To address the challenge of accurately predicting the fatigue crack initiation life caused by surface pitting corrosion in buried thermal pipelines during design, operation, and maintenance, this study employed the finite element method to investigate the influence of pit morphology, defect interaction, and axial loading on the maximum stress concentration factor K_t. An empirical formula for K_t was proposed, and a prediction method for fatigue crack initiation life under soil corrosion was developed. A pipeline in service in Beijing was used as a case study to verify the scientificity and effectiveness of this method. The results indicate that a 10-fold increase in pit depth leads to a 2.84-fold increase in K_t, while a 10-fold reduction in pit circumferential width results in a 4.75-fold increase. Deep and narrow defects characterized by a/c > 0.6 and b/c < 0.6 exert a stronger effect on increasing K_t and significantly shorten crack initiation life. When the defect spacing d=0, K_t reaches 1.03 times that of a single defect. The smaller the defect spacing, the stronger the interaction effects and the lower the crack initiation life. As defect spacing decreases, the fatigue crack initiation life of deep narrow pits is reduced to 0.12 times the original life, whereas shallow wide pits are more sensitive to spacing, with their crack initiation life reduced to 0.83 times the original life. Under conditions of low soil resistivity, low pH, and elevated temperature, crack initiation may occur within 20 years. The crack initiation life of shallow wide pits is more sensitive to soil parameters. Compared with internal pressure loading alone, an axial compressive load of 20 MPa significantly reduces crack initiation life by 0.74 times. Increasing axial tensile load from 20 MPa to 50 MPa results in a further life reduction of 0.82 times.

To address the challenges in accurately describing uncertainty and multiple failure modes in the risk assessment of embankment slope failures in goaf areas, a risk assessment model based on an improved T-S fuzzy fault tree approach was proposed. Initially, Gaussian fuzzy numbers were introduced to characterize the failure states and occurrence probabilities of basic events, thereby addressing the over-reliance on precise probabilistic data in conventional fault tree analysis and the insufficient representation of intermediate event states. Subsequently, T-S fuzzy model was employed to replace traditional AND/OR relationships in logic gates, capturing the uncertainty and fuzzy characteristics among events, and thereby deriving the failure probability of slope collapse. Finally, an engineering case study was conducted for validation. The results demonstrate that the proposed approach simplifies the fault tree construction, identifies the key risk factors leading to slope failure, provides a ranking of their influence, and reveal the intrinsic relationships between failure events and various factors.

To investigate the instability of rock slopes along joint surfaces in water-impounded abandoned open-pit mines under fluctuating water-level conditions, physical model tests were performed using similar materials containing joints and weak interlayers. The mix proportion of the similar materials was optimized through orthogonal experimental design to ensure that their mechanical and seepage properties corresponded to those of the in-situ rock mass. Multiple types of sensors were embedded in the model to monitor pore water pressure, earth pressure, moisture content, and displacement, enabling systematic observation of the multi-field responses of the slope during water-level fluctuations. Working conditions with different numbers of cycles and varied rates of water-level change were designed to examine the deformation characteristics and failure mechanisms of the slope. The results indicate that certain damage to the slope surface is caused by the scouring effect induced by repeated water-level fluctuations at a constant rate, although internal structural damage is limited. This suggests that overall stability is little influenced by slow, single water-level fluctuations. The rate of water-level change is shown to significantly affect slope behavior, with slope displacement increases positively correlated with the rate of water-level decline. Furthermore, the greater the outward-directed pore water pressure of the slope is, the more significantly it is influenced by the hysteresis effect, and the higher likelihood of slope instability becomes.

To address the problem of reduced defect classification accuracy caused by noise contamination in the bend detection signals of oil and gas pipelines, this paper proposes an oil and gas elbow defect diagnosis model based on Welch power spectrum feature enhancement and multi-head attention improved dual-branch multi-scale-residual collaborative network. Firstly, the Welch method was used to convert the collected time domain signal into a feature-enhanced power spectrum, showing the energy distribution of the defect signals at different frequencies. Secondly, the multi-scale network branch composed of parallel stacked convolutional layers was responsible for extracting the multi-dimensional features of the signal power spectrum, and the multi-head attention mechanism was used to establish long-term associations between features. Simultaneously, the residual network branch captured the detail information of the signal power spectrum. Finally, the deep concatenation layer fused the features extracted by the dual-branch network to achieve defect classification. The experiment results show that in a high-noise environment, the test accuracy of the proposed model is 91.6%. Compared with the models based on Kaiser windows and flat-top windows, the classification accuracy is improved by 1%~7.9%; compared with convolutional neural network (CNN) and long short-term memory network (LSTM), the accuracy is improved by 36.9% and 10.3% respectively.

To improve the seepage monitoring level of pumped storage reservoirs, a discrimination method was proposed for seepage caused by geomembrane aperture damage, based on a seepage-sensing structure and the concept of an equivalent seepage aperture . First, the temperature rise difference between dry and wet conditions was acquired through active heating, and a logarithmic linear fit was performed after normalizing the temperature rise difference with respect to the seepage velocity. Then, a linear fit was established between the seepage velocity and the equivalent seepage aperture. The seepage condition was inferred based on field calibration tests combined with the characteristics of distributed temperature sensing (DTS) curve. Finally, laboratory tests were conducted for validation. The results show that the goodness-of-fit (R²) between differential temperature and the equivalent seepage aperture exceeds 0.96, and the relative error of seepage velocity is below 5%. 86% of the residuals between experimental and simulated temperature rise values fall within ±1℃, and the relative error of seepage velocity also remains below 5%, demonstrating the reliability of the simulations.

