## Prediction Load-Settlement of Bored PileS Using Artificial Neural Network | ||

Anbar Journal of Engineering Sciences | ||

Volume 15, Issue 1, May 2024, Pages 17-24 PDF (696.13 K) | ||

Document Type: Research Paper | ||

DOI: 10.37649/aengs.2024.144108.1064 | ||

Authors | ||

Omer Monther Jamel^{*} ^{1}; Khalid R. Aljanabi^{2}
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^{1}university of anbar | ||

^{2}Civil engineering, University of Anbar, Ramadi, Iraq | ||

Abstract | ||

Pile foundations are typically employed when top-soil layers are unstable and incapable of bearing super-structural pressures. Accurately modeling pile behavior is crucial for ensuring optimal structural and serviceability performance. However, traditional methods such as pregnancy testing, while highly accurate, are expensive and time-consuming. Consequently, various approaches have been developed to predict load settlement behavior, including using artificial neural networks (ANNs). ANNs offer the advantage of accurately replicating substrate behavior's nonlinear and intricate relationship without requiring prior formulation. This research aims to employ artificial neural network (ANN) modeling techniques to simulate the load-settlement relationship of drilled piles. The primary aims of this study are threefold: firstly, to assess the effectiveness of the generated ANN model by comparing its results with experimental pile load test data; secondly, to establish a validation method for ANN models; and thirdly, to conduct a sensitivity analysis to identify the significant input factors that influence the model outputs. In addition, this study undertakes a comprehensive review of prior research on using artificial neural networks for predicting pile behavior. Evaluating efficiency measurement indicators demonstrates exceptional performance, particularly concerning the agreement between the predicted and measured pile settlement. The correlation coefficient (R) and coefficient of determination (R^2) indicate a strong correlation between the predicted and measured values, with values of 0.965 and 0.938, respectively. The root mean squared error (RMSE) is 0.051, indicating a small deviation between the predicted and actual values. The mean percentage error (MPE) is 11%, and the mean absolute percentage error (MAPE) is 21.83%. | ||

Keywords | ||

Piles; load-settlement; Modeling; Artificial Neural Networks (ANN) | ||

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