With massive energy demands, the majority of developing countries are at a critical juncture in their industrial development. Their energy structure, on the other hand, is relatively specific and heavily reliant on fossil fuels, resulting in significant environmental pollution. As a result, the development of clean energy is on the horizon, which is related not only to whether developing countries can build a resource-saving and environmentally friendly society but also to whether they can achieve socially sustainable development. As a significant clean energy source, not only does hydropower play an important role in the development of an energy-efficient and environmentally friendly green economy, but it also has numerous benefits such as shipping, irrigation, flood control, and water supply. So, hydropower development is critical for developing countries to adjust their energy structures, achieve regional development balance, and ensure river defense safety. However, precision guidance technology is maturing around the world. If one side’s water-retaining dam is accurately blasted in the event of a full-scale war or local conflict, it may cause significant economic and human losses. Dam safety and protection from strong explosions deserve special attention given the obvious seriousness of the consequences. It is critical to improve the anti-explosion safety of major hydraulic structures by revealing the dynamic response behavior, damage mechanism, and dam characteristics under explosion impact loads, as well as evaluating the dam’s condition after extreme loads. In the critical work of disaster prevention and mitigation, this is crucial to our social and economic development. This study is not only a key technical problem and an important strategic task in hydraulic structure construction, but it may also serve as a guideline for governments to take effective measures to reduce the loss of dam break under special circumstances.

Rainfall infiltration plays a critical role in controlling pore pressure evolution and triggering instability in natural slopes composed of heterogeneous soil–rock deposits. A probabilistic framework combining interval approximation reformulation with adaptive Kriging-assisted Monte Carlo simulation is developed to assess rainfall infiltration-induced instability in deposit slopes. The adaptive Kriging is employed using a U-learning enrichment criterion to surrogate the limit-state response. The approach avoids the dimensional limitations associated with kernel density estimation in high-dimensional probability spaces. The model is applied to the Dahua deposit landslide located along the Lancang River in Yunnan Province, China. Five gradient rainfall scenarios are considered, and the surrogate framework is calibrated against a physical model test. The results show that compared with conventional Monte Carlo approaches, the proposed framework significantly reduces computational cost while preserving accuracy in probabilistic predictions. The interval approximative adaptive Kriging based on Monte Carlo method reproduces the experimental displacement response with a mean relative error of approximately 0.05%, where the simple interval adaptive method based on Monte Carlo method produces an error close to 3%. The computational time is reduced from nearly 18 h to approximately 2 h under single-thread execution on a standard processor. The probabilistic analysis further shows that the maximum failure probability increases monotonically with cumulative infiltration depth as the rainfall return period increases from 25 to 100 years. The study provides insights into uncertainty propagation in rainfall-driven fluid–solid systems and offers a practical tool for probabilistic assessment of infiltration slope instability.