Prediction of unconfined compressive strength and California bearing capacity of cement- or lime-pozzolan-stabilised soil admixed with crushed stone waste

Salehi, Mohammad Hossein; Bayat, Meysam; Saadat, Mohsen; Nasri, Masoud

doi:10.1080/17486025.2022.2040606

Cited by 15 publications

(10 citation statements)

References 63 publications

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“…The Taylor diagram agrees with the performance model shown in Figs 8 and 9 , which shows the ANN and EPR in the 0.95–0.99 segment of the accuracy diagram in Fig 10 . Finally, the closed-form equations especially the EPR model are applicable in the design and construction of landfill liners and subgrade to determine the optimized compressive strength of the compacted earth layer as the foundation course for a flexible pavement at 250 kN/m 2 and compacted earth liner at 200 kN/m 2 strength surfaces to determine the allowable strength for sustainable liner courses for a lime reconstituted gravel-sand-silt-clay (G-S-M-C) graded soil, however this is supported by previous research works [ 1 , 3 , 4 , 65 ]. The ANN model can only be applied within the intelligent interface because it didn’t produce a closed-form equation with which a manual application is executed.…”

Section: Resultsmentioning

confidence: 73%

“…Soil stabilization is the process of changing or maintaining one or more soil qualities to improve a soil’s engineering features and performance [ 1 ]. This process is executed during the construction of civil engineering infrastructures like pavement foundation (e.g., subgrade) and landfill liners (compacted earth liners).…”

Section: Introductionmentioning

confidence: 99%

“…Both transportation geotechnics and environmental geotechnics require a thorough understanding of soil behavior, groundwater conditions, and the interaction between soil and man-made structures [ 26 ]. Many more references have been made that studied the use lime in single and or combination of other cementitious materials such as the application of cement/lime combination with pozzolans accompanied with crushed stone waste to improve the strength of soil [ 60 ], the application of lime-nano-silica combination [ 61 ], the treatment of silty soil with lime considering the strength improvement comparison between UCS and splitting tensile strength [ 62 ], and the application of bagasse-lime combination, where ANN was also utilized to model the behavior of this material combination in soil [ 63 ]. Yet, none tried the use of the novel response surface methodology (RSM) in the combination of other regression techniques to model these problems.…”

Section: Introductionmentioning

confidence: 99%

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Modeling the influence of lime on the unconfined compressive strength of reconstituted graded soil using advanced machine learning approaches for subgrade and liner applications

Guo,

Garcia,

Andrade Valle

et al. 2024

PLoS ONE

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In the field of soil mechanics, especially in transportation and environmental geotechnics, the use of machine learning (ML) techniques has emerged as a powerful tool for predicting and understanding the compressive strength behavior of soils especially graded ones. This is to overcome the sophisticated equipment, laboratory space and cost needs utilized in multiple experiments on the treatment of soils for environmental geotechnics systems. This present study explores the application of machine learning (ML) techniques, namely Genetic Programming (GP), Artificial Neural Networks (ANN), Evolutionary Polynomial Regression (EPR), and the Response Surface Methodology in predicting the unconfined compressive strength (UCS) of soil-lime mixtures. This was for purposes of subgrade and landfill liner design and construction. By utilizing input variables such as Gravel, Sand, Silt, Clay, and Lime contents (G, S, M, C, L), the models forecasted the strength values after 7 and 28 days of curing. The accuracy of the developed models was compared, revealing that both ANN and EPR achieved a similar level of accuracy for UCS after 7 days, while the GP model performed slightly lower. The complexity of the formula required for predicting UCS after 28 days resulted in decreased accuracy. The ANN and EPR models achieved accuracies of 85% and 82%, with R2 of 0.947 and 0.923, and average error of 0.15 and 0.18, respectively, while the GP model exhibited a lower accuracy of 66.0%. Conversely, the RSM produced models for the UCS with predicted R2 of more than 98% and 99%, for the 7- and 28- day curing regimes, respectively. The RSM also produced adequate precision in modelling UCS of more than 14% against the standard 7%. All input factors were found to have almost equal importance, except for the lime content (L), which had an average influence. This shows the importance of soil gradation in the design and construction of subgrade and landfill liners. This research further demonstrates the potential of ML techniques for predicting the strength of lime reconstituted G-S-M-C graded soils and provides valuable insights for engineering applications in exact and sustainable subgrade and liner designs, construction and performance monitoring and rehabilitation of the constructed civil engineering infrastructure.

