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 influen...
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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 HMSLgwoalgorithm, 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. This study proposed a comprehensive assessment model for grouting quality based on the improved catastrophe progression method and the HMSLgwoalgorithm optimized AHP method, which can reduce the complexity of calculation and subjectivity of manual modification while ensuring accurate results, the evaluation results can effectively reflect the grouting quality situation in the grouting area. image
The grey wolf optimisation (gwo) algorithm has fewer numbers of variables and appears quite simple with outstanding capabilities in solving the problems, which are used to describe mathematically what human met in nat...
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The grey wolf optimisation (gwo) algorithm has fewer numbers of variables and appears quite simple with outstanding capabilities in solving the problems, which are used to describe mathematically what human met in nature. However, it still has its capability to be improved in the convergence ratio, stability, and reduce the errors. And it is also easily trapped in local optimum and converged slowly approaching the end, which is just the same defect appearing in other meta-heuristic algorithms such as the bat algorithm (BA), the particle swarm optimisation (PSO) algorithm, and the genetic algorithm (GA). Lots of improvements have been proposed before. In this paper, we propose an improved gwo algorithm inspired by the PSO algorithm to fasten the convergence ratio and reduce the errors. Empirical work and verifications are carried out;and results show its better performance than the standard gwoalgorithm and other well-known meta-heuristic algorithms
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