Abstract
Accurate prediction of pile bearing capacity is an important part of foundation engineering. Notably, the determination of pile bearing capacity through an in situ load test is costly and time-consuming. Therefore, this study focused on developing a machine learning algorithm, namely, Ensemble Learning (EL), using weight voting protocol of three base machine learning algorithms, gradient boosting (GB), random forest (RF), and classic linear regression (LR), to predict the bearing capacity of the pile. Data includes 108 pile load tests under different conditions used for model training and testing. Performance evaluation indicators such as R-square (R2), root mean square error (RMSE), and MAE (mean absolute error) were used to evaluate the performance of models showing the efficiency of predicting pile bearing capacity with outstanding performance compared to other models. The results also showed that the EL model with a weight combination of w 1 = 0.482, w 2 = 0.338, and w 3 = 0.18 corresponding to the models GB, RF, and LR gave the best performance and achieved the best balance on all data sets. In addition, the global sensitivity analysis technique was used to detect the most important input features in determining the bearing capacity of the pile. This study provides an effective tool to predict pile load capacity with expert performance.
Highlights
In recent studies, many scientists have used a new approach to the problems related to the foundation of the building. at method is known as artificial intelligence (AI)
Scientists have used a lot of different AI algorithms to solve the problem of predicting the pile bearing capacity, which can be named as artificial neural network (ANN) [3, 22,23,24,25], deep neural network (DNN) [26,27,28], adaptive neurofuzzy inference system (ANFIS) [29, 30], and random forest (RF) [23]. e above studies all showed good predictive efficiency and were expected to become a highly generalizable tool in the prediction of pile load capacity
A total of 108 data sets from the published literature were used to train and test the model; and since the data set is aggregated from projects around the world, the resulting model can expect the ability to predict the bearing capacity of different types of piles in different regions and can be widely beneficial internationally. e remainder of the paper is structured as follows: Section 2 presents the materials needed for the research, including an introduction to base machine learning methods such as RF, gradient boosting (GB), and linear regression (LR), as well as an algorithm to combine the underlying algorithms into a voting Ensemble Learning (EL) model
Summary
In the first phase, boosting is designed as a machine learning process to improve the prediction of binary results, which is done through base learners (using sets of decision tree weights) [41]. Gradient boosting used the decision trees model of a fixed size as base learners and used boosting algorithm developed by Friedman [45, 46]. E random forest and gradient boosting models were bagging Ensemble Learning themselves based on multiple decision trees to make final predictions. Erefore, a combined model of random forest, gradient boosting, and classical linear regression based on average voting protocol was expected to give the best prediction of pile load capacity. I 1 yi − yi, where k denotes the number of samples, yi and yi are the actual and predicted outputs, respectively, and y is the average value of yi
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