An improved catastrophe progression method based on HMSLGWO–AHP for grouting quality assessment

Abstract Assessing the quality of grouting is a crucial step in the control of grouting construction. The evaluation methods of existing studies are complex, and the evaluation process often suffers from the subjective influence of indicator weightings, detracting from the objectivity of the results. To address these concerns, a streamlined and efficacious comprehensive assessment model is introduced. The model incorporates the catastrophe progression method, which obviates the need for index weight determination. Subsequently, the analytic hierarchy process (AHP) method improved by the hierarchical multi‐strategy learning gray wolf optimization (HMSLGWO) algorithm is employed to determine the relative significance of indices, in which, the HMSLGWO algorithm, augmented by Gaussian mixture model clustering and multi‐strategy learning, optimizes the consistency of the AHP judgment matrix. This enhancement mitigates the complexity and subjective interference associated with manual adjustments while preserving result accuracy. Additionally, modifications to the result representation of the original catastrophe progression method enhance the clarity of assessment expressions. Finally, a case study of an actual grouting initiative, alongside comparative analyses, substantiates the model's efficacy and applicability.