Explore the vast range of titles available.

The study focuses on the kinetic process of landslides, including associated travel trajectory and endangered area. A three-dimensional distance potential discrete element method for the movement process of landslides is developed in this work. Benefited from a new definition of the distance potential function with a clear physical meaning, a holonomic system of the calculation algorithm for the contact interaction is established, accounting for the influence of the tangential contact force. Performance of the new approach is validated with well-known benchmarks, which consist of a block impact simulation, a sliding/toppling test of a joint rock slope, and block response to induced acceleration in the foundation. The results are in good agreement with the analytical solutions, showing an ability of the newly method in capturing the microscopic mechanical behaviors and macro-motion characteristics of individual blocks. After the verification of the overall performance by three cases, the proposed method is applied to simulate the Tangjiashan Landslide trigged by the Wenchuan Earthquake in 2008.

The work is devoted to a coupling method for the finite element method (FEM) and the distance potential discrete element method. In this work, a well-defined distance potential function is developed. Meanwhile, a holonomic and precise algorithm for contact interaction is established, accounting for the influence of the tangential contact force. In addition, the measurement of deformation behaviors of each discrete element is handled by the FEM, where the coupling model and the conversion method of the equivalent nodal force accounting for the influence of contact forces are proposed to generate the corresponding equations of motion. Finally, the velocity verlet algorithm is applied enabling the significant simplification for the calculation of the equations of motion. The proposed approach provides an accurate contact interaction avoiding the influence of the element shape and reflect the movement procedure of multiple deformable bodies precisely. This viewpoint is proved by the classical benchmark cases.

This study presents a dynamic inversion method for the concrete gravity dam on a soft rock foundation, aiming to accurately characterize the time-dependent trend of the dam’s mechanical properties. Conventional static inversion methods often overlook temporal variations in material behavior, particularly the long-term weakening of soft rock foundations under environmental influences. To address this limitation, an improved particle swarm optimization (PSO) algorithm is developed for dynamic parameter inversion, combining real-time monitoring data with finite element modeling to evaluate the time-varying elastic modulus of the foundation. The results reveal an exponential decay in the foundation’s elastic modulus (from 4.67 GPa to approximately 3.83 GPa), while the dam body maintains a stable modulus of 20.74 GPa. Comparative analyses demonstrate that the dynamic inversion approach, which accounts for time-dependent parameter degradation, significantly improves the displacement prediction accuracy of the dam. The results highlight the critical importance of incorporating temporal mechanical property variations in inversion analyses to ensure reliable structural assessments and enhance long-term dam safety management.

In this work, a two-layer depth-integrated smoothed particle hydrodynamics (SPH) model is applied to investigate the effects of landslide propagation on the impulsive waves generated when entering a water body. In order to deal with the open boundary in practical engineering problems, an absorbing boundary method, based on Riemann invariants which can be applied to arbitrary geometries, is implemented. In order to examine the accuracy of the proposed formulation, the model is tested against both available laboratory tests and numerical examples from the literature. Then, it is adopted to model the characteristics of the impulse waves generated by the Halaowo landslide in the Jinsha River, China. The results provide a technical basis for the emergency plan to the Halaowo landslide and benefit the disaster prevention policy, which helps mitigating future hazards in similar reservoir areas.

In the context of dam deformation monitoring, the prediction task is essentially a time series prediction problem that involves non-stationarity and complex influencing factors. To enhance the accuracy of predictions and address the challenges posed by high randomness and parameter selection in LSTM models, a novel approach called sparrow search algorithm–long short-term memory (SSA–LSTM) has been proposed for predicting the deformation of concrete dams. SSA–LSTM combines the SSA optimization algorithm with LSTM to automatically optimize the model’s parameters, thereby enhancing the prediction performance. Firstly, a concrete dam was used as an example to preprocess the historical monitoring data by cleaning, normalizing, and denoising, and due to the specificity of the data structure, multi-level denoising of abnormal data was performed. Second, some of the data were used to train the model, and the hyperparameters of the long and short-term memory neural network model (LSTM) were optimized by the SSA algorithm to better match the input data with the network structure. Finally, high-precision prediction of concrete dam deformation was carried out. The proposed model in this study significantly improves the prediction accuracy in dam deformation forecasting and demonstrates effectiveness in long-term time series deformation prediction. The model provides a reliable and efficient approach for evaluating the long-term stability of dam structures, offering valuable insights for engineering practices and decision-making.

