Subgrade Soil Research Articles

Investigating the Impact of Ground Granulated Blast Furnace Slag (GGBS) and Cement Kiln Dust (CKD) on fine grained Soil Subgrade in Al Hillah City

Abstract In many parts of the world, fine grained soils are regarded as one of the major concerns for civil engineering projects. To mitigate these problems, fine grained soils are typically replaced with stronger materials. However, due to the high cost of this approach, various researchers have tried alternative approaches, the most popular of which is soil stabilization. This method could be deemed suitable for pavements. The research focuses on stabilizing fine soil through the utilization of cement kiln dust (CKD) and ground granulated blast slag (GGBS). This research involves treating fine soil with 10% of the total binder consist from (GGBS+CKD), with the amount of GGBS to CKD determined by dry soil weight as follows: (10% GGBS + 0% CKD), (8% GGBS + 2% CKD), (6% GGBS + 4% CKD), (2% GGBS + 8% CKD), and (0% GGBS + 10% CKD). The compaction parameters, consistency limitations, and the findings of the Unconfined Compressive Strength (UCS) test were used to assess the enhancement levels. In addition, specimens were subjected to UCS testing after (7-28) days of curing. The findings demonstrated that the combination of GGBS activated by CKD notably enhanced the physical characteristics of fine soil. The Plasticity Index (PI) decreased from 7.4 for untreated soil to 4.8 for a mixture containing 10% binder, comprised of 2% GGBS and 8% CKD, and to 4.25 for a single-binder mixture with 10% CKD. Furthermore, the unconfined compressive strength test (UCS) results indicated that a 10% binder mixture of 8% GGBS and 2% CKD increased the UCS by 2.9 to 5.9 times compared to untreated soil after 7-28 days of curing.

Efficient predictive modeling of resilient modulus in stabilized clayey soil using automated machine learning

Pavement subgrade design relies on the resilient modulus (Mr) to analyze structural response to vehicle-like loading. Adding stabilizers to the soil subgrade makes estimating Mr difficult and resource-intensive. This study uses an automated machine learning (ML) strategy to predict the Mr of stabilized clayey soil using recycled plastic waste. The proposed method automates model selection and hyperparameter tuning, making it a feasible alternative to tedious ML modeling and costly laboratory testing. From extensive laboratory investigation involving 3285 experimental data points, the automated ML model using Bayesian optimization evaluates ensembles, support vector machine (SVM), neural network (NET), decision trees, (TREE), and Gaussian process (GP) regression models, identifying the best model based on cross-validation mean squared error (MSE). Bayesian optimization explores hyperparameter spaces to find optimal configurations, enhancing the accuracy, scalability, and reliability of the prediction model. The optimization process yielded the best results for the ensemble least square boost (LSBoost) model with a cross-validation mean squared error (MSE) value of 6.723×10−29. The optimized ML model’s performance is measured using R2 and adjusted R2. The LSboost model’s R2 and adjusted R2 values of 0.9999 suggested overfitting, prompting further investigations using performance metrics like root mean squared error (RMSE), mean absolute error (MSE), and probability density function (PDF) for normalized absolute error (NAE) for training and testing datasets for the predictive ML model. The small RMSE and MAE (0.0049 and 0.0005) values and symmetrical NAE distribution of the proposed ML model demonstrate its high accuracy and generalization capabilities. The proposed model was subsequently tested on the new, unseen data and achieved predictions with an error rate of 0.24 %. This confirms the proposed ML model’s superiority over conventional deformation models, making it ideal for reliable geotechnical engineering applications.

Effects of 3D Geocells on Flexible Pavement Foundations

Abstract: The current below study examines the effects of adding a Cubic Three-dimensional Geo-synthetics material known as Geo-cells to the foundation layer of flexible Bitumen pavements. In this study, the test of California bearing ratio (CBR) of 5% is used to compare the steel reinforced and unreinforced pavement made sections built on that subgrade. To comprehend the impact of Geo-cell reinforcement upon the pavement section's load-carryings mechanism under static and repetitive loading circumstances, a number of model tests were conducted. The following characteristics were examined: surface deformation profile the test sections, real rut subgrade level, load-settlement response of the pavement sections, and pressure is transmitted into the subgrade soil beneath into the Geo-cell reinforced foundation layer.

