Genetic programming for soil-fiber composite assessment

Kurugodu, HV; Bordoloi, S; Hong, Y; Garg, A; Garg, A; Sreedeep, S; Gandomi, AH

Genetic programming for soil-fiber composite assessment

Kurugodu, HV Bordoloi, S Hong, Y Garg, A Garg, A Sreedeep, S Gandomi, AH

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Advances in Engineering Software, 2018, 122, pp. 50-61
Issue Date:: 2018-08-01

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Field	Value	Language
dc.contributor.author	Kurugodu, HV
dc.contributor.author	Bordoloi, S
dc.contributor.author	Hong, Y
dc.contributor.author	Garg, A
dc.contributor.author	Garg, A
dc.contributor.author	Sreedeep, S
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2022-08-30T00:56:28Z
dc.date.available	2022-08-30T00:56:28Z
dc.date.issued	2018-08-01
dc.identifier.citation	Advances in Engineering Software, 2018, 122, pp. 50-61
dc.identifier.issn	0965-9978
dc.identifier.issn	1873-5339
dc.identifier.uri	http://hdl.handle.net/10453/161081
dc.description.abstract	Unconfined compressive strength (UCS) of soil is one of the basic index parameters for representing the compressive bearing strength of soil. Fiber reinforced soil is one of the most popular and practical ground improvement approaches used in geotechnical infrastructures. Analytical models for estimating UCS of soil-fiber composites have been developed in the literature. However, these models rarely incorporate the combined effects of dynamic field parameters such as fiber content, soil moisture, and density. These effects can be studied by the development of a holistic model based on a dimensionless strength improvement factor (SIF), which is defined as the ratio of UCS of reinforced soil to the unreinforced UCS. The current model estimating SIF indicates the improvement expected in UCS of soil-PP fiber composite based on the three design conditions such as fiber content, soil density, and moisture content. For this purpose, a series of 108 laboratory tests were first conducted to measure UCS of both fiber-reinforced soil and unreinforced soil under different fiber contents, soil density, and soil moisture content. Clayey silt soil and commercially used polypropylene (PP) fibers were selected in this study as soil and fiber material respectively. Genetic programming (GP) approach was then used to formulate models based on the measured data. The hidden non-linear relationships between SIF and the three inputs were determined by sensitivity and parametric analysis of the GP model. It was found that the moisture content in the soil has the highest influence on the strength factor that accounts for the change in strength. Coupled effects of soil parameters (soil moisture, soil density) and fiber content have been studied using parametric analysis which includes different possible field conditions (parameters). The results have been discussed along with the reinforcement mechanism of PP fiber for different soil conditions. It is believed that the robust GP model developed will be useful to determine optimum input values for designing safe bearing foundation soils which are reinforced with PP fibers.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Advances in Engineering Software
dc.relation.isbasedon	10.1016/j.advengsoft.2018.04.004
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Applied Mathematics
dc.title	Genetic programming for soil-fiber composite assessment
dc.type	Journal Article
utslib.citation.volume	122
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-30T00:56:27Z
pubs.publication-status	Published
pubs.volume	122

Abstract:

Unconfined compressive strength (UCS) of soil is one of the basic index parameters for representing the compressive bearing strength of soil. Fiber reinforced soil is one of the most popular and practical ground improvement approaches used in geotechnical infrastructures. Analytical models for estimating UCS of soil-fiber composites have been developed in the literature. However, these models rarely incorporate the combined effects of dynamic field parameters such as fiber content, soil moisture, and density. These effects can be studied by the development of a holistic model based on a dimensionless strength improvement factor (SIF), which is defined as the ratio of UCS of reinforced soil to the unreinforced UCS. The current model estimating SIF indicates the improvement expected in UCS of soil-PP fiber composite based on the three design conditions such as fiber content, soil density, and moisture content. For this purpose, a series of 108 laboratory tests were first conducted to measure UCS of both fiber-reinforced soil and unreinforced soil under different fiber contents, soil density, and soil moisture content. Clayey silt soil and commercially used polypropylene (PP) fibers were selected in this study as soil and fiber material respectively. Genetic programming (GP) approach was then used to formulate models based on the measured data. The hidden non-linear relationships between SIF and the three inputs were determined by sensitivity and parametric analysis of the GP model. It was found that the moisture content in the soil has the highest influence on the strength factor that accounts for the change in strength. Coupled effects of soil parameters (soil moisture, soil density) and fiber content have been studied using parametric analysis which includes different possible field conditions (parameters). The results have been discussed along with the reinforcement mechanism of PP fiber for different soil conditions. It is believed that the robust GP model developed will be useful to determine optimum input values for designing safe bearing foundation soils which are reinforced with PP fibers.

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