Common anomaly recognition methods are easy to misjudge and miss outliers for the online monitoring data. This is a bottleneck problem that needs to be overcome in dam safety management moving toward informatization. Based on the data of nine hydropower stations along Dadu River Basin, this paper analyzed existing problems of the common anomaly identification method and an algorithm was proposed based on improved M-robust regression recognition. In this algorithm, the AR factor was introduced to avoid the defect that the traditional model cannot simulate random variables. The extreme value method and robust estimation were utilized to avoid the leverage effect. The model collapse caused by maximum measured value was avoided through improving the residual calculation model of M-robust and optimizing the weight distribution function. The maximum of the three values, residual quartile difference, discrete quartile difference, and measurement accuracy, was used as an anomaly recognition criterion to improve the evaluation criteria. The algorithm compiled was used in the Dadu River Company since 2017. The statistics showed that for the 150,000 measured values per day, the evaluation time could be within 15 min, the missed judgment rate was 0%, and the misjudgment rate was less than 2%. The proposed algorithm achieved a great improvement and can meet the needs of online outlier recognition in dam safety management.

Crest cracking is one of the most common damage types for high earth-rockfill dams. Cracking risk of dam crest is closely related to the duration of abnormal deformation state. In this paper, a methodology for dynamic risk evaluation and early warning of crest cracking for high earth-rockfill dams is proposed and mainly consists of: (a) The discrimination of abnormal deformation state related to crest cracking, which is implemented by comparing the crest settlement inclination with the threshold value. (b) Computation of crest cracking probability and estimation of cracking time. The exponential distribution is adopted to represent the probability distribution of the duration T_AS of abnormal state before crest cracking. Then the crest cracking probability in a given time can be computed by integration with respect to T_AS. Inversely, the cracking time corresponding to a given probability can be estimated. (c) Determination of the values of probability adopted to early warn crest cracking, which are suggested to be selected by statistical analysis of the calculated probabilities at the observed cracking times. (d) Bayesian estimation and updating of probability distribution of the parameter λ in the PDF of T_AS, according to observed durations of abnormal state before crest cracking. The methodology is illustrated and verified by the case study for an actual earth-rockfill dam, of which crest cracking and recracking events were observed during the periods of high reservoir level. According to the observed values of T_AS, the probability distribution for λ is progressively updated and the dispersion of the distributions of λ gradually decreases. The crest cracking probability increases with the duration of abnormal state and the width of confidence interval of the estimated cracking probability progressively contracts with the updating of the distribution for λ. Finally, the early warning of crest cracking for the dam is investigated by estimating the lower limit of cracking time. It is shown that early warning of crest cracking can be issued from at least 20 days ahead of the occurrence of crest cracking event. The idea of using duration of abnormal state of crest settlement to evaluate crest cracking risk of the earth-rockfill dam in this paper may be applicable to other dams.

To overcome the limitations of rigid body limit equilibrium methods in earth rock dam slope stability analysis, this study develops a system reliability framework using the finite element strength reduction method (FEM-SRM). An elastoplastic finite element model simulates dam construction and impoundment, identifying potential slip pathways. Each pathway, treated as a parallel system of shear-failed elements, is analyzed via the response surface method to derive explicit limit state functions. Reliability indices are computed using an improved first-order second-moment method, while interdependencies are assessed through stepwise equivalent linearization. System reliability is determined using Ditlevsen’s narrow bound method. Applied to a 314 m earth rockfill dam, three critical slip pathways were identified: upstream shallow (reliability index is 6.94), upstream deep (reliability index is 6.87), and downstream deep (reliability index is 7.44), with correlation coefficients between 0.26 and 0.89. The system reliability index (6.81) significantly exceeds the code target (4.2), highlighting the method’s ability to integrate material randomness, stress-strain nonlinearity, and multi-slip interactions. This framework provides a robust probabilistic approach for high earth rock dam stability assessment, enhancing engineering safety evaluations.

The stability of reservoir bank slopes is critical to the engineering operation’s safety. Due to the complexity of geological conditions, the monitoring mode based on deformation monitoring data cannot directly respond to the structural damage stability state, whereas anther mode based on structural calculation is time-consuming and lacks real-time capabilities. To that end, this paper proposes a method for fast prediction of the safety state of reservoir bank rock slope based on the physical significance of time-dependent deformation and rock creep at monitoring points, with the safety coefficient and reliability obtained by numerical calculation as the dependent variables and the slope deformation monitoring sequence as the independent variable, based on full verification of the rationality of numerical calculation. The model can be used to forecast the stability and reliability coefficients of reservoir bank slopes online using deformation data from the field. The application verification of the left bank slope of the Dagangshan arch dam reveals that the average and maximum error of slope stability safety coefficient prediction is within 5% for 90 and 180 days and the average and maximum error of reliability index prediction is within 10%, which meet the engineering requirements and can provide a new way for rapid prediction of slope engineering safety.

The hydropower project had comprehensively benefited people from aspects of the economy, society, ecology, etc. The comprehensive benefit is a key indicator for evaluating a project’s performance. However, the existing studies only evaluated the comprehensive benefit and ignored the relationship among different benefits, which is of great significance for the sustainable development of a project. Therefore, in the framework of the complex system composed of economic, social, and ecological benefit subsystems, a synergy degree evaluation method is constructed based on the evaluation index system of the comprehensive benefits, and the compound weight is determined by using the non-linear model and the Lagrange function. Thus, the changing rules of the order degree and the synergy degree for the subsystems in different years can be obtained. The proposed method is applied to a gate dam (named SG) to appraise the relationship among benefits. The results show that the economic, social, and ecological benefits of the SG dam from 2011 to 2018 are gradually to be a better state, but the synergy degrees of the complex system belong to “bottom synergy” and “moderate synergy” level, which indicates that there is no close cooperation among the three benefit subsystems.