Strength Evaluation of Clayey Sand Admixed with Wollostonite with A Special Reference To Construction of Geotextile Reinforced Subgrade

Objectives: To enhance the performance of subgrade constructed using Clayey sand. Clayey sand is a commonly found inferior soil and it is made to be suitable for flexible pavement construction. This study is intended to make Clayey sand suitable by admixing with Wollastonite and embedding with geotextile reinforcement. Methods: The study involved laboratory tests to determine the optimal dosage of Wollastonite to stabilize clayey sand. Tests included Unconfined Compressive Strength (UCS) and California Bearing Ratio (CBR) evaluations. Subsequently, geotextile reinforcement was incorporated with the Wollastonite-stabilized soil to further enhance its strength characteristics. Findings: The research revealed that Clayey sand, in its original form, is unsuitable for subgrade construction due to its poor strength properties. Admixing with Wollastonite significantly improved its strength and expected performance was observed at a specific dosage. Further enhancement was achieved by embedding geotextile reinforcement, which increased both UCS and CBR values. The combination of Wollastonite stabilization and geotextile reinforcement yielded requisite strength for Clayey Sand with respect to strength and stability thus making it suitable for subgrade construction of the flexible pavement. These findings are consistent with existing literature on soil stabilization techniques, but the usage of Wollastonite and geotextile in this context presents a novel approach. The results highlight the potential of this method to address the scarcity of suitable subgrade soils, offering a viable solution for pavement construction projects with inferior soils. Novelty: The study introduces Wollastonite and geotextile reinforcement as effective methods to stabilize and enhance the strength of Clayey sand for subgrade construction in flexible pavements. Keywords: Soil Stabilization, Clayey Sand, Subgrade, Wollostonite, Geotextile, Unconfined Compressive Strength, California Bearing Ratio

Comparative study on in-situ resilient modulus of subgrade estimated using in-situ modulus detector

This study presents a comprehensive evaluation of the resilient modulus of unsaturated subgrade soils using a novel in-situ modulus detector (IMD) along with various laboratory and field-testing methods. The current study employs several testing devices, including bender elements, standard dynamic cone penetrometer, and crosshole-type dynamic cone penetrometer (CDP), to assess the reliability of the IMD in estimating the resilient modulus profiles of small-scale homogeneous subgrade models in unsaturated conditions. The index properties, compaction and strength characteristics of the compacted soils, are evaluated. The resilient modulus values obtained from the IMD are compared with those obtained from repeated load triaxial tests under various stress states. Experimental results show a decrease in the penetration depth per blow with increased dry unit weight. In addition, elastic modulus and shear wave velocity increase with soil density. The resilient modulus estimated from the IMD increase with both depth and soil density. A robust correlation has been established between the resilient modulus values estimated from the IMD and those estimated from the CDP or bender element tests. Furthermore, the resilient moduli estimated from the universal model and the IMD demonstrate linear relationships with a coefficient of determination greater than 0.8. Thus, the IMD may be useful for the in-situ evaluation of resilient modulus in unsaturated subgrade soils, considering influencing factors such as soil density and confining pressure.