The stability analysis of rock slopes has been a prominent topic in the field of rock mechanics, primarily due to the widespread occurrence of discontinuous structural planes in rock masses. Based on this complex characteristic of rock slopes, this paper proposes a novel numerical method, the Partitioned-Rigid-Element and Interface-Element (PRE-IE) method. In the PRE-IE method, the structure is modeled as several rigid bodies and discontinuous structural planes, which are, respectively, divided into partitioned rigid elements and interface elements. Taking the contact force of node pairs and the displacement of the rigid body centroid as mixed variables, according to the principle of minimum potential energy, the governing equations of PRE-IE can be established using the Lagrange multiplier method and then solved using the nonlinear contact iterative method and the incremental method. A classic case study demonstrates that using the failure of all contact node pairs as the criterion for slope failure is appropriate. This criterion is objective and avoids the potential impact of personal bias on safety factor calculations. Two numerical examples of differently shaped slopes are provided to verify the correctness and validity of the PRE-IE method. By comparing the safety factor calculated using the PRE-IE method with those obtained from other different methods, as well as comparing the computational time, it is shown that the PRE-IE method, in combination with the SRM, can accurately and efficiently analyze the stability problems of rock slopes.

This paper addresses the challenge of systemic extreme risk in long-service gravity dams under human-controlled operation. It is the first study to construct a Generalized Extreme Value (GEV) distribution model using long-term operational monitoring data. The model, validated by multiple statistical tests and engineering boundary conditions, is then applied within a Response Surface Method-Monte Carlo (RSM-MC) reliability framework. Results indicate that the historical GEV model accurately captures the high-water-level tail characteristics and significantly overcomes the risk underestimation inherent in the uniform distribution model. Compared to the Log-Pearson Type III (Log-P3) design condition model, the GEV model yields a significantly lower probability of failure, e.g., the probability of cracking at the dam heel, the most sensitive failure mode, is reduced by nearly six times. This quantitative difference fully demonstrates GEV’s ability to precisely quantify the effective risk reduction achieved by human control, establishing a more scientific and realistic foundation for risk assessment of long-service gravity dams.

A deformable spheropolygon-based discrete element method is developed to predict the evolution of fracture by coupling the finite element method (FEM) and the spheropolygon-based discrete element method (DEM). Within the framework of the coupling method, the spheropolygon-based DEM is adopted to capture the discontinuum behaviors, while the continuum behaviors are analyzed by the FEM. By introducing the fracture model of joint elements based on fracture mechanics, a continuous-discontinuous coupling approach for simulating the fracture of quasi-brittle materials is presented. The tensile failure is described with the fictitious crack model, meanwhile, the Mohr–Coulomb failure criterion with a tension cut-off is employed to determine the shear failure state. Finally, the results of numerical simulations indicate that this novel method is versatile in simulating the whole process of quasi-brittle materials from continuum to discontinuum, including the initiation and propagation of cracks, and the collision of fragments after the failure of brittle materials.Graphic abstract

Aiming to investigate the problem that dam-monitoring data are difficult to analyze in a timely and accurate automated manner, in this paper, we propose an automated framework for dam health monitoring based on data microservices. The framework consists of structural components, monitoring sensors, and a digital virtual model, which is a hybrid of a finite element (FE) model, a geometric model, a mathematical model, and a deep learning algorithm. Long short-term memory (LSTM) was employed to accurately fit and predict the monitoring data, while dynamic inversion and simulation were used to calibrate and update the data in the hybrid model. The automated tool enables systematic maintenance and management, minimizing errors that are commonly associated with manual visual inspections of structures. The effectiveness of the framework was successfully validated in the safety monitoring and management of a practical dam project, in which the hybrid model improved the prediction accuracy of monitored data, with a maximum absolute error of 0.35 mm. The proposed method can be considered user-friendly and cost-effective, which improves the operational and maintenance efficiency of the project with practical significance.

The solution using built-in beam element method to increase the calculation efficiency of aqueduct pile foundations is studied in this work. The formulations of built-in beam element method based on the normal generalized displacement method of beam element are introduced in this work. Then, the validity of the built-in beam element method is checked by a static problem of a simplified aqueduct model. Furthermore, the solution to increase calculation efficiency by built-in beam element method for aqueduct pile foundations is introduced by studying the features of built-in beam element method and normal generalized displacement method of beam element in models with different grid density and foundation stiffness. Finally, a static problem of an arch aqueduct is solved using the summed solution. The results show that it is feasible to improve efficiency with sufficient accuracy by reasonably selecting the built-in beam element method and normal generalized displacement method in the analysis of aqueduct pile foundation.