Abstract With the continuous expansion of global transportation infrastructure and the increasing frequency of extreme climate events, the risk of instability of soft rock slopes has increased dramatically. The combined effects of complex geological structures and non-stationary hydroclimatic forcing make traditional stability analysis methods and control technologies inadequate and unreliable. Recently, numerous novel approaches for the stability analysis and control of soft rock slopes have emerged. This paper systematically reviews these approaches and their core advances. The mechanical behavior and degradation mechanisms of soft rocks are reviewed within a coupled multiphysics framework, covering soft rock classification, typical characteristics, transient and time-dependent responses, deformation behavior, and constitutive characterization and parameter evolution. The effects of individual and coupled environmental factors on rock degradation are elucidated. Secondly, stability analysis methods for soft rock slopes are reviewed, incorporating both conventional approaches and their recent advances. The improved methodologies encompass theoretical advances in: (1) limit-equilibrium and block-kinematic formulations; (2)numerical modeling across continuum, discontinuum, and multiscale frameworks; (3)reliability-based analyses integrating probabilistic, spatial, and time-dependent uncertainties; and (4) intelligent approaches that couple data-driven techniques with physical modeling. Reinforcement and protection technologies are subsequently reviewed, including conventional measures for rock/soil slope stabilization, as well as structural reinforcement, seepage control, and ecological stabilization approaches specifically for soft rock slopes. Finally, this paper outlines future perspectives, including: coupling multiphysics modeling with multi-source intelligent monitoring; advancing nature-based protection and durable protective materials; and integrating these into digital-twin platforms for dynamic reliability assessment and AI-assisted decision optimization in soft rock slope management.
Zeng et al. (2026) studied this question.