To accurately characterize freely accumulated rock blocks and pore space properties in fully mechanized top-coal caving goafs, a PNM was introduced. Slice tests of free rock block accumulation in fixed space and corresponding image processing were conducted. Pore network information was extracted using Open-source libraries like OpenPNM and PoreSpy, followed by 3D visual reconstruction and quantitative characterization of PNM to analyze topological geometric and statistical features of the reconstructed model. Results show that with increasing freely accumulated rock block size in fixed space, pore-throat size increases significantly: the average diameters of pores and throats rise from 2.46 mm and 1.41 mm to 3.85 mm and 2.73 mm, respectively, with weak correlation to throat length. The numbers of pores and throats decrease from 3 146 and 6 428 to 1 043 and 2 225, respectively, and the pore coordination numbers concentrate between 2 and 5 (peak at 2). The proportion of pore space increases, and the connectivity of porous media is enhanced. The topological property parameters are approximately positively skewed distribution as a whole, with the peak shewed to the left and the concentrated values in the dataset being relatively small. Two-point correlation function calculations reveal that the relative errors of porosity between the original image data and the reconstructed model are less than 2.5%.

To address the challenges of high uncertainty and complex influencing factors in predicting blast fragmentation in mining operations, this study proposed a LightGBM prediction model GA-QLightGBM that integrated GA optimisation with quantile regression. First, GA was employed to optimise the hyperparameters of LightGBM by simulating the natural selection process (selection, crossover, and mutation), thereby improving the model's predictive accuracy and stability. Then, different quantiles were set to construct prediction intervals for blast fragmentation, enabling the quantification of prediction uncertainty. Finally, the proposed model was applied to mine field datasets to verify its predictive performance and generalisation ability, providing an effective approach for blast fragmentation prediction and uncertainty analysis. The results show that the model achieves a coefficient of determination (R²) of 0.880 and a mean squared error (MSE) of 0.004 in point prediction, outperforming traditional point prediction models. In interval prediction, the prediction interval coverage probability (PICP), prediction interval normalized average width (PINAW), and corrected prediction interval accuracy (CPIA) are 0.947, 0.228, and 0.762, respectively, confirming the accuracy and reliability of GA-QLightGBM model. These findings offer a practical framework for quantifying the uncertainty of blast fragmentation, supporting refined blast design and risk control in mining engineering.

To standardize the scenario representation of accident disaster emergency rescue systems, improve the efficiency of information transmission and the level of scientific decision-making in the emergency rescue process, literature research and case analysis methods were applied to integrate existing theoretical achievements. The scenario elements of the accident disaster emergency rescue system were extracted from three dimensions: system structure composition, interaction between components, and temporal evolution process. The connotation, extension, structure, and function of each element were clarified. A mathematical model was constructed to demonstrate the feasibility of predicting the next state based on the current state and interaction relationship. The results indicate that the proposed scenario element system is consistent with the knowledge element theory framework, and the dynamic evolution characteristics of the accident disaster emergency rescue system can be systematically characterized.

To obtain the powder suppressant with greater inhibition effect, NaHCO₃, Al(OH)₃, K₂CO₃ and NH₄H₂PO₄ were mixed by a compounding method, and a new composite powder was obtained. A visual spherical vessel was applied to study the effects of the composite powder with different mixing ratios on methane explosion overpressure, flame propagation and free radical production. The results indicate that as the mass proportion of NaHCO₃, Al(OH)₃, K₂CO₃ and NH₄H₂PO₄ is 1∶1∶2∶1, the inhibition effect of composite powder is significantly greater than that of the single powder. The synergistic mechanisms of the composite powder are as follows: Before 250 ℃, the composite powder undergoes a metathesis reaction, a large number of gases such as H₂O, CO₂ and NH₃ are released. In the early stage of methane explosion, the energy is absorbed and methane is diluted by the gases, so the rapid flame propagation is inhibited. NH₄H₂PO₄ plays a role in absorbing C-containing free radicals during methane combustion. This effect is enhanced when powder is mixed, leading to a further inhibition for methane combustion reaction.

To investigate the influence of particle morphology on the explosion characteristics of aluminum powder, spherical aluminum powder (SAP) and flake aluminum powder (FAP) were selected as research subjects. By combining particle image velocimetry with vertical duct explosion experiments, the effects of particle morphology on transient dispersion behavior and flame propagation characteristics were systematically analyzed. The results show that FAP, due to its larger specific surface area and the asymmetric geometry, exhibits stronger suspension capability and turbulence-inducing effects, leading to a more uniform dispersion state under the same mass concentration. Influenced by enhanced turbulence, increased particle population in the critical ignition zone, and greater suspension stability, FAP demonstrates faster ignition response and significantly promoted flame front wrinkling. At a concentration of 350 g/m³, its flame propagation velocity reaches 44.1 m/s, which is 32.8% higher than that of SAP. In contrast, SAP tends to form localized high-mass concentration regions due to agglomeration, resulting in a white overexposure area accounting for 98.6% during the explosion, indicating a more concentrated heat release behavior.