Abstract
Safety has been always challenging in geotechnical engineering owing to the inherently variable nature of the soil. In pile foundations, conducting field tests is highly expensive and time-consuming, and thus soft-computing based simulation models analysis is a realistic and useful alternative. This study presented a comparative analysis of artificial neural network (ANN)-based hybrid models and conventional soft computing techniques to estimate the probability of failure of pile foundation. With this respect, dynamic pile load test data of pile foundations were used to construct ANN-based models. Five widely used meta-heuristic optimization algorithms, namely particle swarm optimization, grasshopper optimization algorithm, artificial bee colony, ant colony optimization, and ant lion optimizer, were employed for this purpose. In addition, three widely used conventional soft computing techniques; including genetic programming (GP), multivariate adaptive regression splines (MARS), and group method of data handling (GMDH) were utilized for comparison purposes. The performances of all the developed models were assessed using various statistical performance indices. Experimental results show that the ANN-PSO (hybrid model of ANN and particle swarm optimization) and GP estimate the probability of failure of pile foundation accurately both in training and testing phases. However, a detailed review of results reveals that the ANN-PSO (R2 = 0.9773, RMSE = 0.0439) and GP (R2 = 0.9859, RMSE = 0.0353) showed comparatively better performance in the testing phase. The result of the ANN-PSO and GP models is significantly better than those obtained from other benchmark methods. Based on the results, the developed ANN-PSO and GP models can be used to estimate the probability of failure of pile foundation in the design phase of civil engineering projects.
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Kumar, M., Kumar, V., Rajagopal, B.G. et al. State of art soft computing based simulation models for bearing capacity of pile foundation: a comparative study of hybrid ANNs and conventional models. Model. Earth Syst. Environ. 9, 2533–2551 (2023). https://doi.org/10.1007/s40808-022-01637-7
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DOI: https://doi.org/10.1007/s40808-022-01637-7