Study on wetting deformation model of coarse-grained materials based on P-Z model and BP neural network - Zhang, Hongyang; Li, Xuan; Liu, Jianlong; Han, Pengju; Yang, Yige; Ding, Zelin; Han, Liwei; Zhang, Xianqi; Wang, Shunsheng (Frontiers in Earth Science, 2023)
The wetting deformation of coarse-grained materials is an essential factor affecting the safe operation of earth-rock dams. There are many wetting models and complex parameters, and only a small amount of test data is fitted by mathematical statistics. The universality of the obtained wetting model is unknown, and the Duncan-Chang E-B constitutive model cannot accurately reflect the wetting deformation characteristics of coarse-grained materials. Based on the wetting test data, the wetting models proposed by predecessors were compared and analyzed. The results show that the relationship between wetting axial strain and wetting stress level is best expressed by the exponential function. The relationship between wetting volumetric strain and wetting stress level is best described by Cheng’s linear function. By combining the empirical formula with the P-Z model, a P-Z wetting model is established, and then a BP neural network is introduced to establish an artificial neural network wetting deformation prediction model based on the P-Z model. The results show that the P-Z model can well reflect the wetting deformation characteristics of coarse-grained materials under a complex stress path, the relative error between the predicted value and the experimental value of the prediction model is within 6%, and the proposed prediction model is more reliable and accurate.