The current methods for evaluating the stability of cast-in-place pile hole walls are inefficient and have poor accuracy, leading to incorrect selection of steel reinforcement methods and affecting the quality of construction projects. To address the above issues, a smart evaluation method for the stability of cast-in-place pile hole walls is proposed, taking the southeastern coastal region as an example. Firstly, a wall stability evaluation index system was constructed based on typical local geological conditions, covering dimensions such as geological factors, groundwater levels, and construction parameters. Principal Component Analysis (PCA) and Factor Analysis (FA) were applied to extract key feature variables. Secondly, historical data from 90 CIPP engineering projects were preprocessed through cleaning and normalization, and then divided into training and testing sets in a 7:3 ratio. A multi-strategy Harris Hawks Optimization (MHHO) algorithm was used to optimize the initial weights of the Backpropagation Neural Network (BPNN), thereby building a stability prediction model. Finally, the model performance was evaluated using multiple metrics including F1-score, AUC, recall, and fitting degree. The results show that MHHO-BPNN achieved an accuracy of 0.967 and an F1-score of 0.975 on the test set, significantly outperforming mainstream benchmark models such as the Genetic Algorithm-optimized BPNN (RGA-BPNN) and the Particle Swarm Optimization-based SVM (IPSO-SVM). This method provides data support for rapid evaluation of wall stability and reinforcement scheme formulation, and demonstrates strong potential for practical engineering applications.

Keywords: Cast-in-place pile, Stability evaluation, Southeast coastal areas, BPNN, Harris Eagle optimization algorithm