doi:10.16186/j.cnki.1673-9787.2022110051

Received:2022-11-21

Revised:2023-03-13

Online Date:2023-09-26

Prediction method of vertical bearing capacity of piles assisted with GBDT algorithm（Online）

XU Zhijun, ZHAO Shipeng, WANG Zhengquan, TIAN Jiangtao, ZONG Feilong

School of Civil Engineering，Henan University of Technology，Zhengzhou 450000，Henan，China

Abstract:Aiming at the problem that the vertical bearing capacity of piles is difficult to predict accurately due to the many factors affecting the vertical bearing capacity of piles and the complex nonlinear relationship between them，the gradient lifting decision tree （ GBDT） algorithm in machine learning is used to predict the vertical bearing capacity of piles.First of all，174 groups of measured data of vertical bearing capacity of foundation piles were collected from the literature，and the database required by the machine learning prediction model was established.Then，the GBDT algorithm is used to establish the prediction model of the vertical bearing capacity of the foundation pile. Combined with the three evaluation indicators of mean square error （MSE），mean absolute error （MAE） and judgment coefficient （R2），the prediction model established is comprehensively compared with the three models of decision tree （DT），BP neural network （BP） and linear regression （LR），as well as the calculation formula of vertical bearing capacity of piles in the specification. The research results show that compared with DT model，BP model，LR model and calculation formula in the specification，the established GBDT model has the smallest MAE and MSE in the testset and the largest R2.The conclusion is that GBDT model has the advantages of high accuracy，good stabil⁃ity，strong robustness and small model error，and can better predict the vertical bearing capacity of foundation piles.

Key words:vertical bearing capacity of piles;gradient boosting decision tree;prediction model;evaluation index;robustness

CLC：TU473.11