Strength and durability assessment of expansive soil stabilized with recycled ash and natural fibers

Tiwari, Nitin; Satyam, Neelima; Puppala, Anand J.

doi:10.1016/j.trgeo.2021.100556

Cited by 86 publications

(24 citation statements)

References 57 publications

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“…However, the attendant negative environmental impacts associated with the production of these energy intensive binders are a present global concern. Hence, based on current developments in knowledge and research, attention is gradually shifting from an over-reliance on solely cement and lime to the utilisation of waste materials, industrial and agricultural by-products, organics, etc., in soil stabilisation [5,6].…”

Section: Introductionmentioning

confidence: 99%

Strength Predictive Modelling of Soils Treated with Calcium-Based Additives Blended with Eco-Friendly Pozzolans—A Machine Learning Approach

2022

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The unconfined compressive strength (UCS) of a stabilised soil is a major mechanical parameter in understanding and developing geomechanical models, and it can be estimated directly by either lab testing of retrieved core samples or remoulded samples. However, due to the effort, high cost and time associated with these methods, there is a need to develop a new technique for predicting UCS values in real time. An artificial intelligence paradigm of machine learning (ML) using the gradient boosting (GB) technique is applied in this study to model the unconfined compressive strength of soils stabilised by cementitious additive-enriched agro-based pozzolans. Both ML regression and multinomial classification of the UCS of the stabilised mix are investigated. Rigorous sensitivity-driven diagnostic testing is also performed to validate and provide an understanding of the intricacies of the decisions made by the algorithm. Results indicate that the well-tuned and optimised GB algorithm has a very high capacity to distinguish between positive and negative UCS categories (‘firm’, ‘very stiff’ and ‘hard’). An overall accuracy of 0.920, weighted recall rates and precision scores of 0.920 and 0.938, respectively, were produced by the GB model. Multiclass prediction in this regard shows that only 12.5% of misclassified instances was achieved. When applied to a regression problem, a coefficient of determination of approximately 0.900 and a mean error of about 0.335 were obtained, thus lending further credence to the high performance of the GB algorithm used. Finally, among the eight input features utilised as independent variables, the additives seemed to exhibit the strongest influence on the ML predictive modelling.

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Section: Introductionmentioning

confidence: 99%

Strength Predictive Modelling of Soils Treated with Calcium-Based Additives Blended with Eco-Friendly Pozzolans—A Machine Learning Approach

2022

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“…In recent years, the MICP method has gained popularity for its potential to reinforce soil particles [5][6][7] in comparison with other ground-improvement practices, which use industrial waste, ashes, agricultural waste, and fibers [8][9][10][11][12]. It is one of the environmentally friendly methods for soil improvement [13].…”

Section: Introductionmentioning

confidence: 99%

Experimental Study on Optimization of Cementation Solution for Wind-Erosion Resistance Using the MICP Method

2022

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In the present study, an environmentally friendly microbial-induced calcium carbonate precipitation (MICP) technique was explored to reinforce the desert sand using the stopped-flow pouring method. A detailed experimental study has been conducted with Sporosarcina (S.) pasteurii urease-producing bacteria with a 0.5 M cementation solution. To optimize the cementation solution, three different pore volumes (PV), i.e., 0.4, 0.6, and 0.8, were considered. The cementation solution was provided every 24 h and considered as one treatment cycle. The cylindrical specimen in three replicas was biotreated for 7, 14, and 21 days in 1:1 and 1:2 (diameter: height) ratios for determina-tion of split-tensile strength (STS) and unconfined compressive strength (UCS), respectively. Micro-structure characterization of untreated and biotreated sand was also examined using a scanning electron microscope (SEM) and energy-dispersive X-ray analysis (EDX). Rocklike behavior was ob-served for biotreated-sand samples using the UPV test. Test results for 21 days with 0.8 PV were 1340 kPa, 241 kPa, and 1762 m/s for UCS, STS, and UPV, respectively, with an average calcite content of 16.2%. Overall, the 0.5 M cementation solution with a 24 h treatment cycle, 0.8 PV with 7 days, and 0.4 PV with 14 days gave optimum treatment solution, and showed heavily cemented and rock-type behavior of the biotreated-sand sample.

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“…This volume change is known as the swelling-shrinkage behavior of expansive subgrades, and hence expansive subgrades are also known as swell-shrink subgrades 1 – 4 . Pavement distresses in roadways are one of the impacts of expansive soil in the subgrades layer 5 – 7 . In a dry state, expansive subgrades initiate shrinkage cracking, which propagates through the pavement system and leads to longitudinal, transverse, and fatigue cracking and rutting in the case of pavement surface 8 .…”

Section: Introductionmentioning

confidence: 99%

An experimental study on strength improvement of expansive subgrades by polypropylene fibers and geogrid reinforcement

Tiwari

Satyam

2022

Sci Rep

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The rapid development of infrastructure often encounters the loose subgrades and is becoming difficult to carry to construction activities. Numerous counteracting methods are developed to control the swelling-shrinkage behavior of the expansive subgrades. The mechanical stabilization of the expansive subgrades by reinforcing with the polypropylene fiber and geogrid is sustainable. Geogrids and polypropylene fibers have been used extensively to strengthen the expansive subgrade and foundations as individuals. The polypropylene fiber reinforcement enhanced the reinforced expansive subgrades's tensile strength capacity, wherein the geogrid reinforcement is the quick fix mechanical stabilization technique, which reduces the pavement failures. In this research, the polypropylene fiber and geogrid reinforcement’s combined effect has been evaluated to stabilize the pavement subgrades. The various mechanical strength test such as unconfined compressive strength (UCS) and large direct shear box test was conducted to evaluate the mechanical interaction between expansive subgrades, polypropylene fiber, triaxial geogrid, and biaxial geogrid at the interface. The polypropylene fiber of 12 mm length was used in the proportion of 0.25%, 0.5%, and 1.0% and single geogrid layer at mid-depth. The result shows that reinforced subgrades’ shear strength with a layer of biaxial/ triaxial geogrid and polypropylene fiber increases by 177%. It is also observed that the unconfined compressive strength of the expansive subgrades increased ranging 3.8–139.6% with the inclusion of polypropylene fiber with geogrid in different combinations. The combined reinforcement method shows an effective treatment methodology to improve the property of expansive subgrades.

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Strength and durability assessment of expansive soil stabilized with recycled ash and natural fibers

Cited by 86 publications

References 57 publications

Strength Predictive Modelling of Soils Treated with Calcium-Based Additives Blended with Eco-Friendly Pozzolans—A Machine Learning Approach

Strength Predictive Modelling of Soils Treated with Calcium-Based Additives Blended with Eco-Friendly Pozzolans—A Machine Learning Approach

Experimental Study on Optimization of Cementation Solution for Wind-Erosion Resistance Using the MICP Method

An experimental study on strength improvement of expansive subgrades by polypropylene fibers and geogrid reinforcement

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