Taking coal under hydro-mechanical coupling as the research object, the discrete element software PFC3D (particle flow code) was used to analyze the relationships among the force, acoustic emission (AE), and energy during coal fracture. Based on the moment tensor (MT) inversion, we revealed the AE event distribution and source type during crack initiation and propagation until the final failure of coal. Meanwhile, we examined the relationships among the stress, number and type of cracks, magnitude, K_E, and b value of AE under different water and confining pressures. The results show that the numerical simulation can effectively determine the microscopic damage mechanism of coal under different conditions. Moreover, the rupture type of the numerical simulation is consistent with the field investigations, which verifies the rationality of the simulation. These research results can provide reference for safety production evaluation of water inrush mines.

Accurately predicting the uniaxial compressive strength of ultra-high-performance concrete is vital for optimizing material design and ensuring cost-effective construction. However, existing predictive models often fail to capture the nonlinear relationships inherent in complex UHPC datasets and suffer from extended training times, thereby limiting their accuracy and practical utility. From the perspective of artificial intelligence, this study proposes an enhanced predictive framework that integrates a kernel extreme learning machine with a globally strengthened whale optimization algorithm. The proposed framework introduces three novel mechanisms—adaptive inertia weight, variable helix search, and optimal neighborhood perturbation—designed to improve the algorithm’s global search capability, convergence speed, and solution stability. From the engineering application perspective, the proposed model is applied to three UHPC datasets to predict uniaxial compressive strength. Prior to modeling, feature normalization is employed to reduce the impact of dimensional inconsistency among input variables. Experimental results demonstrate the model’s superior predictive precision, achieving $$R^2$$ R 2 values of 0.8823, 0.8906, and 0.8951 across the datasets. These findings confirm that the integration of advanced AI optimization techniques significantly enhances the performance of predictive models in civil engineering, offering a reliable and efficient approach for ultra-high-performance concrete strength estimation in design and construction workflows.