Early Warning Model Construction for Deformation Monitoring and Management of Deep Foundation Pit Project Combined with Artificial Intelligence

In various engineering construction projects, construction safety problems caused by pit deformation continue to be solved. The existing early warning model for pit deformation management cannot effectively meet the needs of actual construction for complex pit projects. Artificial intelligence technology has more obvious advantages in foundation pit deformation detection due to its wide applicability, flexibility, and other characteristics. This study uses Gaussian regression analysis model to construct a corresponding deep foundation pit deformation monitoring and management warning model. The purpose is to better monitor and manage the deformation of deep foundation pits, ensuring the smooth and stable development of the entire construction project. In the experimental analysis, different performance indicators were used to verify the effectiveness of the research method, including different error indicators, precision, recall rate, F1 score, etc. MAE can effectively evaluate the deviation between predicted values and actual values, which indicates that the model is closer to the true value. Precision, recall, and F1 score can better evaluate the proportion of correctly classified samples and demonstrate the model's discriminative ability. These indicators comprehensively measure the performance of the model from different perspectives. In specific construction projects, the results showed that the proposed method had an RMSE of 0.012 and a MAE of 0.015, both significantly lower than the comparative methods, indicating better performance. The precision, recall, and F1 score of GRGA were 92.37%, 47.52%, and 0.17, respectively. In the comparison of existing foundation pit deformation monitoring methods BPNN, CNN, and GM, the precision was 90.52%, 90.03%, and 89.95%, respectively, the recall was 34.20%, 32.01%, and 29.67%, respectively, and the F1 score was 0.10, 0.13, and 0.14, respectively. The research method has more obvious advantages. The results demonstrate that the early warning model is an effective method for analyzing and predicting the deformation of deep foundation pits. The combination of Gaussian regression and genetic algorithm for deep excavation management can model and predict nonlinear deformation data, optimize the parameters of Gaussian regression process, and improve prediction accuracy. Compared with existing warning methods, the method proposed in this study utilizes Gaussian regression process to better model and analyze the deformation process of foundation pits, thus accurately analyzing the detailed changes of foundation pits.