Xiao Fu

Aiming at the gross error and data missing in the monitoring sequence of concrete dam, the variational mode decomposition method and the gated recurrent unit depth learning algorithm are respectively used to extract the effective information of the monitoring sequence. On the basis of the research on the characteristics of the traditional concrete dam structural behavior characterization model, the paper explores the expression mode of the effect of cold wave, freeze-thaw, wintering layer and other influencing factors. In order to reflect the correlation between the monitoring measurement values, the space coordinate variable is introduced to establish the monitoring measurement change characterization model, so as to realize the characterization and analysis of the structural behavior changes of concrete dams in cold regions and the quantitative analysis of various influencing factors. Based on the research in this article, we can fully understand the operation status of the dam, identify hidden dangers, and carry out relevant risk investigation and reinforcement. It can reduce the risk of dam failure to a certain extent.

Due to human activities, the evolution of the lower reaches of the Yangtze River is complex, and the underwater terrain near the docks is varied. There may be serious erosion and sedimentation of the bank slope, which will affect the stability and cause engineering accidents. To explore the changes in underwater terrain and their impacts, taking a dock in Jiangsu Province in China as an example, a multi-beam bathymetry system is used to conduct full coverage measurement of the underwater terrain at the front of the dock. A comprehensive analysis method for underwater terrain is proposed from the perspectives of contour lines, cross-sections, etc., in order to monitor the changes in erosion and sedimentation in the dock area. Then, the impact of underwater terrain changes on the bank slope stability is explored by simulating the bank slope soil and the concrete pile foundation. Targeted engineering measures to ensure the stability and safety of the dock and bank slope are proposed. The methods and conclusions proposed in this manuscript, including underwater terrain measurement, analysis of the impact of underwater terrain changes on stability, and recommendations for related engineering measures, will help to ensure the long-term safe operation of docks. This research will be of great significance in reducing water conservancy accidents and promoting sustainable and efficient development of water resources.

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.

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.

As an important feature, deformation analysis is of great significance to ensure the safety and stability of arch dam operation. In this paper, Jinping-I arch dam with a height of 305 m, which is the highest dam in the world, is taken as the research object. The deformation data representation method is analyzed, and the processing method of deformation spatiotemporal data is discussed. A deformation hybrid model is established, in which the hydraulic component is calculated by the finite element method, and other components are still calculated by the statistical model method. Since the relationship among the measuring points is not taken into account and the overall situation cannot be fully reflected in the hybrid model, a spatiotemporal hybrid model is proposed. The measured values and coordinates of all the typical points with pendulums of the arch dam are included in one spatiotemporal hybrid model, which is feasible, convenient, and accurate. The model can predict the deformation of any position on the arch dam. This is of great significance for real-time monitoring of deformation and stability of Jinping-I arch dam and ensuring its operation safety.

Real‐time monitoring of the actual elastic modulus is essential and necessary to ensure the safe operation of arch dams. The zoning elastic modulus of a high arch dam is inversed by using deformation safety monitoring data in the operation period, based on the particle swarm optimization with gravitation search algorithm for support vector machine (PSOGSA‐SVM) method. Firstly, the measured data of multipoints with a pendulum are separated to construct the initial sample training set; then, an optimal inversion model is established to reflect the complex nonlinear relationship between the mechanical parameters of the high arch dam and the deformation of measured points; finally, the PSOGSA‐SVM method is used to train and dynamically update the training set so as to realize the optimization solution of the inversion model. The proposed inversion method is successfully applied to a high arch dam in China to verify its feasibility and validity. The results show that the actual elastic modulus of the dam body is much larger than the initial elastic modulus, which is beneficial to structural stability.

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.