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Geomechanics and Engineering Volume 36, Number 3, February10 2024 , pages 259-276 DOI: https://doi.org/10.12989/gae.2024.36.3.259 |
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Estimating pile setup parameter using XGBoost-based optimized models |
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Xigang Du, Ximeng Ma, Chenxi Dong and Mehrdad Sattari Nikkhoo
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| Abstract | ||
| The undrained shear strength is widely acknowledged as a fundamental mechanical property of soil and is considered a critical engineering parameter. In recent years, researchers have employed various methodologies to evaluate the shear strength of soil under undrained conditions. These methods encompass both numerical analyses and empirical techniques, such as the cone penetration test (CPT), to gain insights into the properties and behavior of soil. However, several of these methods rely on correlation assumptions, which can lead to inconsistent accuracy and precision. The study involved the development of innovative methods using extreme gradient boosting (XGB) to predict the pile set-up component "A" based on two distinct data sets. The first data set includes average modified cone point bearing capacity (qt), average wall friction (fs), and effective vertical stress (ovo), while the second data set comprises plasticity index (PI), soil undrained shear cohesion (𝑆𝑢), and the over consolidation ratio (OCR). These data sets were utilized to develop XGBoost-based methods for predicting the pile set-up component "A". To optimize the internal hyperparameters of the XGBoost model, four optimization algorithms were employed: Particle Swarm Optimization (PSO), Social Spider Optimization (SSO), Arithmetic Optimization Algorithm (AOA), and Sine Cosine Optimization Algorithm (SCOA). The results from the first data set indicate that the XGBoost model optimized using the Arithmetic Optimization Algorithm (XGB- AOA) achieved the highest accuracy, with R2 values of 0.9962 for the training part and 0.9807 for the testing part. The performance of the developed models was further evaluated using the RMSE, MAE, and VAF indices. The results revealed that the XGBoost model optimized using XGBoost-AoA outperformed other models in terms of accuracy, with RMSE, MAE, and VAF values of 0.0078, 0.0015, and 99.6189 for the training part and 0.0141, 0.0112, and 98.0394 for the testing part, respectively. These findings suggest that XGBoost-AoA is the most accurate model for predicting the pile set-up component. | ||
| Key Words | ||
| arithmetic optimization algorithm; cone penetration test; extreme gradient boosting model; particle swarm optimization; pile set-up parameter A; social spider optimization | ||
| Address | ||
| Xigang Du, Ximeng Ma and Chenxi Dong: Department of Transportation Engineering, Hebei University of Water Resources and Electric Engineering, Hebei Cangzhou,061000 Mehrdad Sattari Nikkhoo: Department of Civil Engineering, University of Mohaghegh Ardabili, Ardabil, Iran | ||