Prediction of vertical pile capacity of driven pile in cohesionless soil using artificial intelligence techniques
| Autor: | Ranajeet Mohanty, Shakti Suman, Sarat Kumar Das |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Engineering
Environmental Engineering Multivariate adaptive regression splines business.industry 0211 other engineering and technologies Empirical modelling Statistical parameter Soil Science Genetic programming 02 engineering and technology Overfitting Geotechnical Engineering and Engineering Geology 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Geotechnical engineering Artificial intelligence Bearing capacity business Pile Predictive modelling 021101 geological & geomatics engineering |
| Zdroj: | International Journal of Geotechnical Engineering. 12:209-216 |
| ISSN: | 1939-7879 1938-6362 |
| DOI: | 10.1080/19386362.2016.1269043 |
| Popis: | Piles are used in substructures of different infrastructural constructions. Due to the complex nature of soil, there are different empirical models to predict the bearing capacity of piles. The objective of the present study is to develop prediction models for vertical loaded driven piles in cohesionless soil using a novel artificial intelligence (AI) technique multi-objective genetic programming (MOGP). Two other recent AI techniques, multivariate adaptive regression spline (MARS) and functional network (FN), are also used to compare the efficacy of different AI techniques. The results MOGP, MARS and FN models are compared in terms of different statistical parameters such as correlation coefficient (R), absolute average error, root-mean–square-error, overfitting ratio and P50. A ranking criteria approach has been implemented to assess the performance of the prediction models developed in this study along with other AI and empirical models available in the literature. The predictive model equation... |
| Databáze: | OpenAIRE |
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