Mechanical properties and durability analysis of CS-CG stabilized soil: Towards sustainable subgrade soil enhancement

In this study, carbide slag (CS) and coal gangue (CG) powder were utilized to enhance the properties of the subgrade soil. CS-CG stabilized soil underwent lab experiments to assess its mechanical properties and durability. Tests included unconfined compressive strength (UCS), compressive resilient modulus (CRM), and California bearing ratio (CBR) at stabilizer dosages of 5 %, 10 %, and 15 %. Additional tests, such as dry-wet cycling, salt solution immersion, permeability, leaching, thermogravimetric analysis (TGA), X-ray diffraction (XRD), scanning electron microscope (SEM) and mercury intrusion porosimetry (MIP), were conducted specifically for the 10 % dosage. The mechanical properties and durability were comprehensively analyzed, with a microscopic investigation into pore size. Furthermore, the soil-water characteristic curve (SWCC) of CS-CG stabilized soil is derived through MIP, providing insights into its impact on the material's strength. Results showcased favorable bearing capacity and durability of CS-CG stabilized soil. The optimal mixing dosage is 10 %, with the best ratio being CS: CG= 70: 30. After 6 dry-wet cycles, UCS loss rate was 18.6 %, comparable to 4 % Portland cement (PC) stabilized soil. Dry-wet cycle characteristics surpassed PC and Lime stabilized soils. Immersion in a 5 % NaCl solution for 30 days yielded a UCS of 3.8 MPa at 28-day age, while exposure to 5 % Na2SO4 solution led to an 11.6 % strength decrease compared to NaCl. Permeability coefficient indicated low permeability akin to PC and Lime stabilized soils. Heavy metal content met standards, with minimal increase during cycles. Hydration products mainly comprised C-S-H gel, Ca(OH)2 crystals, and carbonate modification. Analysis suggested capillary and transition pores predominantly, with minimal macropores presence. Dry-wet cycles induced a marginal increase in pore size, with negligible overall impact. SWCC predicted water content (θs) ranged from 30 % to 32 %, with a slight increase in matrix suction during dry-wet cycles. CS-CG stabilized soil shows favorable mechanical properties, durability, and environmental sustainability, indicating its potential as a substitute for traditional cement and lime treatments in subgrade soil reinforcement.

Strength and deformation characteristics of waste mud–solidified soil

The treatment, disposal, and resource utilization of waste mud are challenges for engineering construction. This study investigates the road performance of waste mud–solidified soil and explains how solidifying materials influence the strength and deformation characteristics of waste mud. Unconfined compressive strength tests, consolidated undrained triaxial shear tests, resonant column tests, and consolidation compression tests were conducted to evaluate the solidification effect. The test results show that with an increase in cement content from 5 to 9%, the unconfined compressive strength of the waste mud–solidified soil increased by over 100%, the curing time was extended from 3 to 28 days, and the unconfined compressive strength increased by approximately 70%. However, an increase in initial water content from 40 to 60% reduced the unconfined compressive strength by 50%. With the increase of cement content from 5 to 9%, the cohesion and friction angles increased by approximately 78% and 24%, respectively. The initial shear modulus under dynamic shear increased by approximately 38% and the shear strain corresponding to a damping ratio decay to 70% of the initial shear modulus decreased by nearly 11%. The compression coefficient decreased by approximately 55%. Scanning electron microscopy and X-ray diffraction tests showed that a higher cement content led to the formation of more hydration reaction products, especially an increase in the content of AlPO4, which can effectively fill the pores between soil particles, enhance the bonding between soil particles, and form a skeleton with soil particles to improve compactness. Consequently, the strength of the waste mud–solidified soil increased significantly while its compressibility decreased. This study can provide data support for dynamic characteristics of waste mud solidified soil subgrade.

Effect of Tannery Sludge Ash Additions on the Properties of Subgrade Soil Stabilized by Cement

Effect of Tannery Sludge Ash Additions on the Properties of Subgrade Soil Stabilized by Cement

Design of sustainable bituminous concrete pavement with cement–treated granular layers constructed on stabilized subgrade

