A Systematic Review of Machine Learning Techniques and Applications in Soil Improvement Using Green Materials - PRISMA meta-analysis

Aug 3, 2026

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Extensive evaluation of published studies finds limited systematic literature reviews focused on machine learning prediction techniques for soil improvement using green materials. Machine learning algorithms are reviewed as effective tools for predicting compressive strength, deformations, bearing capacity, California bearing ratio, compaction performance, stress–strain behavior, geotextile pullout strength, and soil classification. The study comprehensively assesses recent advances in machine learning algorithms through PRISMA-guided systematic procedures and meta-analysis.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages17

Reading Time43 min

AccessFree to download

Abstract

Research gap and scope

Prediction targets and evaluated algorithms

Methodology (PRISMA and meta-analysis)

Databases and screening criteria

Impact and implications for geotechnical practice

Scopus-Print Document [https://www.scopus.com/citation/print.uri?origin=recordpage&sid=&s..](https://www.scopus.com/citation/print.uri?origin=recordpage&sid=&s..) . Documents | Saad, A. H.a , Nahazanan, H.a , Yusuf, B.a , Toha, S. F. b , Alnuaim, A.c , El-Mouchi, A.d , Elseknidy, M.e , Mohammed, A.A.a
A Systematic Review of Machine Learning Techniques and Applications in Soil Improvement Using Green Materials
(2023) Sustainability (Switzerland), 15 (12), [art. no. 9738](art. no. 9738) , . Cited 3 times.
DOI: 10.3390/su15129738
a Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, Selangor, Serdang, 43400, Malaysia b Department of Mechatronics, Faculty of Engineering, International Islamic University Malaysia (IIUM), Kuala Lumpur, 53100, Malaysia
c College of Engineering, Civil Engineering Department, King Saud University, Riyadh, 11421, Saudi Arabia
d School of Engineering, Faculty of Applied Science, The University of British Columbia, Okanagan Campus, 3333 University Way, Kelowna, BC V1V 1V7, Canada
e Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia, Selangor, Serdang, 43400, Malaysia
Abstract
According to an extensive evaluation of published studies, there is a shortage of research on systematic literature reviews related to machine learning prediction techniques and methodologies in soil improvement using green materials. A literature review suggests that machine learning algorithms are effective at predicting various soil characteristics, including compressive strength, deformations, bearing capacity, California bearing ratio, compaction performance, stress–strain behavior, geotextile pullout strength behavior, and soil classification. The current study aims to comprehensively evaluate recent breakthroughs in machine learning algorithms for soil improvement using a systematic procedure known as PRISMA and meta-analysis. Relevant databases, including Web of Science, ScienceDirect, IEEE, and SCOPUS, were utilized, and the chosen papers were categorized based on: the approach and method employed, year of publication, authors, journalsand conferences, research goals, findings and results, and solution and modeling. The review results will advance the understanding of civil and geotechnical designers and practitioners in integrating data for most geotechnical engineering problems. Additionally, the approaches covered in this research will assist geotechnical practitioners in understanding the strengths and weaknesses of artificial intelligence algorithms compared to other traditional mathematical modeling techniques. © 2023 by the authors.
Author Keywords
artificial intelligence; by-product; environmental impact; green materials; PRISMA; soil improvement
Index Keywords
artificial intelligence, byproduct, environmental impact, geotechnical engineering, literature review, machine learning, prediction, soil classification, soil improvement; California, United States
References
Binal, A. , Binal, B. E.
Ternary Diagrams for Predicting Strength of Soil Ameliorated with Different Types of Fly Ash
(2020) Arab. J. Sci. Eng, 45, pp. 8199-8217.
Iravanian, A. , Kassem, Y. , Gökçekuş , H.
Stress–Strain Behavior of Modified Expansive Clay Soil: Experimental
Measurements and Prediction Models
(2022) Environ. Earth Sci, 81, p. 107.
Schaad, D. E. , Halley, J. M. , Wilson, S.A.
Using UCS as a Surrogate Performance Standard at the NCSU NPL Site
(2006) J. Environ. Eng, 132, pp. 1355-1365. | | | --- | --- | 1 of 17 17/5/2024, 4:11 pm Scopus-Print Document [https://www.scopus.com/citation/print.uri?origin=recordpage&sid=&s..](https://www.scopus.com/citation/print.uri?origin=recordpage&sid=&s..) . Prasad, P.S. , Ramana, G.V. Imperial Smelting Furnace (Zinc) Slag as a Structural Fill in Reinforced Soil Structures (2016) Geotext. Geomembr, 44, pp. 406-428. Jiang, P. , Mao,

FAQs

What research gap does the systematic review highlight?

It highlights a shortage of research on systematic literature reviews addressing machine learning prediction techniques and methodologies for soil improvement using green materials.

Which soil properties can machine learning predict according to the review?

Reported predictions include compressive strength, deformations, bearing capacity, California bearing ratio, compaction performance, stress–strain behavior, geotextile pullout strength, and soil classification.

How does the study conduct the review and analysis?

It evaluates recent advances using a PRISMA-based systematic procedure and meta-analysis, drawing on databases such as Web of Science, ScienceDirect, IEEE, and SCOPUS.

Aug 3, 2026

Upload by Himbo