Prediction Model of Maximum Displacement for RC Slabsunder Blast Load Based on Machine Learning

As the main force components of engineering structures, reinforced concrete slab are prone to damage when it is subjected to terrorist attacks or accidental explosions, and even cause the overall collapse of the structure. Therefore, it is of great significance to understand and predict the dynamic response of concrete slab under the action of explosions to enhance the anti-explosion protection ability of engineering structure and reduce the economic loss of life and property. In this paper, the numerical simulation data of ordinary reinforced concrete slab explosion test and parametric analysis based on the test in the literature in China and abroad are collected. The support vector machine and Gaussian process regression algorithms in machine learning regression algorithm are used to predict the maximum displacement of reinforced concrete slab under near-field explosion. The generalization performance of the model is analyzed by using the improved deviation-variance decomposition principle. At the same time, the machine learning model is compared with the existing prediction methods. Finally, the replacement feature importance and Sobol global sensitivity analysis method are used to explain the model from the local and global to increase the reliability of the model. The above results show that the generalization performance of the two machine learning methods is better, but the prediction effect of the Gaussian process regression algorithm is better than that of the support vector machine algorithm. At the same time, compared with the existing prediction methods, it is found that the machine learning method is better, with higher prediction accuracy and computational efficiency. The influence of different input parameters on the output results of the model is obtained, which realizes the interpretability of the output results and further increases its reliability.