Purpose Designing a sustainable bituminous concrete with long-term performance is a challenging problem. In addition, strength of the subgrade has a crucial impact on pavement design. This paper aims to concentrate on subgrade soil stabilization with granite dust powder (GDP) and crumb rubber powder (CRP) to improve the engineering properties of the soil. Further design of bituminous concrete pavement with cement-treated layers in base and subbase course layers was carried out with life cycle cost analysis and life cycle assessment for 1 km of a four-lane national highway. Design/methodology/approach Subgrade soil stabilized with GDP and CRP is characterized as per Indian Standards (IS)-2720 to determine the optimum dosage. Further, the mechanistic-empirical pavement design was carried out using Indian Road Congress-37 (2018), analyzed using IITPAVE software and validated with ANSYS software. The life cycle cost analysis is carried out using the net present value method, and the life cycle assessment is performed according to the cradle-to-grave approach. Findings A soil mix comprising 10% GDP and 2.5% CRP yielded a soaked California bearing ratio value of 6.58%. In addition, the design of bituminous concrete pavement with cement-treated granular layers showed a 26.9% reduction in life cycle cost and 59.4% reduction in total carbon footprint per kilometer compared to the pavement with traditional aggregate layers. Originality/value The research on subgrade stabilization with sustainable materials like GDP and CRP incorporating mechanistic empirical pavement design, life cycle cost analysis and life cycle assessment is limited. Overall, the study recommends the use of GDP and CRP to stabilize soil for subgrade application and incorporate cement-treated granular layers, which offer economic and environmental benefits compared to traditional pavement construction.

Investigation of the parallel gradation method based on response of ballast penetration into subgrade soil by discrete element method

Investigation of the parallel gradation method based on response of ballast penetration into subgrade soil by discrete element method

The Effect of Adding Bamboo Leaf Ash and Cement on the Stability of Clay Soil

Subgrade soil or clay subgrade is a type of soil with very high pore water characteristics, causing problems for civil structures, both buildings and road pavements. Bamboo leaf ash contains several compounds such as silica (SiO2) at 77.24%, while cement contains several calcium oxide (CaO) compounds at 60%. Bamboo leaf ash has a high silica content which functions as a support for pozzolanic reactions with clay soil. The variations in the mixture of bamboo leaf ash and cement used are 2%, 3%, 4%, 5% and 6% for bamboo leaf ash and 20% for cement by weight of soil, with a curing time of 4 days. From the two tests that have been carried out, namely soil compaction (Standard Proctor) and Unsoaked CBR (California Bearing Ratio), the optimum values ​​obtained for soil compaction (Standard Proctor) and CBR (California Bearing Ratio) Unsoaked are in the variation of soil + cement addition of 20% + Bamboo Leaf Ash 3%. In the soil compaction test (Standard Proctor), the dry volume weight value was 1,728 gr/cm3 and the optimum water content value was 16.02%, while in the Unsoaked CBR (California Bearing Ratio) the optimum CBR percentage value was 10.25%.

Investigating the utilization of oxidized textile residue as a strengthening substance in the composition of subgrade soil for pavement construction

<p>The textile industry, vital in the country's industrialization and playing a crucial role in the national economy, generates solid byproducts known as textile sludge. Integrating waste into stabilized soils, along with textile sludge (TS), fly ash (FA), and asphalt additives, offers scientific, economic and sustainability benefits. This study aimed to examine the subsurface solidification technique employing sludge and two supplement additives (asphalt and fly ash additive) implemented on pavement groundwork and supporting layers. The research involved characterization tests and physical soil stabilization with TS and stabilizers (fly ash, and asphalt) at proportions of 2.5%, 5%, 7.5%, 10%, 15%, and 20% as soil additives, which were carried out experimentally. The test results highlighted the potential application of soil-textile sludge mixtures in pavement strata (support and subgrade). The inclusion of fly ash has been proven to be the most effective method for chemically stabilizing the sludge. The maximum dry density was achieved with soil containing 10% textile sludge, supplemented with 15% fly ash. This resulted in a notable increase in UCS value from 235 kN/m2 to 242 kN/m2 and an improvement in CBR value from 4.1% to 9.12%. Furthermore, the addition of 5% bitumen further enhanced the UCS value, reaching 451 kN/m2 and significantly increased the CBR value to 21.6%. Stabilizing textile sludge offers significant environmental benefits by mitigating issues associated with improper waste disposal.</p>

