The development height of the water-conducting fracture zone (WCFZ) is crucial for the safe production of coal mines. The back-propagation neural network (BP-NN) can be utilized to forecast the WCFZ height, aiding coal mines in water hazard prevention and control efforts. However, the stochastic generation of initial weights and thresholds in BP-NN usually leads to local optima, which might reduce the prediction accuracy. This study thus invokes the excellent global optimization capability of the Improved Radial Movement Optimization (IRMO) algorithm to optimize BP-NN. The influences of mining thickness, coal seam depth, working width, and hard rock lithology proportion coefficient on the height of WCFZ are investigated through 75 groups of in situ data of WCFZ heights measured in the Jurassic coalfield of the Ordos Basin. Consequently, an IRMO-BP-NN model for predicting WCFZ height in the Jurassic coalfield of the Ordos Basin was constructed. The proposed IRMO-BP-NN model was validated through monitoring data from the 4⁻²216 working faces of Jianbei Coal Mine, followed by a comparative analysis with empirical formulas and conventional BP-NN models. The relative error of the IRMO-BP-NN prediction model is 4.93%, outperforming both the BP-NN prediction model, the SVR prediction model, and empirical formulas. The results demonstrate that the IRMO-BP-NN model enhances the accuracy of predicting WCFZ height, providing an application foundation for predicting such heights in the Jurassic coalfield of the Ordos Basin and protecting the ecological environment of Ordos Basin mining areas.

The stability of abandoned open-pit mine slopes and their ecological environment are threatened owing to their fragile, complicated, and uncertain characteristics. This study establishes a novel evaluation indicator system for enhancing mine design and environmental protection insight. The weights in the system are assigned using a combined method, which consists of the game theory, the interval analytic hierarchy process (IAHP), and the entropy weight method (EWM). The IAHP is optimized by the improved radial movement optimal (IRMO) algorithm and the simulated annealing (SA) algorithm to ensure calculation stability and efficiency. Meanwhile, a two-dimensional cloud model (TDCM) is developed to obtain the slope resilience level and visualize the result. This comprehensive evaluation method is applied to three abandoned mine slopes in the Yellow River Basin, and the results demonstrate that the method can provide crucial insights for rational mine slope stabilization and ecological restoration.

External water level fluctuation is the major trigger causing reservoir slope failure, and therefore it is of great significance for the safety assessment and corresponding safety management of reservoir slopes. In this work, the seepage effects stemming from fluctuating external water levels are given special analysis and then incorporated into the rigorous limit equilibrium method for assessing the stability of reservoir slope. An advanced metaheuristic intelligent algorithm, the improved radial movement optimisation (IRMO), is introduced to efficiently locate the critical failure surface and associated minimum factor of safety. Consequently, the effect of water level fluctuation directions, changing rates, and soil permeability coefficient on reservoir stability are investigated by the proposed method in three cases. It is found that the clay slope behaved more sensitively in stability fluctuation compared to the silty slope. With the dropping of external water, the higher dropping speed and lower soil permeability coefficient have worse impacts on the slope stability. The critical pool level during reservoir water dropping could be effectively obtained through the analysis. The results indicate that the IRMO-based method herein could effectively realise the stability analysis of the reservoir slope in a dynamic fluctuating reservoir water level, which could provide applicable technology for following preventions.

Fragile ancient buildings are recognized as an eloquent testimony to human civilization, and their safety should arouse more attention. According to the special case of adjacent blasting construction, the assessment model should be essentially built to assess the effect of tunnel blasting on the safety of the ancient buildings. To analyze the structural safety of ancient buildings under blasting vibration and to protect the precious ancient buildings, a risk assessment model of ancient buildings with 20 relevant assessment indexes was initiatively built in this study. To be specific, the relative factors of blasting, the factors of ancient buildings, and other factors (e.g., religion) were comprehensively considered in the model. Subsequently, the risk level and weight were calculated more systematically and quantitatively by adopting the optimized optimal comprehensive method integrating the G1 method and the entropy method. Lastly, the overall risk value was determined by applying the fuzzy gray method. Afterward, the value was adopted to assess the safety of the Asoka Temple, the only existing temple named after the Indian King Asoka in China, as an attempt to verify the feasibility of this model. Besides, the Asoka tunnel was around it. As demonstrated from the results, the age of the buildings maximally impacted the safety of ancient buildings, and the safety level of the Asoka Temple was “relatively safe.” The present study built an effective model to assess the safety of ancient buildings under adjacent subway blasting construction, which could help improve the efficiency and accuracy of assessments.

Considering the change of different soil layer parameters of a two–layered strip foundation, a planar kinematically permissible multi–block failure mechanism of a two–layered strip foundation under vertical uniform load is formulated. Based on upper–bound limit analysis theorem, the solution process of ultimate bearing capacity of a two–layered strip foundation is proposed. The improved radial movement optimization (IRMO) could search the critical slip surface of the foundation and calculate associated ultimate bearing capacity. On this basis, analyze the influence parameters. The results show that the IRMO algorithm is feasible, stable, and efficient in solving the ultimate bearing capacity. With the increase in the internal friction angle and cohesion of the two–layered strip foundation, the critical slip surface will expand more deeply, and the ultimate bearing capacity will increase. The influence of upper soil parameters on the calculation results is greater than that of lower soil. For the case with upper–hard soil, the ultimate bearing capacity of it increases gradually with the increase in the H/B ratio. For the upper–soft case, the ultimate bearing capacity of it decreases gradually with the increase in the H/B ratio. Moreover, the increase of ground overload will also cause a linear–increasing in the ultimate bearing capacity.

The prediction of the ultimate bearing capacity (UBC) of composite foundations represents a critical application of test monitoring data within the field of intelligent geotechnical engineering. This paper introduces an effective combinational prediction algorithm, namely SA-IRMO-BP. By integrating the Improved Radial Movement Optimization (IRMO) algorithm with the simulated annealing (SA) algorithm, we develop a meta-heuristic optimization algorithm (SA-IRMO) to optimize the built-in weights and thresholds of backpropagation neural networks (BPNN). Leveraging this integrated prediction algorithm, we forecast the UBC of soil–cement mixed (SCM) pile composite foundations, yielding the following performance metrics: RMSE = 3.4626, MAE = 2.2712, R = 0.9978, VAF = 99.4339. These metrics substantiate the superior predictive performance of the proposed model. Furthermore, we utilize two distinct datasets to validate the generalizability of the prediction model presented herein, which carries significant implications for the safety and stability of civil engineering projects.