Approximating Helical Pile Pullout Resistance Using Metaheuristic-Enabled Fuzzy Hybrids

Piles have paramount importance for various structural systems in a wide scope of civil and geotechnical engineering works. Accurately predicting the pullout resistance of piles is critical for the long-term structural resilience of civil infrastructures. In this research, three sophisticated models...

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Bibliographic Details
Published in:Buildings (Basel) 2023-01, Vol.13 (2), p.347
Main Authors: Ahmadianroohbakhsh, Mohammadmehdi, Fahool, Farzad, Pour, Mohammad, Mojtahedi, S., Ghorbanirezaei, Behnam, Nehdi, Moncef
Format: Article
Language:English
Subjects:
Accuracy
Adaptive systems
Algorithms
Ant colony optimization
Artificial intelligence
Artificial neural networks
Civil engineering
Correlation coefficient
Correlation coefficients
Datasets
Efficiency
Evolutionary computation
evolutionary optimization
Fuzzy logic
Fuzzy sets
fuzzy systems
Geotechnical engineering
Geotechnology
helical piles
Heuristic methods
Hybrids
Optimization
Piles
Pull-out resistance
pullout resistance
Root-mean-square errors
Sample variance
Shear strength
Simulation
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Summary:Piles have paramount importance for various structural systems in a wide scope of civil and geotechnical engineering works. Accurately predicting the pullout resistance of piles is critical for the long-term structural resilience of civil infrastructures. In this research, three sophisticated models are employed for precisely predicting the pullout resistance (Pul) of helical piles. Metaheuristic schemes of gray wolf optimization (GWO), differential evolution (DE), and ant colony optimization (ACO) were deployed for tuning an adaptive neuro-fuzzy inference system (ANFIS) in mapping the Pul behavior from three independent factors, namely the embedment ratio, the density class, and the ratio of the shaft base diameter to the shaft diameter. Based on the results, i.e., the Pearson’s correlation coefficient (R = 0.99986 vs. 0.99962 and 0.99981) and root mean square error (RMSE = 7.2802 vs. 12.1223 and 8.5777), the GWO-ANFIS surpassed the DE- and ACO-based ensembles in the training phase. However, smaller errors were obtained for the DE-ANFIS and ACO-ANFIS in predicting the Pul pattern. Overall, the results show that all three models are capable of predicting the Pul for helical piles in both loose and dense soils with superior accuracy. Hence, the combination of ANFIS and the mentioned metaheuristic algorithms is recommended for real-world purposes.
ISSN:2075-5309
2075-5309
DOI:10.3390/buildings13020347