Interpretable gradient boosting based ensemble learning and African vultures optimization algorithm optimization for estimating deflection induced by excavation

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Intelligent construction has become an inevitable trend in the development of the construction industry. In the excavation project, using machine learning methods for early warning can improve construction efficiency and quality and reduce the chances of damage in the excavation process. An interpretable gradient boosting based ensemble learning framework enhanced by the African Vultures Optimization Algorithm (AVOA) was proposed and evaluated in estimating the diaphragm wall deflections induced by excavation. We investigated and compared the performance of machine learning models in predicting deflections induced by excavation based on a database generated by finite element simulations. First, we exploratively analyzed these data to discover the relationship between features. We used several state-of-the-art intelligent models based on gradient boosting and several simple models for model selection. The hyperparameters for all models in evaluation are optimized using AVOA, and then the optimized models are assembled into a unified framework for fairness assessment. The comprehensive evaluation results show that the AVOA-CatBoost built in this paper performs well (RMSE = 1.84, MAE = 1.18, R2 = 0.9993) and cross-validation (RMSE = 2.65 ± 1.54, MAE = 1.17 ± 0.23, R2 = 0.998 ± 0.002). In the end, in order to improve the transparency and usefulness of the model, we constructed an interpretable model from both global and local perspectives.