Performance evaluation of marginal materials in geosynthetic reinforced base layers

The high-altitude regions are prone to natural disasters such as landslides, and tunnelling that generate substantial amounts of debris. Utilising these waste (marginal) materials in the pavement sector offers a viable solution. This study aims to stabilise tunnel muck, landslide debris, and Reclaimed Asphalt Pavement using geosynthetics (geogrid, geocell, double geogrid, and geogrid + geocell) for the base course. Cyclic Plate Load tests were performed on pavement prototypes built with available marginal materials and conventional GSB as the base/subbase course, with Black cotton and silty soil subgrades. The results indicated that geosynthetic reinforcement significantly improved the performance of marginal materials by lowering permanent deformation and increasing design life. The pavement prototypes reinforced with double geogrid and geogrid + geocell indicated rutting performance equivalent to conventional GSB for specific combinations. Additionally, incorporating geosynthetics in pavement layers increased the structural strength of the base layers by a layer coefficient ratio factor ranging from 1.1 to 2.5.

Dynamic deformation characteristics of lightweight soil based on Davidenkov model

The dynamic deformation characteristics of lightweight soil were studied using consolidated-undrained dynamic triaxial tests to predict its non-linear stress and strain behaviour under a traffic load. The results showed that the influence of confining pressure on the attenuation of the dynamic shear modulus ratio became significant with increasing expanded polystyrene (EPS) particle content or cement content. The attenuation curve of the dynamic shear modulus ratio was evaluated using Davidenkov model parameters, A, B and γ 0. It was proved that the parameter A was mostly affected by EPS particle content and confining pressure, with its value ranging between 1.1 and 1.85. The parameter γ 0 was affected by cement content and confining pressure, and its value ranged between 0.0007 and 0.0016. The parameter B was found to range between 0.38 and 0.55, in general. The relationship curves between damping ratio and dynamic shear strain revealed two forms, S shaped and bell shaped. The verification tests showed that the Davidenkov model could predict accurately the dynamic deformation characteristics of lightweight soil both under a changing stress state and under constant gradient loading conditions. These results provide a theoretical basis for determining the deformation of a lightweight soil subgrade under a traffic load.

Novel base predictive model of resilient modulus of compacted subgrade soils by using interpretable approaches with graphical user interface

The flexible pavement system consists of layers that are made up of a blend of aggregates and bitumen. To design flexible pavement systems that are both safe and environmentally sustainable, it is essential to have an accurate understanding of the resilient modulus (Mr) of the compacted subgrade soil. Mr refers to the ability of the soil to resist deformation under repeated loads. Thus, a critical parameter affects the performance and longevity of pavement systems. This study employs machine learning (ML) algorithms such as individual and ensemble learners using an extensive database of 2813 data points. These include dry unit weight, weighted plasticity index, confining stress, deviator stress, moisture content, and the number of freeze-thaw cycles (FT). The individual or weak learners were incorporated to create strong and robust ensemble learners by employing techniques such as bagging, adaptive boosting, and random forest (RF). Ensemble learning methods were used to improve the performance of individual learners, such as support vector machine (SVM) and decision tree (DT), by combining their predictions. To achieve the highest R2 value, a total of twenty bagging and boosting sub-models were trained and optimized. The validation of the test data was carried out through K-Fold cross-validation, utilizing metrics such as R2, MAE, and RMSE. The developed models were rigorously tested using statistical indices (MAE, MSE, RMSE, and RMLSE) to verify their predictive accuracy, reliability, and trustworthiness. The findings indicate that the integration of bagging and boosting techniques improves the efficiency of individual machine learning (ML) models. The combination of RF and DT utilizing bagging resulted in the most reliable performance, achieving an R2 value of 0.9 and demonstrating minimum errors. In general, the implementation of the ensemble algorithm in ML improved the overall prediction accuracy of the model. Sensitivity analysis reveals that the prediction of the resilient modulus (Mr) of the subgrade is primarily influenced by dry density, confining stress, and deviator stress. Moreover, a graphical user interface (GUI) is developed for practical implantation.

