A Data-Driven Two-Layer Case-Based Reasoning Framework for Intelligent Deep Excavation Retaining Structure Selection Under Incomplete Information

Abstract

In early-stage projects, incomplete geological data can affect the selection of retaining structures for deep excavations. This paper proposes an adaptive two-layer Case-Based Reasoning (CBR) framework to address this issue. First, a case database was constructed; feature interaction terms were incorporated into the similarity measure to capture the nonlinear coupling among geological parameters, and a Gradient Boosted Decision Tree (GBDT) was employed to achieve an objective, data-driven allocation of feature weights. The first layer implements a gating mechanism based on logical conjunction and adaptive tolerance thresholds, which automatically switches between the two inference paths when information is missing, preventing invalid hard matches; the second layer applies K-means++ clustering and local inductive reasoning to mitigate the biases caused by data sparsity. Experiments demonstrate that, under conditions of parameter incompleteness and noise interference, the method’s Top-3 recommendation accuracy significantly outperforms traditional models and machine learning baseline models and exhibits strong resistance to interference, providing a solid methodological foundation for retaining structure selection in complex data scenarios.