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Section: Resultsmentioning

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling the influence of lime on the unconfined compressive strength of reconstituted graded soil using advanced machine learning approaches for subgrade and liner applications

Guo,

Garcia,

Andrade Valle

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

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“…A peer glance at the past studies on applying ANN models to predict CBR and UCS. Depending up on the type of the soil and additive added in the blend, the regression performance varies from 0.91 to 0.99 [20,21]. However, there are no explicit mathematical equations from the ANN model; it can have precession prediction.…”

Section: Capability Of Proposed Ann Model and Performancementioning

confidence: 99%

Strength and stiffness prediction models of expansive clays blended with sawdust ash

Alisha¹,

Nagaraju²,

Murty³

et al. 2023

IOP Conf. Ser.: Mater. Sci. Eng.

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Strength and stiffness characteristics are major concern for selecting any geomaterial. However, laboratory testing of these characteristics is time associated, laborious, and high cost. So, there is a need of intelligence tools to estimate the strength and stiffness of geomaterial. The impact of sawdust ash on the stiffness and strength properties of combined expanding clays is discussed in this research. The combined expansive clays underwent tests for California bearing ratio (CBR), unconfined compressive strength (UCS), optimal moisture content, maximum dry density, plasticity characteristics (liquid limit and plastic limit), and differential free swell (DFSI). According to test results, adding more sawdust to the blended clays improves their performance. This study also investigates the artificial neural network (ANN) model that considers six input variables to forecast the CBR and UCS of blended clays. The findings demonstrate that the ANN model performs more accurately for the CBR and UCS models. This clever method may help manage the under- or overestimation of additive dosage and reduce project costs.

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“…The problems of structures situated on weak or soft deposits are represented by significant settlement, low shear and compressive strength parameters [7][8][9][10][11]. Similar to conventional additive such as cement or lime, natural or synthetic fibres such as cotton, coir, sisal, polypropylene, basalt and polyester may be used to enhance the mechanical characteristics of weak or soft soils [12][13][14][15]. The effectiveness of soil improvement method is mainly dependent on the soil characteristics.…”

Section: Introductionmentioning

confidence: 99%

Unconfined Compressive Strength Characteristics of Treated Peat Soil with Cement and Basalt Fibre

2022

IJE

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So far many studies have focused on the mechanical behavior of fibre reinforced soils and stabilized soils with conventional chemical stabilizers such as cement and lime; however, very limited researches were conducted on the unconfined compressive strength characteristics of fibre reinforced cement stabilized peat soils. Fibre-reinforcement of a stabilized soil resulted in a significant improvement in the ductility and strength characteristics of weak or soft soils. The main objective of the current study is considering the effects of cement content, fibre content, fibre length and curing time on the unconfined compressive strength (UCS) of peat soil. The study finds that adding basalt fibre or cement causes a remarkable increase in the UCS values of peat soil. The UCS value of the cement-stabilized sample is observed significantly more than basalt fibre-reinforced ones. However, the sample reinforced with basalt fibers showed more ductile behavior compared to the stabilized sample with cement. The results showed that the increase in UCS values of combined basalt fibre and cement inclusions was more than the increase caused by each of them, individually.

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Prediction of unconfined compressive strength and California bearing capacity of cement- or lime-pozzolan-stabilised soil admixed with crushed stone waste

Cited by 15 publications

References 63 publications

Modeling the influence of lime on the unconfined compressive strength of reconstituted graded soil using advanced machine learning approaches for subgrade and liner applications

Modeling the influence of lime on the unconfined compressive strength of reconstituted graded soil using advanced machine learning approaches for subgrade and liner applications

Strength and stiffness prediction models of expansive clays blended with sawdust ash

Unconfined Compressive Strength Characteristics of Treated Peat Soil with Cement and Basalt Fibre

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