1. Reliability-Based Load and Resistance Factor Design of an Energy Pile with CPT Data Using Machine Learning Techniques.
- Author
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Kumar, Pramod and Samui, Pijush
- Subjects
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LOAD factor design , *BUILDING foundations , *MACHINE learning , *SUSTAINABLE construction , *SOFT computing - Abstract
Energy piles are gaining popularity worldwide as both pile foundations and ground source heat producers. While considerable research has been conducted on energy pile design and evaluation, implementing these approaches effectively remains challenging for engineers. This study presents a novel approach based on Load and Resistance Factor Design (LRFD) with First-Order Second-Moment (FOSM) reliability analysis and target reliability ( β T ) for energy pile design. Additionally, three soft computing techniques, namely 1D-CNN, LSTM, and Bi-LSTM, were employed to enhance the design process. To conduct this research, datasets from each model were analyzed using various statistical indices, including R2, RMSE, RMSLE, MAPE, BF, AARD, NSE, U 95 , t-stat, TIC, and PI. The results indicated that the Bi-LSTM model demonstrated superior performance for heating operations, with R2, RMSE, and RMSLE values of 0.975, 0.021, and 0.007 for training and 0.984, 0.019, and 0.006 for testing datasets, respectively. For cooling operations, the LSTM model exhibited the most suitable predictive capabilities, achieving R2, RMSE, and RMSLE values of 0.969, 0.022, and 0.008 for training and 0.982, 0.019, and 0.007 for testing datasets, respectively. Comparative analyses, including rank analysis, Williams plot, accuracy matrix, and Taylor's diagram, were performed to assess the accuracy of the proposed models. The results demonstrated that the Bi-LSTM model for heating and the LSTM model for cooling outperformed other soft computing models within the reliability-based LRFD approach for energy pile design. This research contributes to the advancement of energy pile design methodologies, providing engineers with a more efficient and reliable LRFD-based framework. The successful application of soft computing techniques further enhances the accuracy of predictions for both heating and cooling operations. Overall, the findings of this study hold significant implications for the design and evaluation of energy piles, contributing to sustainable and energy-efficient construction practices. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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