Summary The unique structures and foundations of a dam make its safety monitoring a complex task. As the most intuitive effect of dams, deformation contains important information on dam evolution. Actual response has the purpose of diagnosis and early warning compared with model prediction. Given the poor generalization ability of the conventional statistical model, establishing a dam deformation monitoring model is thus essential. The prediction of concrete dam deformation using statistical model and random forest regression (RFR) model is studied. To build an optimized RFR model, the statistical model is used to establish input variables, select the appropriate parameters Mtry and Ntree according to out‐of‐bag error, and extract strong explanatory variables. The model's advantage is that the influence factors can describe concrete dam deformation, and RF can serve as a sensible new data mining tool. The importance of variables for deformation prediction is measured by RF. The RFR method can extract representative influencing factors based on variable importance. The methods are applied to an actual concrete dam. Results indicate that the RFR model can be applied for analysis and prediction of other structural behavior.

Prediction models are essential in dam crack behavior identification. Prototype monitoring data arrive sequentially in dam safety monitoring. Given such characteristic, sequential learning algorithms are preferred over batch learning algorithms as they do not require retraining whenever new data are received. A new methodology using the genetic optimized online sequential extreme learning machine and bootstrap confidence intervals is proposed as a practical tool for identifying concrete dam crack behavior. First, online sequential extreme learning machine is adopted to build an online prediction model of crack behavior. The characteristic vector of crack behavior, which is taken as the online sequential extreme learning machine input, is extracted by the statistical model. A genetic algorithm is introduced to optimize the input weights and biases of online sequential extreme learning machine. Second, the BC a method is proposed to produce confidence intervals based on the improved online sequential extreme learning machine prediction. The improved online sequential extreme learning machine for identifying crack behavior is then built. Third, the crack behavior of an actual concrete dam is taken as an example. The capability of the built model for predicting dam crack opening is evaluated. The comparative results demonstrate that the improved online sequential extreme learning machine can provide highly accurate forecasts and reasonably identify crack behavior.

Earth-rock dams make up a large proportion of the dams in China, and their failures can induce great risks. In this paper, the risks associated with earth-rock dam failure are analyzed from two aspects: the probability of a dam failure and the resulting life loss. An event tree analysis method based on fuzzy set theory is proposed to calculate the dam failure probability. The life loss associated with dam failure is summarized and refined to be suitable for Chinese dams from previous studies. The proposed method and model are applied to one reservoir dam in Jiangxi province. Both engineering and non-engineering measures are proposed to reduce the risk. The risk analysis of the dam failure has essential significance for reducing dam failure probability and improving dam risk management level.