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Silicon Valley North
Type: Book
ISBN: 978-0-08044-457-4

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Abstract

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Silicon Valley North
Type: Book
ISBN: 978-0-08044-457-4

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Book part
Publication date: 4 September 2004

Abstract

Details

Silicon Valley North
Type: Book
ISBN: 978-0-08044-457-4

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Article
Publication date: 18 April 2017

Harsha Vardhan, Sanandam Bordoloi, Akhil Garg, Ankit Garg and Sreedeep S.

The purpose of this study is to measure the effects of density, moisture, fiber content on unconfined compressive strength (UCS) of soil by formulating the models based on…

Abstract

Purpose

The purpose of this study is to measure the effects of density, moisture, fiber content on unconfined compressive strength (UCS) of soil by formulating the models based on evolutionary approach and artificial neural networks (ANN).

Design/methodology/approach

The present work proposes evolutionary approach of multi-gene genetic programming (MGGP) to formulate the functional relationships between UCS of reinforced soil and four inputs (soil moisture, soil density, fiber content and unreinforced soil strength) of the silty sand. The hidden non-linear relationships between UCS of reinforced soil and the four inputs are determined by sensitivity and parametric analysis of the MGGP model.

Findings

The performance of MGGP is compared to those of ANN and the statistical analysis indicates that the MGGP model is the best and is able to generalize the UCS of reinforced soil satisfactorily beyond the given input range.

Research limitations/implications

The explicit MGGP model will be useful to provide optimum input values for design and analysis of various geotechnical infrastructures. In addition, utilization of Water hyacinth reinforced fiber reinforced soil will minimize negative impact of this species on environment and may generate rural employment.

Originality/value

This work is first of its kind in application and development of explicit holistic model for evaluating the compressive strength of heterogeneous soil blinded with fiber content. This includes the experimental and cross-validation for testing robustness of the model.

Details

Engineering Computations, vol. 34 no. 2
Type: Research Article
ISSN: 0264-4401

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