Modification in Strength Parameter (CBR) of Sub-Grade Soil with Addition of Fly Ash

The determination of the California Bearing Ratio value of soil is tiresome, uneconomical, and time-consuming in the laboratory. Therefore, there is a required automation system to determine the California Bearing Ratio value of soil. Machine learning algorithms are being used for automation systems. In this paper, Artificial Neural Network has been proposed for the prediction of the California Bearing Ratio value of soil. Ash percentage, Liquid Limit, Plastic_Limit, Plasticity index, Shrinkage Limit, MDD and OMC parameters of soil affect the value of the California Bearing Ratio. In the laboratory, the training dataset has generated using these parameters of soil. The proposed classifier has been trained and tested using the training and testing dataset. Experimental results show that the proposed Artificial Neural Network is very accurate to predict California Bearing Ratio values of soil. It is also observed that the linear regression algorithm is very easy and useful to determine the value of the California Bearing ratio depending on seven attributes of soil. The rules generated by J48 and PART can be used to determine the California Bearing ratio. These models are very useful for civil engineers and civil constructors as a California Bearing ratio prediction automation system.

Dynamic response of a permafrost railway subgrade with 3D train-track-subgrade-ground model simulations

Studying train-induced response characteristics is essential for safely operating permafrost railway subgrades. A three-dimensional thermal-mechanical coupling nonlinear dynamic model of train-track-subgrade-ground relationships was established to analyse the train-induced dynamic stress, acceleration and stress path characteristics of a permafrost railway subgrade, and field monitoring data were used to verify this model. The differences between the 2D and 3D models are also discussed, along with the seasonal changes, train speed, axle load, and train type affecting permafrost subgrades. The main results are as follows. First, the vibration load significantly impacts the subgrade 6 m below the sleeper, producing distinct vertical dynamic stress waves due to the wheels and bogies. Dynamic compression stress dominates the subgrade and is influenced by the train structure, speed, and sleeper spacing. While the 2D model tends to underestimate the dynamic stress in shallower layers, it concurs with the 3D model in deeper subgrade dynamics within a 10% margin of error. Then, the principal stress axis of the subgrade soil rotates synchronously with train movements, exhibiting regular stress paths in the YZ plane (longitudinal section) with depth-dependent variations in the stress cycles and deviatoric stress. Finally, predominantly originating from sleeper-induced vibrations, the subgrade vibration acceleration varies with the train speed, sleeper spacing, and season and is most pronounced in the vertical direction. This study provides theoretical guidance for the vibration response of permafrost subgrades on the Qinghai-Tibet Railway (QTR).

Indirect estimation of resilient modulus (Mr) of subgrade soil: Gene expression programming vs multi expression programming

Accurate prediction of resilient modulus (MR) in compacted subgrade soil is crucial for planning secure and environmentally friendly flexible pavement systems. This research assembled a dataset of 2813 data points from twelve compacted soils. The dry density, confining stress, deviator stress, number of freeze-thaw cycles, and moisture content were among the important variables considered for determining the MR. Subsequently, this study employs ensemble machine learning methodologies, specifically gene expression programming (GEP) and multi-expression programming (MEP), to investigate the subject further. The precision and anticipatory proficiency of both the GEP and MEP models are assessed through statistical evaluations, encompassing crucial metrics (R, RMSE, MAE, RSE, RRMSE, and ρ). The GEP and MEP models align well with validation criteria, underscoring their robustness in predicting novel data and showcasing their broad applicability. The GEP model consistently outperformed the MEP model, with higher coefficient of regression (R2) values in both training (0.992 vs. 0.983) and testing (0.981 vs. 0.972) phases, demonstrating its superior predictive accuracy and robustness. In summary, the GEP model consistently outperforms the MEP model in accuracy and prediction, making it the preferred choice for subgrade soil MR prediction. Sensitivity analysis was done, which ranked the parameters by their influence: dry density (26.6 %), confining stress (22.7 %), weighted plasticity index (15.3 %), moisture content (13.5 %), deviator stress (12.5 %), and freeze and thaw cycles (9.4 %). This research aims to enhance the utilization of GEP and MEP in civil engineering for more accurate and efficient MR prediction, ultimately reducing time and costs.

