Resumen
The present study focused on the design of geothermal energy piles based on cone penetration test (CPT) data, which was obtained from the Perniö test site in Finland. The geothermal piles are heat-capacity systems that provide both a supply of energy and structural support to civil engineering structures. In geotechnical engineering, it is necessary to provide an efficient, reliable, and precise method for calculating the group capacity of the energy piles. In this research, the first aim is to determine the most significant variables required to calculate the energy pile capacity, i.e., the pile length (L), pile diameter (D), average cone resistance (qc0" role="presentation">????0qc0
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), minimum cone resistance (qc1" role="presentation">????1qc1
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), average of minimum cone resistance (qc2" role="presentation">????2qc2
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), cone resistance (qc" role="presentation">????qc
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), Young?s modulus (E" role="presentation">??E
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), coefficient of thermal expansion (αc" role="presentation">????ac
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), and temperature change (ΔT" role="presentation">????T
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). The values of qc0, qc1, qc2, qc, and E are then employed as model inputs in soft computing algorithms, which includes random forest (RF), the support vector machine (SVM), the gradient boosting machine (GBM), and extreme gradient boosting (XGB) in order to predict the pile group capacity. The developed soft computing models were then evaluated by using several statistical criteria, and the lowest system error with the best performance was attained by the GBM technique. The performance parameters, such as the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean biased error (MBE), median absolute deviation (MAD), weighted mean absolute percentage error (WMAPE), expanded uncertainty (U95), global performance indicator (GPI), Theil?s inequality index (TIC), and the index of agreement (IA) values of the testing data for the GBM models are 0.80, 0.10, 0.08, -0.01, 0.06, 0.21, 0.28, -0.00, 0.11, and 0.94, respectively, demonstrating the strength and capacity of this soft computing algorithm in evaluating the pile?s group capacity for the energy pile. Rank analysis, error matrix, Taylor?s diagram, and the reliability index have all been developed to compare the proposed model?s accuracy. The results of this research also show that the GBM model developed is better at estimating the group capacity of energy piles than the other soft computing models.