Human factors are introduced into the dam risk analysis method to improve the existing dam risk management theory. This study constructs the path of human factor failure in dam collapse, explores the failure pattern of each node, and obtains the performance shaping factors (PSFs) therein. The resulting model was combined with a Bayesian network, and sensitivity analysis was performed using entropy reduction. The study obtained a human factor failure pathway consisting of four components: monitoring and awareness, state diagnosis, plan formulation and operation execution. Additionally, a PSFs set contains five factors: operator, technology, organization, environment, and task. Operator factors in a BN (Bayesian network) are the most sensitive, while the deeper causes are failures in organizational and managerial factors. The results show that the model can depict the relationship between the factors, explicitly measure the failure probability quantitatively, and identify the causes of high impact for risk control. Governments should improve the significance of the human factor in the dam project, constantly strengthen the safety culture of the organization’s communications, and enhance the psychological quality and professional skills of management personnel through training. This study provides valuable guidelines for the human reliability analysis on dam failure, which has implications for the theoretical research and engineering practice of reservoir dam safety and management.

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 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.

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.

Soil–rock binary structure slopes are a unique and common phenomenon in water conservancy and geotechnical engineering, with complex stability issues that challenge traditional evaluation methods. This paper proposes the Partitioned Rigid Element–Interface Element–Finite Element (PRE-IE-FE) method, a novel numerical technique that addresses computational efficiency and accuracy in slope stability analysis. The method integrates composite destabilization modes along the soil interior, soil–rock contact surface, and rock structural surfaces, ensuring high computational performance. A slope instability criterion for PRE-IE-FE is established through derived control equations, a developed calculation program, and the strength reduction method. Case studies validate the method’s effectiveness and practicality, offering innovative solutions for similar engineering challenges.