Lightweight rPPU Eco-block Using Recycled Plastic (rP) and Polyurethane (PU) for Soft Subgrade Soil Stabilisation

The stability of the soil plays a crucial role in the safety and longevity of various civil engineering structures. However, problematic soil conditions, such as clay soil and peat soil, often pose challenges due to their high-water content, poor strength, low bearing capacity, and high compressibility. This research focuses on addressing these challenges by utilizing lightweight fill materials as an effective solution for backfilling applications. The study aims to investigate the characteristics and performance of a novel lightweight fill material using recycled plastic (rP) and polyurethane (PU) known as rPPU ecoblock. The eco-block is mainly fabricated using recycled plastic bottles, recycled plastic packaging, and polyurethane, offering a sustainable approach to waste management and construction materials. To achieve the research objectives, a series of experiments will be conducted. The strength and density of plastic bottles filled with recycled plastic packaging will be analyzed, providing insight into their mechanical properties. Furthermore, the water absorption and buoyancy of the lightweight rPPU eco-block are evaluated, considering its suitability for various construction scenarios. The research findings contribute to the understanding of the physical and mechanical characteristics of the rPPU eco-block, providing valuable information for its potential application as a backfill material. The compressive strength, water absorption, and buoyancy tests conducted per ASTM standards provide quantitative data on the eco-block's performance. The use of lightweight fill materials, particularly the rPPU eco-block, offers benefits such as reduced embankment self-weight, improved stability, and minimized environmental impact. Moreover, the recycling of plasticwaste contributes to the circular economy and aligns with sustainable development goals (SDG): SDG9 and SDG11. The results of this research provide valuable insights for engineers, construction professionals, and stakeholders involved in geotechnical and civil engineering projects.

Improvement of soft clay soil with a combination of pre-gelatinized corn starch and nanoparticle agro waste: A multi-objective grey wolf optimizer approach

The present study aimed to assess the potential of a bio-inspired algorithm, multi-objective grey wolf optimization algorithm (MOGWO), to optimize the strength properties (California bearing ratio (CBR) and unconfined compressive strength (UCS)) of an expansive subgrade soil. This optimization process involves the use of two additives, namely a bio-polymer, pregelatinized corn starch (PGCS), and a nanoparticle agro waste, rice husk ash (RHA), blended with the soil in different mix ratios determined by a 32 factorial experimental design. The CBR samples were cured for 7 days, while the UCS samples were cured for 1, 7, and 28 days. To optimize the expansive subgrade soil strength, regression models were developed using PGCS and RHA as predictors for CBR and UCS, serving as fitness functions in the slightly modified MOGWO optimization technique. Next, the optimization analysis produced non-dominated solutions. The results obtained from the laboratory experiments and optimization analysis revealed that there was significant improvement in the UCS and CBR of the soil. These improvements can be attributed to the pozzolanic reaction between the soil-RHA matrix, the formation of intercalated and exfoliated nanocomposites, and the hydrophilic interaction of PGCS. By applying the slightly modified MOGWO technique, the study achieved optimal enhancements in UCS (710.3 kN/m2) and CBR (24.2%) when the expansive subgrade soil was mixed with 0.2637% PGCS and 12.2413% RHA. The results demonstrate the potential of the MOGWO technique in improving the properties of expansive subgrade soil.