A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles

The aim of this research is to develop three soft-computing techniques, including adaptive-neuro-fuzzy inference system (ANFIS), genetic-programming (GP) tree-based, and simulated annealing–GP or SA–GP for prediction of the ultimate-bearing capacity ( Q ult ) of the pile. The collected database cons...

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Bibliographic Details
Published in:Engineering with computers Vol. 37; no. 3; pp. 2111 - 2127
Main Authors: Yong, Weixun, Zhou, Jian, Jahed Armaghani, Danial, Tahir, M. M., Tarinejad, Reza, Pham, Binh Thai, Van Huynh, Van
Format: Journal Article
Language:English
Published: London Springer London 01.07.2021
Springer Nature B.V
Subjects:
Area
CAE) and Design
Calculus of Variations and Optimal Control; Optimization
Classical Mechanics
Computer Science
Computer-Aided Engineering (CAD
Control
Cross-sections
Driven piles
Drop hammers
Estimation
Fuzzy logic
Genetic algorithms
Machine learning
Math. Applications in Chemistry
Mathematical and Computational Engineering
Original Article
Performance indices
Pile bearing capacities
Prediction models
Predictions
Sensitivity analysis
Simulated annealing
Systems Theory
Hybrid SA–GP
GP tree-based
ANFIS
Pile bearing capacity
Predictive model
ISSN:0177-0667, 1435-5663
Online Access:Get full text
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Summary:The aim of this research is to develop three soft-computing techniques, including adaptive-neuro-fuzzy inference system (ANFIS), genetic-programming (GP) tree-based, and simulated annealing–GP or SA–GP for prediction of the ultimate-bearing capacity ( Q ult ) of the pile. The collected database consists of 50 driven piles properties with pile length, pile cross-sectional area, hammer weight, pile set and drop height as model inputs and Q ult as model output. Many GP and SA–GP models were constructed for estimating pile bearing capacity and the best models were selected using some performance indices. For comparison purposes, the ANFIS model was also applied to predict Q ult of the pile. It was observed that the developed models are able to provide higher prediction performance in the design of Q ult of the pile. Concerning the coefficient of correlation, and mean square error, the SA–GP model had the best values for both training and testing data sets, followed by the GP and ANFIS models, respectively. It implies that the neural-based predictive machine learning techniques like ANFIS are not as powerful as evolutionary predictive machine learning techniques like GP and SA–GP in estimating the ultimate-bearing capacity of the pile. Besides, GP and SA–GP can propose a formula for Q ult prediction which is a privilege of these models over the ANFIS predictive model. The sensitivity analysis also showed that the Q ult of pile looks to be more affected by pile cross-sectional area and pile set.
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ISSN:0177-0667
1435-5663
DOI:10.1007/s00366-019-00932-9