly to abrupt increases in seepage discharge. A case study of an in-service CFRD confirms the effectiveness and feasibility of the proposed method, with the identified underwater damage zones in concrete face slabs showing good agreement with inspection results. These findings highlight the practical value of the proposed method for rapid underwater damage diagnosis and structural health monitoring of CFRDs.