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.

No abstract

No abstract

No abstract

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.

Effective deformation monitoring is vital for the structural safety of super-high concrete dams. The radial displacement of the dam body is an important index of dam deformation, which is mainly influenced by reservoir water level, temperature effect, and time effect. In general, the safety monitoring models of dams are built on the basis of statistical models. The temperature effect of dam safety monitoring models is interpreted using approximate functions or the temperature values of a few points of measurement. However, this technique confers difficulty in representing the nonlinear features of the temperature effect on super-high concrete dams. In this study, a safety monitoring model of super-high concrete dams is established through the radial basis neural network (RBF-NN) and kernel principal component analysis (KPCA). The RBF-NN with strong nonlinear fitting capacity is utilized as the framework of the model, and KPCA with different kernels is adopted to extract the temperature variables of the dam temperature dataset. The model is applied to a super-high arch dam in China, and results show that the Hybrid-KPCA -RBF-NN model has high fitting and prediction precision and thus has practical application value.

Based on the principal component analysis, principal components that have major influence on data variance are determined by the energy percentage method according to the correlation between monitoring effects. Then principal components are extracted through reconstructing multi effects. Moreover, combining with the optimal estimation theory, the method of singular value diagnosis in dam safety monitoring effect values is proposed. After dam monitoring information matrix is obtained, single effect state estimation matrix and multi effect fusion estimation matrix are constructed to make diagnosis on singular values to reduce false alarm rate. And the diagnosis index is calculated by PCA. These methods have already been applied to an actual project and the result shows the ability of the monitoring effect reflecting dam evolution behavior is improved as dam safety monitoring effect fusion estimation can take accurate identification on singular values and achieve data reduction, filter out noise and lower false alarm rate effectively. dam safety monitoring, effect, singular value diagnosis, principal component analysis, optimal estimation Citation: Gu C S, Zhao E F, Jin Y, et al. Singular value diagnosis in dam safety monitoring effect values.

Existing component separation methods fail to consider the complex nonlinear relationship between dam effect quantities and environmental variables. In this study, a novel nonlinear component separation method for the effect quantities is proposed by combining kernel partial least squares (KPLS) and pseudosamples. By this method, a nonlinear monitoring model is established based on KPLS, and the complicated nonlinear relationship between the effect quantities and environmental variables can be determined accurately through the model. Furthermore, special pseudosamples are constructed to separate independent components and coupling influence components of environmental factors from the KPLS model. These methods have been applied into a super-high arch dam, and the separated displacement components conform to the general deformation law. The presented results indicate that it is more reliable than traditional multiple linear regression models.