Study on characteristics and prediction model of jet impact concrete crushing based on SPH modeling. (October 2022)

• Record Type: Journal Article
• Authors: Ju, Anjun; Zhang, Rui; Cai, Yuting; Ling, Jun; Yang, Jie; Su, Chunli
• Is Part Of: Structures. Volume 44(2022)
• Page Start: 1523
• Page End: 1531
• Publication Date: 2022-10
• Subjects: Smoothed Particle Hydrodynamics (SPH) method -- Jet impact concrete -- Crushing characteristics -- Prediction model; Structural engineering -- Periodicals; 624.1
• DOI: 10.1016/j.istruc.2022.08.065
• Languages: English
• ISSNs: 2352-0124

Abstract

Highlights: The numerical model of water jet impact concrete is verified. The effect of different parameters on the degree of concrete crushing are investigated. Concrete crushing is mainly caused by continuous damage and spalling of concrete particles. The XGBoost algorithm is used to predict concrete crushing damage. Abstract: The complex mechanical response characteristics of concrete materials under hydraulic damage is a difficult research area for hydraulic crushing concrete. In order to improve the accuracy of hydraulic crushing concrete, the Smoothed Particle Hydrodynamics (SPH) particles are conducted to establish the numerical model of jet impact concrete and verified by hydraulic crushing concrete test. Secondly, image processing technology were used to explore the effect of different parameters on concrete crushing damage, and then the concrete crushing process was further studied. Finally, the concrete crushing damage was predicted by Extreme gradient boosting (XGBoost) algorithm. The results show that the numerical model of jet impact concrete based on the Smoothed particle hydrodynamics (SPH) method was consistent with the experimental results of hydraulic crushing concrete. Furthermore, the jet pressure exerted the greatest effect on the degree of concrete crushing, followed by nozzle diameter and abrasive concentration. The stress of concrete under jet impact changes rapidly, and its damage form is expressed as compression-shear damage. Finally, the goodness of fit (R 2), root mean square error (RMSE) and mean absolute error (MAE) of the XGBoost algorithm prediction model are 0.962, 0.00987 and 0.0078, respectively, which indicates that the XGBoost algorithm has good accuracy in predicting the effect of jet impact concrete.