As an inherent property of the accumulation of elastic energy and the sudden instability failure of coal, coal bursting liability (CBL) is the basis of the research on the early warning and prevention of coal burst. To accurately classify the CBL level, the support-vector-machine (SVM) method was introduced in this paper, and the dynamic failure time (DT), elastic energy index (W_ET), impact energy index (K_E) and uniaxial compressive strength (R_C) were selected as the classification indexes. An imbalanced sample set, containing 95 groups of measured data of CBL, was established, and eight SVM classification models were constructed, based on different kernel functions and swarm-intelligence-optimization algorithms. Focusing on the problem of sample imbalance, the classification accuracy, A, F1-score and kappa coefficient were used to comprehensively evaluate the classification performance of SVM models, and the grey-wolf-optimizer SVM (GWO-SVM) model was selected as the best model in this paper, reaching the highest accuracy of 98.9%. The GWO-SVM was applied to identify the CBL level of the 4# coal seam in Xiaozhuang Coal Mine and the 1# coal seam in the Wanfeng Coal Mine. The results of the engineering application are consistent with those from the engineering field, and show that the proposed model is scientific and practical, and can be a new method for CBL classification.

Evaluating slope failure is a primary concern in geotechnical engineering, and employing advanced machine learning techniques to design Factor of Safety (FOS) has become a critical focus. This study introduces a method that integrates Principal Component Analysis (PCA) with Back Propagation Neural Networks (BPNN) to predict the FOS. Compared to existing machine learning design approaches, the PCA-BPNN method demonstrates superior accuracy, achieving an R² of 0.917, RMSE of 0.061, and MAE of 0.047 for the training set, and an R² of 0.879, RMSE of 0.071, and MAE of 0.057 for the testing set. This method is applied to assess the slope stability of the H mining area in Guangdong, China, resulting in a designed FOS of 1.409, which meets practical engineering requirements. The findings highlight the effectiveness of the PCA-BPNN method in enhancing slope stability assessments in geotechnical applications.

Rockbursts are serious threats to the safe production of mining, resulting in great casualties and property losses. The accurate prediction of rockburst is an important premise that influences the safety and health of miners. As a classical machine learning algorithm, the back propagation (BP) neural network has been widely used in rockburst prediction. However, there are few reports about the influence study of different training sample sizes, optimization algorithms and index dimensionless methods on the prediction accuracy of BP neural network models. Therefore, 100 groups of typical rockburst engineering samples were collected locally and abroad, and considering the relevance, scientificity and quantifiability of the prediction indexes, the ratio of the maximum tangential stress of surrounding rock to the rock uniaxial compressive strength (σ_θ/σ_c), the ratio of the rock uniaxial compressive strength to the rock uniaxial tensile strength (σ_c/σ_t) and the elastic energy index (W_et) were chosen as the prediction indexes. When the number of samples was 40, 70 and 100, sixty improved BP models were established based on the standard gradient descent algorithm and four optimization algorithms (momentum gradient descent algorithm, quasi-Newton algorithm, conjugate gradient algorithm, Levenberg–Marquardt algorithm) and four index dimensionless methods (unified extreme value processing method, differentiated extreme value processing method, data averaging processing method, normalized processing method). The prediction performances of each improved model were compared with those of standard BP models. The comparative study results indicate that the sample size, optimization algorithm and dimensionless method have different effects on the prediction accuracy of BP models, which are described as follows: (1) The prediction accuracy value A of the BP model increases with the addition of sample size. The average value A_ave of twenty improved models under three kinds of sample sizes increases from A_ave (40) = 69.7% to A_ave (100) = 75.3%, with a maximal value A_max from A_max (40) = 85.0% to A_max (100) = 97.0%. (2) The value A and comprehensive accuracy value C of the BP model based on four optimization algorithms are generally higher than those of the standard BP model. (3) The improved BP model based on the unified extreme value processing method combined with the Levenberg–Marquardt algorithm has the highest value A_max (100) = 97.0% and value C = 194, and the prediction results of five engineering cases are completely consistent with the actual situation at the site, so this is the best BP neural network model selected in this paper.

The classification of coal bursting liability (CBL) is essential for the mitigation and management of coal bursts in mining operations. This study establishes an index system for CBL classification, incorporating dynamic fracture duration (DT), elastic strain energy index (W_ET), bursting energy index (K_E), and uniaxial compressive strength (R_C). Utilizing a dataset comprising 127 CBL measurement groups, the impacts of various optimization algorithms were assessed, and two prominent machine learning techniques, namely the back propagation neural network (BPNN) and the support vector machine (SVM), were employed to develop twelve distinct models. The models’ efficacy was evaluated based on accuracy, F1-score, Kappa coefficient, and sensitivity analysis. Among these, the Levenberg–Marquardt back propagation neural network (LM-BPNN) model was identified as superior, achieving an accuracy of 96.85%, F1-score of 0.9113, and Kappa coefficient of 0.9417. Further validation in Wudong Coal Mine and Yvwu Coal Industry confirmed the model, achieving 100% accuracy. These findings underscore the LM-BPNN model’s potential as a viable tool for enhancing coal burst prevention strategies in coal mining sectors.

Focusing on coarse-grained soil slopes along the Sejila Mountain section of the G318 highway in China, this study adopts the environmentally friendly polymer polyvinyl alcohol (PVA) as an improvement material. Through a combination of laboratory tests, field tests, and microscopic analyses, the improvement patterns, reinforcement mechanisms, and ecological adaptability of PVA-modified coarse-grained soils are systematically investigated. A natural soil control group and five PVA mix-ratio groups (4%, 6%, 8%, 10%, and 12% by weight) were designed. The results show that PVA can significantly increase soil cohesion and internal friction angle, optimize the stress–strain relationship, and inhibit dilatancy, with 8% determined as the optimal content. PVA forms a continuous reticulate cementation structure among soil particles, effectively filling pores, strengthening interparticle bonding, and enhancing the integrity and stability of the soil. However, excessive PVA may wrap soil particles and weaken frictional interlocking, resulting in strength reduction. Indoor and field ecological planting tests (using highland barley and alfalfa) indicate that an appropriate amount of PVA can increase seed germination rate and plant height, while soil pH and nutrient contents remain stable, showing no ecological inhibition. Thus, the modified soil meets the requirements of slope ecological restoration.

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.