A framework for remediation prioritization of unstable rock slopes based on indirect information

Prioritizing multiple potentially unstable slopes based on varying levels of criticality is an essential approach when resources are limited. This study presents a framework for planning the prioritization of unstable rock slope remediation. The prioritization of mitigation measures for these slopes is determined using deformation values derived from indirect information, such as rock classification systems. To demonstrate the application of this framework, a case study involving unstable rock slopes in southwestern China is presented. Initially, the most appropriate estimation model was chosen from among six candidates for estimating the deformation modulus of the unstable rock mass in the slope, utilizing indirect information. Subsequently, the deformation modulus, updated through Bayesian analysis, was employed to generate the corresponding deformation distributions for the rock slopes. Compared to traditional quantitative methods, the proposed framework is adaptable to various working conditions and yields more definitive results. The indirect data used in this framework are cost-effective, enabling the assessment of all unstable slopes. Moreover, the effect of the amount of indirect data and prior information on slope remediation prioritization is discussed. The proposed framework offers a valuable tool for decision-makers in planning and prioritizing mitigation measures.