A large proportion of the dams in China are earth-rock dams. Regarding the well-studied loss of life and economic consequences due to dam breaks, this paper introduces the causes and modes of earth-rock dam breaks and the corresponding dam-break losses in terms of the social and environmental aspects. This study formulates the evaluation index system and criteria of earth-rock dam breaks’ impact on society and the environment based on a fuzzy comprehensive evaluation method. The results show that the evaluation grade of the social and environmental impact of the dam break of the Liujiatai Reservoir was “serious”. Therefore, similar dams in China should take corresponding measures in advance to reduce the social and environmental impact of earth-rock dam breaks.

We present a novel deformation prediction model for super-high arch dams based on the prototype monitoring displacement field. The noise reduction processing of the monitoring data is conducted by a wavelet technique. The performance-improved random forest intelligent regression approach is then established for constructing the arch dam deformation statistical models, whose hyper-parameters are intelligently optimized in terms of the improved salp swarm algorithm. In total, three enhancement strategies are developed into the standard salp swarm algorithm to improve the global searching ability and the phenomenon of convergence precocious, including the elite opposition-based learning strategy, the difference strategy, and the Gaussian mutation strategy. A prediction example for super-high arch dams is presented to confirm the feasibility and applicability of the prediction model based on five evaluation criteria. The prediction results show that the proposed model is superior to other standard models, and exhibits high-prediction accuracy and excellent generalization performance. The stability of the proposed prediction model is investigated by artificially introducing noise strategies, which demonstrates the high-robust prediction features and provides a promising tool for predicting carbon emissions, epidemics, and so forth.

A dam deformation prediction model based on adaptive weighted least squares support vector machines (AWLSSVM) coupled with modified Ant Lion Optimization (ALO) is proposed, which can be utilized to evaluate the operational states of concrete dams. First, the Ant Lion Optimizer, a novel metaheuristic algorithm, is used to determine the punishment factor and kernel width in the least squares support vector machine (LSSVM) model, which simulates the hunting process of antlions in nature. Second, aiming to solve the premature convergence phenomenon, Levy flight is introduced into the ALO to improve the global optimization ability. Third, according to the statistical characteristics of the datum error, an improved normal distribution weighting rule is applied to update the weighted value of data samples based on the learning result of the LSSVM model. Moreover, taking a concrete arch dam in China as an example, the horizontal displacement recorded by a pendulum is used as a study object. The accuracy and validity of the proposed model are verified and evaluated based on the four evaluating criteria, and the results of the proposed model are compared with those of well-established models. The simulation results demonstrate that the proposed model outperforms other models and effectively overcomes the influence of outliers on the performance of the model. It also has high prediction accuracy, produces excellent generalization performance, and can be a promising alternative technique for the analysis and prediction of dam deformation and other fields, including flood interval prediction, the stock price market, and wind speed forecasting.

A new approach was developed for the inversion modeling of dam‐zoning elasticity modulus for heightened concrete dam, with old and new concrete zones. The proposed inversion modeling procedure takes advantage of the improved cuckoo search (ICS) algorithm and improved particle swarm optimization (IPSO) algorithm to adjust the mechanical parameters, which are used as input. An objective function is constructed based on the horizontal displacement increment by using the finite element method (FEM) and statistical analysis of the prototype monitoring data. One ideal arch dam model and one actual heightened concrete dam were taken as examples. The proposed method was used to implement the optimal selection of the dam‐zoning elasticity modulus. The inversion analysis results indicate that the mechanical parameters identification method for heightened concrete gravity dams proposed in this article is accurate and has a fast convergence rate. Consequently, it can be applied as a reliable model to identify the dam‐zoning elasticity modulus in practical engineering applications.

The district handover is one of the key technologies in the high-speed maglev operation control system. It completes the transfer of safety responsibilities when a train passes across two districts, and it directly affects the safety and stability regarding train operation. This article first makes a general analysis of its mechanism, including the essential characteristics, stakeholders and tasks of the district handover. In order to simplify the complexity of the behavior models, the whole procedure is divided into four phases, namely, the preparation phase, the stopping point stepping phase, the vehicle reception phase and the termination phase. The information interaction and timing relationship of these models are presented by UML.