Optimization of ANFIS with GA and PSO estimating α ratio in driven piles View Full Text


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Article Info

DATE

2019-01-23

AUTHORS

Hossein Moayedi, Mehdi Raftari, Abolhasan Sharifi, Wan Amizah Wan Jusoh, Ahmad Safuan A. Rashid

ABSTRACT

This study aimed to optimize Adaptive Neuro-Fuzzy Inferences System (ANFIS) with two optimization algorithms, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for the calculation friction capacity ratio (α) in driven shafts. Various studies are shown that both ANFIS are valuable methods for prediction of engineering problems. However, optimizing ANFIS with GA and PSO has not been used in the area of pile engineering. The training data set was collected from available full-scale results of the driven piles. The input parameters used in this study were pile diameter (m), pile length (m), relative density (Id), embedment ratio (L/D), both of the pile end resistance (qc) and base resistance at relatively 10% base settlement (qb0.1) from CPT result, whereas the output was α. A learning fuzzy-based algorithm was used to train the ANFIS model in the MATLAB software. The system was optimized by changing the number of clusters in the FIS and then the output was used for the GA and PSO optimization algorithm. The prediction was compared with the real-monitoring field data. As a result, good agreement was attained representing reliability of all proposed models. The estimated results for the collected database were assessed based on several statistical indices such as R2, RMSE, and VAF. According to R2, RMSE, and VAF, values of (0.9439, 0.0123 and 99.91), (0.9872, 0.0117 and 99.99), and (0.9605, 0.0119 and 99.97) were obtained for testing data sets of the optimized ANFIS, GA–ANFIS, and PSO–ANFIS predictive models, respectively. This indicates higher reliability of the optimized GA–ANFIS model in estimating α ratio in driven shafts. More... »

PAGES

227-238

References to SciGraph publications

  • 2012-07-06. Least square support vector machine and multivariate adaptive regression spline for modeling lateral load capacity of piles in NEURAL COMPUTING AND APPLICATIONS
  • 2016-03-28. Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling in ENGINEERING WITH COMPUTERS
  • 2016-05-26. An optimized ANN model based on genetic algorithm for predicting ripping production in NEURAL COMPUTING AND APPLICATIONS
  • 2015-10-14. Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles in NEURAL COMPUTING AND APPLICATIONS
  • 2018-09-21. Modification of landslide susceptibility mapping using optimized PSO-ANN technique in ENGINEERING WITH COMPUTERS
  • 2015-10-31. Several non-linear models in estimating air-overpressure resulting from mine blasting in ENGINEERING WITH COMPUTERS
  • 2017-01-19. A Monte Carlo technique in safety assessment of slope under seismic condition in ENGINEERING WITH COMPUTERS
  • 2014-12-03. The uplift load capacity of an enlarged base pier embedded in dry sand in ARABIAN JOURNAL OF GEOSCIENCES
  • 2017-11-16. Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil in ENGINEERING WITH COMPUTERS
  • 2018-09. Performance Analysis of a Piled Raft Foundation System of Varying Pile Lengths in Controlling Angular Distortion in SOIL MECHANICS AND FOUNDATION ENGINEERING
  • 2017-11-22. Implementing an ANN model optimized by genetic algorithm for estimating cohesion of limestone samples in ENGINEERING WITH COMPUTERS
  • 2014-07-10. Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2017-11-13. Developing hybrid artificial neural network model for predicting uplift resistance of screw piles in ARABIAN JOURNAL OF GEOSCIENCES
  • 2018-01-16. Numerical ANFIS-Based Formulation for Prediction of the Ultimate Axial Load Bearing Capacity of Piles Through CPT Data in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • 2018-06-14. Artificial intelligence design charts for predicting friction capacity of driven pile in clay in NEURAL COMPUTING AND APPLICATIONS
  • 2015-03-25. Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting in ENVIRONMENTAL EARTH SCIENCES
  • 2017-04-04. An artificial neural network approach for under-reamed piles subjected to uplift forces in dry sand in NEURAL COMPUTING AND APPLICATIONS
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    http://scigraph.springernature.com/pub.10.1007/s00366-018-00694-w

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