Effect of nano-silica on engineering properties of lime-treated marl soil

Karimiazar, Jafar; Sharifi Teshnizi, Ebrahim; O'Kelly, Brendan C.; Sadeghi, Shahriar; Karimizad, Nafiseh; Yazdi, Abdollah; Arjmandzadeh, Reza

doi:10.1016/j.trgeo.2023.101123

Cited by 9 publications

(1 citation statement)

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“…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.…”

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confidence: 99%

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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