Frequent rockfalls in mountainous areas pose a serious threat to bridge piers. In this study, scaled laboratory tests and numerical simulations were conducted to investigate the dynamic response and damage mechanisms of double-column reinforced concrete (RC) bridge piers under rockfall impact, considering rockfall mass, release height (or impact velocity), impact angle, impact location, and rockfall shape. From the experimental database, 65 representative valid impact records were retained for detailed analysis. The impact locations were mainly concentrated in the middle and lower sections of the pier and tended to move upward with increasing rockfall mass, release height, and impact angle. Under the sensor-mediated contact condition, the measured peak interfacial force exhibited a positive correlation with both rockfall mass and impact velocity, while it decreased as the impact angle decreased. The measured peak interfacial force under the 25° oblique impact condition was approximately 65.9
Rockburst poses a severe threat to underground coal mining in mountainous terrain, yet its specific mechanism under variable topographic loads remains inadequately studied. Focusing on the Kuangou Coal Mine beneath the rugged topography of the Tianshan Mountains, this work integrates theoretical analysis with multi-source field monitoring to investigate terrain-induced rockburst risk and its mitigation. Results provide field evidence suggesting that mining under high-relief mountains concentrates high-energy seismic events and extreme support pressures. These phenomena are closely associated with rockburst occurrence, which can be explained by the coupling of static compressive stress and dynamic rotating stress. A novel risk identification method is developed using a membership function that synthesizes topographic factors: mountain height, gully drop, gully width, and slope angle. Applied to the I010206 working face, the method identified five additional high-risk zones overlooked by conventional methods, three of which were validated by actual mining. Directional long-borehole hydraulic fracturing was subsequently implemented for rockburst mitigation. After fracturing, microseismic frequency increased by 32.8
Tunnel earthquake disaster investigations show that damage at tunnel portals is the most severe. During the Wenchuan Earthquake, retaining structures effectively prevented landslides on portal slopes. Therefore, research on the seismic performance of retaining structures at tunnel portals is of great significance. In this paper, a large-scale shaking table test was designed and conducted on an anti-dip rock slope at a tunnel portal reinforced with a prestressed anchor cable(bolt) frame beam. The seismic damage evolution of the slope and the seismic performance of the retaining structure were investigated. The test results indicate that although the support system effectively suppressed macroscopic landslides, significant toppling deformation of the slope still occurred. Vertical seismic waves exhibit a pronounced amplification effect at the tunnel portal. As the input acceleration increases, damage initiates in the shallow rock mass near the frame beam; damage to the surrounding rock beneath the lower portion of the lining intensifies and gradually propagates into the deeper part of the slope. Before the input intensity reaches 1.0 g, the axial force at the anchor head located at the slope crest is maximal; after it exceeds 1.0 g, the axial force at the anchor head of the frame beams above the tunnel portal becomes maximal. Throughout the duration of earthquake excitation, the frame beams on the tunnel portal slope are subjected to alternating tensile and compressive forces. When the input seismic intensity exceeds 0.6 g, all frame beams maintain a stress state of outer - side compression and inner - side tension after the earthquake. The seismic design of slope retaining structures should consider both the force and deformation characteristics during and after the earthquake.
Soft–hard composite rock masses are widely encountered in underground roadway engineering, and their mechanical behavior plays a critical role in surrounding-rock stability. However, their damage and failure mechanisms under excavation-induced unloading paths remain insufficiently understood. In this study, triaxial confining-pressure unloading tests combined with acoustic emission (AE) monitoring were conducted on soft–hard composite rock masses. Their damage and failure characteristics and AE responses under unloading paths were investigated, and nonlinear mechanical models based on axial crack strain were developed for the unloading stage. The results show that the specimens exhibit increasingly pronounced brittle failure as the unloading rate increases. The failure mode transitions from shear to splitting failure, accompanied by an increasing contribution of axial crack strain to the total deformation. AE activity exhibits distinct staged and abrupt characteristics, while the amplitude distribution evolves from a “low-amplitude, broad-distribution” pattern to a “high-amplitude, narrow-distribution” pattern, effectively reflecting crack propagation and precursors to instability. The proposed nonlinear models accurately reproduce the axial strain–differential stress and axial strain–time relationships during unloading. The effects of the model parameters on the predicted curves are consistent with the experimental observations, and the axial strain–time model effectively predicts the temporal evolution of strain increments at low unloading rates. These results reveal the unloading-rate-dependent mechanical response and crack evolution of soft–hard composite rock masses, reproduce their strain–stress and strain–time responses during triaxial confining-pressure unloading, and enhance the understanding of unloading-induced failure mechanisms.
Predicting the impacts of climate change on biodiversity is hindered by data scarcity, particularly for semi-arid endemic species. While Deep Learning (DL) offers powerful modeling capabilities, overfitting risks limit its application to rare species. We propose a Deep Transfer Learning (TL) framework to project the distribution of the data-scarce Persian Oak (Quercus brantii) in the Zagros Mountains of western Iran. We pre-trained a Deep Neural Network (DNN) on European Oak (Quercus robur) data and fine-tuned it using limited Q. brantii records. To mitigate spatial autocorrelation, we employed rigorous Spatial Cross-Validation (SCV). The TL model significantly outperformed a tuned Random Forest benchmark (Mean Spatial AUC: 0.88 vs. 0.75), demonstrating superior spatial generalization. We projected distributions under two emission scenarios (SSP2-4.5 and SSP5-8.5) using an ensemble of three CMIP6 models. Projections for 2070, based on the identified critical annual mean temperature threshold of 19.8°C, reveal severe sensitivity to global mitigation: the suitable habitat is projected to contract by 50.1
