Application of deep reinforcement learning and data fusion in foundation settlement control

In view of the problems of insufficient precision and response lag faced by traditional methods in foundation settlement control, this paper introduces an intelligent control method for foundation settlement based on deep reinforcement learning (DRL) and data fusion. Firstly, the DQN (Deep Q-Network) algorithm in deep reinforcement learning is used to construct an intelligent control model. Through interactive learning between the agent and the environment, the control strategy is automatically optimized to achieve accurate control and prediction of the foundation settlement process. Then, combined with multi-sensor data, including foundation displacement, temperature and humidity, groundwater level and other information, the Kalman filter technology is used to fuse the sensor data, so as to effectively remove noise and error and further improve the accuracy of settlement prediction. Finally, the experimental results show that the proposed method significantly improves the accuracy of settlement prediction under different foundation conditions. For example, under loose soil conditions, the mean squared error (MSE) is 0.92 and the mean absolute error (MAE) is 0.56. In addition, in the real-time and control responsiveness evaluation experiments, the system also shows strong responsiveness, correcting the settlement and restoring stability in a short time. The robustness experiment results show that the system can maintain high control accuracy and fast response capabilities under disturbance conditions such as load fluctuations, environmental interference and sensor noise. In the above data conclusions, the intelligent control method of foundation settlement based on DQN algorithm and data fusion is effective and practical in improving prediction accuracy, response speed and system robustness.