Prediction and analysis of long pile base resistance based on CTNOA-SBL-MIC model

Machine learning (ML) has emerged as a powerful tool in the modeling of pile foundation engineering, achieving considerable success. Nevertheless, the prediction of pile base resistance poses a challenge, particularly for long piles. This resistance becomes significant only once the pile has been sufficiently loaded and displaced. To enhance the accuracy of predicting pile base resistance in long piles, we identified three crucial factors: mechanical effects, pile properties, and soil properties. We then developed a novel hybrid model framework. This framework integrates multi-fold cross-validation, chaotic sequences, the nutcracker search algorithm, sparse Bayesian algorithms, and the maximum information coefficient test, thereby improving both the predictive accuracy and the interpretability of the model. We selected a comprehensive dataset comprising 920 realistic instances of long and super-long pile foundation engineering projects from Ho Chi Minh City, Vietnam, as our benchmark dataset. Model performance was evaluated using root mean square error, mean absolute error, and the correlation coefficient as metrics. The results indicate that the proposed model outperforms existing ML models in point prediction, achieving values close to the optimal for various indicators. Furthermore, this study quantifies the correlation strengths among multiple factors influencing pile base resistance, incorporating real engineering insights to significantly enhance the interpretability of the model's internal calculations. This advancement holds profound implications for the design and research of long pile foundations in soft soil conditions.