Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming View Full Text


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Article Info

DATE

2016-10-24

AUTHORS

Danial Jahed Armaghani, Roohollah Shirani Faradonbeh, Hossein Rezaei, Ahmad Safuan A. Rashid, Hassan Bakhshandeh Amnieh

ABSTRACT

The settlement design of bored piles socketed into rock has received considerable attention. Although many design methods of pile settlement are recommended in the literature, proposing new/practical technique(s) with higher performance prediction is of advantage. A new model based on gene expression programming (GEP) is presented in this paper for predicting the settlement of the rock-socketed pile. To do this, 96 piles socketed in different types of rock (mostly granite) as part of the Klang Valley Mass Rapid Transit project, Malaysia, were studied. In order to propose a predictive model with higher performance prediction, a series of GEP analyses were conducted using the most important factors on pile settlement, i.e. ratio of length in soil layer to length in rock layer, ratio of total length to diameter, uniaxial compressive strength, standard penetration test and ultimate bearing capacity. For comparison purpose, using the same dataset, linear multiple regression (LMR) technique was also performed. After developing the equations, their prediction performances were checked through several performance indices. The results demonstrated the feasibility of GEP-based predictive model of settlement. Coefficients of determination (CoD) values of 0.872 and 0.861 for training and testing datasets of GEP equation, respectively, show superiority of this model in predicting pile settlement while these values were obtained as 0.835 and 0.751 for the LMR model. Moreover, root mean square error (RMSE) values of (1.293 and 1.656 for training and testing) and (1.737 and 1.767 for training and testing) were achieved for the developed GEP and LMR models, respectively. More... »

PAGES

1115-1125

References to SciGraph publications

  • 2002. Gene Expression Programming in Problem Solving in SOFT COMPUTING AND INDUSTRY
  • 2012-06-12. Prediction of Backbreak in Open-Pit Blasting Operations Using the Machine Learning Method in ROCK MECHANICS AND ROCK ENGINEERING
  • 2010-03-10. Prediction of rock fragmentation due to blasting using artificial neural network in ENGINEERING WITH COMPUTERS
  • 2016-04-11. Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction in INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY
  • 2015-10-31. Several non-linear models in estimating air-overpressure resulting from mine blasting in ENGINEERING WITH COMPUTERS
  • 2016-04-01. Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2016-04-06. Modification and prediction of blast-induced ground vibrations based on both empirical and computational techniques in ENGINEERING WITH COMPUTERS
  • 2016-09-14. Airblast prediction through a hybrid genetic algorithm-ANN model in NEURAL COMPUTING AND APPLICATIONS
  • 2015-03-20. Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods in ENGINEERING WITH COMPUTERS
  • 2011-08-30. A new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problems in NEURAL COMPUTING AND APPLICATIONS
  • 2011-05-22. Correlation of Pile Axial Capacity and CPT Data Using Gene Expression Programming in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • 2016-04-13. Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study in JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE A
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