Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest

Tuan Anh Pham,Van Quan Tran,Huong-Lan Thi Vu,Hong-Anh Thi Duong,Loi Van Giap,Hai-Bang Ly

doi:10.3390/app10051871

Tuan Anh Pham, Van Quan Tran + Show 4 more

Open Access

https://doi.org/10.3390/app10051871

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Journal: Applied sciences	Publication Date: Mar 9, 2020
Citations: 58	License type: CC BY 4.0

Affiliation: University Of Transport Technology

Abstract

Axial bearing capacity of piles is the most important parameter in pile foundation design. In this paper, artificial neural network (ANN) and random forest (RF) algorithms were utilized to predict the ultimate axial bearing capacity of driven piles. An unprecedented database containing 2314 driven pile static load test reports were gathered, including the pile diameter, length of pile segments, natural ground elevation, pile top elevation, guide pile segment stop driving elevation, pile tip elevation, average standard penetration test (SPT) value along the embedded length of pile, and average SPT blow counts at the tip of pile as input variables, whereas the ultimate load on pile top was considered as output variable. The dataset was divided into the training (70%) and testing (30%) parts for the construction and validation phases, respectively. Various error criteria, namely mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination (R2) were used to evaluate the performance of RF and ANN algorithms. In addition, the predicted results of pile load tests were compared with five empirical equations derived from the literature and with classical multi-variable regression. The results showed that RF outperformed ANN and other methods. Sensitivity analysis was conducted to reveal that the average SPT value and pile tip elevation were the most important factors in predicting the axial bearing capacity of piles.

Highlights

In applied engineering, piles have been used as the foundation elements, in which the axial bearing capacity of the pile (Pu ) is considered as the most important parameter in the design of pile foundation
The predictive performance given by artificial neural network (ANN) was more reliable compared to supporting vector machines (SVM)
Performance given by ANN was more reliable compared to supporting vector machines (SVM)

Summary

Introduction

Piles have been used as the foundation elements, in which the axial bearing capacity of the pile (Pu ) is considered as the most important parameter in the design of pile foundation. The axial bearing capacity of piles can be determined by five approaches, namely the static analysis, dynamic analysis, dynamic testing, pile load testing, and in-situ testing [1]. Several approaches have been developed to predict the axial bearing capacity of pile or to enhance the prediction accuracy The nature of these methods included some simplifications, assumptions, or empirical approaches with respect to the soil stratigraphy, soil–pile structure interactions, and the distribution of soil resistance along the pile. In such studies, the test results were used as complementary elements to improve the prediction accuracy. The European standard (Euro code 7) [2] recommends using the following ground field tests: DP (dynamic probing test), SS (press-in and screw-on probe test), SPT (standard penetration test), PMT (pressure meter tests), PLT (plate loading test), DMT (flat dilatometer test), FVT (field vane test), CPTu (cone penetration tests with the measurement of pore pressure)

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