Enhanced Prediction and Evaluation of Hydraulic Concrete Compressive Strength Using Multiple Soft Computing and Metaheuristic Optimization Algorithms

Abstract

Concrete is the material of choice for constructing hydraulic structures in water-related buildings, and its mechanical properties are crucial for evaluating the structural damage state. Machine learning models have proven effective in predicting these properties. However, a single machine learning model often suffers from overfitting and low prediction accuracy. To address this issue, this study introduces a novel hybrid method for predicting concrete compressive strength by integrating multiple soft computing algorithms and the stacking ensemble learning strategy. In the initial stage, several classic machine learning models are selected as base models, and the optimal parameters of these models are obtained using the improved metaheuristic-based gray wolf algorithm. In the subsequent stage, the lightweight gradient boosting tree (LightGBM) model and the metaheuristic-based optimization algorithm are combined to integrate information from base models. This process identifies the primary factors affecting concrete compressive strength. The experimental results demonstrate that the hybrid ensemble learning and heuristic optimization algorithm achieve a regression coefficient of 0.9329, a mean absolute error (MAE) of 2.7695, and a mean square error (MSE) of 4.0891. These results indicate superior predictive performance compared to other advanced methods. The proposed method shows potential for application in predicting the service life and assessing the structural damage status of hydraulic concrete structures, suggesting broad prospects.

Keywords

• structural damage diagnosis
• machine learning
• artificial intelligence
• concrete life prediction
• data mining