Genetic programming for soil-fiber composite assessment. (August 2018)

• Record Type: Journal Article
• Authors: Kurugodu, HV; Bordoloi, S; Hong, Y; Garg, Ankit; Garg, Akhil; Sreedeep, S; Gandomi, AH
• Is Part Of: Advances in engineering software. Volume 122(2018)
• Page Start: 50
• Page End: 61
• Publication Date: 2018-08
• Subjects: Unconfined compressive strength -- Reinforced soil -- Polypropylene fiber -- Genetic programming -- Strength improvement factor; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2018.04.004
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: Problem of Assessment of soil-fiber composite is undertaken in this work. The effect of fiber content, moisture content and soil density on composite is studied. Genetic programming with variable subtrees and depth is proposed to solve problem. Parametric-Sensitivity analysis reveals complex relationships among the variables. GP is able to estimate the mechanical factor of soil-fiber composite accurately. 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.