Recent advances in deep learning and artificial intelligence (AI) have significantly transformed the analysis of rock slopes and geotechnical structures. Rock slope stability is governed by complex interactions among rock mass discontinuity networks, mechanical properties, environmental loading conditions, and stress redistribution. Traditional analytical and numerical methods, including discrete element methods, finite element simulations, and limit equilibrium approaches, provide valuable insights; however, they often have limitations in capturing complex failure mechanisms and handling heterogeneous datasets. This review systematically synthesizes recent developments in AI-driven approaches for rock slope engineering, with particular emphasis on their integration with physical and numerical modeling frameworks and their role in improving the performance assessment of geotechnical systems. Key applications include machine learning-based slope stability prediction, automated discontinuity detection, surrogate modeling for numerical simulations, and spatiotemporal forecasting of slope deformation using monitoring data. The review further discusses emerging approaches such as physics-informed machine learning, digital twin systems, and hybrid AI–numerical frameworks, which combine data-driven learning with established rock mechanics principles. In addition, the potential of AI technologies to support sustainable rock slope management is evaluated, including early warning systems, optimal stabilization design, and resilient infrastructure monitoring. Finally, major challenges related to data quality, model interpretability, uncertainty, and integration with physical models are identified. The review suggests that future research should focus on integrating AI with physics-based modeling and uncertainty quantification, supported by rigorous validation strategies and high-quality datasets, to improve reliability and practical applicability in rock slope engineering. This paper provides a comprehensive perspective on how AI and deep learning can improve the understanding, prediction, and long-term management of rock slopes in modern geotechnical engineering practice.

Crack coalescence between discontinuities plays a critical role in the progressive failure and stability evolution of jointed rock masses. This study aims to develop a modified coalescence coefficient for quantifying crack coalescence behavior and evaluating its influence on rock mass stability. Uniaxial compression tests and heterogeneous numerical simulations were conducted to investigate the effects of crack dip angle, rock bridge dip angle, crack length, and rock bridge length on crack coalescence. The results revealed cubic polynomial relationships between the coalescence coefficient and crack dip angle, rock bridge dip angle, and crack length, whereas a linear relationship was identified with rock bridge length. Based on an L₂₅(5⁴) orthogonal design and nonlinear regression analysis, a modified coalescence coefficient applicable to jointed rock masses under static loading conditions was proposed. The coefficient was subsequently incorporated into the block theory implemented in GeoSMA-3D and applied to a tunnel excavation case. The results demonstrated that considering structural-plane coalescence significantly alters the geometry and volume of key blocks, leading to a more realistic stability assessment. The proposed coefficient provides an effective tool for improving block identification and stability evaluation in underground rock engineering.

Fracture roughness is critical to fluid flow behavior in fractured rock masses. However, the mechanism by which such roughness heterogeneity influences fluid flow and amplifies cubic law deviation remains incompletely understood. Current theoretical analyses are focused on uniform roughness or smooth parallel plates, neglecting the roughness heterogeneity in natural fractures. The equivalent fracture geometry models with heterogeneous roughness are established based on the fracture walls of the smooth parallel plates and the sinusoidal profiles in this study. Based on the geometry models and derived from the Navier–Stokes equations, two simplified fracture models are proposed: the equivalent plate–sinusoidal walled and the sinusoidal–sinusoidal walled fracture model, validated via COMSOL Multiphysics. A roughness heterogeneity index Z_r is defined to quantify the roughness heterogeneity. The influence of roughness heterogeneity on hydraulic behavior (e.g., fluid flow rate, equivalent hydraulic aperture) is analyzed and compared with those of uniform roughness and smooth parallel plates. Additionally, the influence of roughness heterogeneity on the power–law exponent relationship between fracture mechanical aperture and flow rate is examined. The results indicate that the flow rate and hydraulic aperture decrease with increasing roughness heterogeneity, while the deviation of fluid flow from the cubic law increases. The power–law exponent can be as high as 15.5. This study provides theoretical models for understanding the effects of roughness heterogeneity and a reference for extending flow models to complex fracture morphologies.

The urgent global need for sustainable infrastructure drives the demand for high-value buildings and waste removal. This paper studies the feasibility of using recycled foam concrete aggregate (FCA) as a substitute for natural coarse aggregate (NCA) in concrete and studies its impact on rheology, mechanical degradation, shear resistance, and the full-range replacement ratio (0–100). The experimental results show that the monotonic change in the workability of fresh concrete determines the lubrication threshold at 60% replacement, which is driven by the volume proportion effect. Beyond this value, capillary suction dominates, and the viscosity rises rapidly. From a mechanical perspective, the porous structure of FCA is conducive to “internal curing” so that moisture is released from the drying interface, but it also becomes a source of defects that change the fault topology. Specifically, the critical transition of the shear failure mode shifts from the debonding of the interface to the crushing of the cross-particle aggregate. At this time, the shear capacity decreases substantially, experiencing a reduction of 71.8% when completely replaced. There is a strong correlation between ultrasonic pulse velocity (UPV), rebound number, and compressive strength, and a multivariate nonlinear regression model (R² > 0.85) with non-destructive strength prediction is ultimately obtained. Based on the balance between mechanical capacity and resource cyclability, an optimal alternative zone of 20% to 40% is proposed. This work not only provides a mechanism for multi-scale coupling between pore structure and structural properties but also provides a data-driven method for the safety assessment of lightweight recycled aggregate concrete (RAC).