California Bearing Ratio Research Articles

Groutability prediction model of coral sand reinforced by excess-sulfate phosphogypsum slag grouting material under permeation grouting

Excess-sulfate phosphogypsum slag grouting material (EPSGM) has strong advantages for stabilizing the coral sand foundation, which requires establishing a groutability prediction model involving multi-factors to ensure better stabilization. Specifically, the grain size (d85) and water-to-cement ratio (w/c) of the grout, particle size (D15), relative density (Dr), and fine content (FC) of coral sand, and grouting pressure (P) are considered. The results show that the optimal particle size d85 of EPSGM is 15.567 μm. As the w/c increases from 1.0 to 3.0, the compressive strength of mortar (EPSGM + coral sand) decreases from 11.74 MPa to 0.32 MPa, and the California Bearing Ratio (CBR) value meets the typical requirements (≥ 15%). The groutability criterion (N) is positively correlated with the D15, w/c, and P, while it is negatively correlated with the Dr and FC, namely, the model writes: N=1−0.071Dr1−1.54FC0.024D15d85+0.42w/c+0.897P−0.86. Particularly, the predictive accuracy can attain a level of 87.50% for systems characterized by varying grain sizes (D15 ≥ 0.431 mm) and densities (Dr ≤ 0.67) of EPSGM stabilizing coral sand foundations. Furthermore, it also applies to other systems in literature that exhibit accuracy ranging from 33.33% to 62.50%. Therefore, the derived predictive model performs greater engineering relevance in informing the selection of grout factors.

Study on the effect of cinder ash as an auxiliary additive mineral in hydrated lime treatment for expansive sub-grade soil in road construction

Expansive soils pose significant challenges to road construction globally due to their tendency to expand and contract with changes in moisture content, leading to pavement failure. In the construction industry, finding an economical and cost-effective alternative to conventional road construction materials is crucial. The use of a natural cinder ash and hydrated lime mixture as reinforcement has proven to be effective in addressing this issue. Cinder ash, a readily available waste material in countries like Ethiopia, is adaptable and water-resistant, making it an ideal choice for reinforcing expansive soil. This article investigates the strength performance of a cinder ash and hydrated lime mixture as a reinforcement material for expansive soil. The study involves mixing 0%, 20%, 25%, 30%, and 35% of cinder ash with soil stabilized with 2–5% hydrated lime. Expansive soil samples are prepared according to the ASSHTO & ASTM method and tested for compressive strength, California bearing ratio, bulk density, and water absorption. The findings reveal a 448.62%, and 374% increase in compressive strength and California Bearing Ratio respectively for the specimen containing 5% hydrated lime and 35% cinder ash, cured for 28 days. The water absorption is minimized at 15.23% for the 5% hydrated lime and 35% cinder ash mixture; while the maximum density of the soil is observed at 1.774 kg/m3 for the optimal combination of 5% hydrated lime and 35% cinder ash mix. The study suggests that using 5% hydrated lime and 35% cinder ash content for the stabilization of expansive soil significantly enhances the strength characteristics of compressed stabilized soil.

The Use of Waste Materials Red Mud and Fly Ash as Road Embankment Fill

This study provides a comprehensive evaluation of red mud as a sustainable material for road base construction, particularly in combination with bottom ash. Red mud, a by-product of the Bayer process used in alumina extraction, is known for its high alkalinity and heavy metal content. For this reason, this waste material causes environmental challenges. Red mud sourced from the Eti Aluminum Factory in Seydişehir, Konya (Turkey), was stabilized with bottom ash. Then, these waste materials were tested through a number of experiments, such as in relation to their Atterberg limits, compaction characteristics, unconfined compressive strength (UCS), California bearing ratio (CBR), and microstructure through a scanning electron microscopy (SEM) analysis. The results highlight that the UCS of stabilized red mud samples significantly improved with the addition of bottom ash and longer curing periods. Specifically, the UCS values increased from 0.5 MPa to 2.5 MPa after 28 days of curing. Moreover, RM specimens stabilized with 25% bottom ash achieved a CBR value of 146.64% after 28 days, far exceeding Turkey’s road fill material requirement, which mandates a minimum unsoaked CBR value of 15%. These findings indicate that red mud stabilized with bottom ash not only meets but exceeds the structural requirements for road base materials. This approach provides a sustainable solution for the environmental management of red mud while contributing to infrastructure development. Through the recycling of these industrial by-products, this study presents a viable method to reduce waste and support economic and environmental sustainability in road construction projects.

Modeling of the effect of gradation and compaction characteristics on the california bearing ratio of granular materials for subbase and landfill liner construction

The California bearing ratio (CBR) of a granular materials are influence by the soil particle distribution indices such as D10, D30, D50, and D60 and also the compaction properties such as the maximum dry density (MDD) and the optimum moisture content (OMC). For this reason, the particle packing and compactibility of the soil play a big role in the design and construction of subbases and landfills. In this research paper, experimental data entries have been collected reflecting the CBR behavior of granular soil used to construct landfill and subbase. The database was utilized in the ratio of 78–22% to predict the CBR behavior considering the artificial neural network (ANN), the evolutionary polynomial regression (EPR), the genetic programming (GP), Extreme Gradient Boosting (XGBoost), Random Forest (RF) and the response surface methodology (RSM) intelligent learning and symbolic abilities. The relative importance values for each input parameter were carried out, which indicated that the (CBR) value depends mainly on the average particle size (D30, 50 & 60). They showed a combined influence index of 66% of the considered parameters in the model exercise. This further shows the importance and structural influence of the particles within the D50 and D60 range in a granular material consistency in the design and construction purposes. Performance indices were also used to study the ability of the models. The ANN model showed the best performance with accuracy of 88%, then GP, EPR and RF with almost the same accuracies of 85% and lastly the XGBoost with accuracy of 81%. Also, the RSM produced an R2 of 0.9464 with a p-value of less than 0.0001. These values show that the ANN produced the decisive model with the superior performance indices in the forecast of CBR of granular material used as subbase and waste compacted earth liner material. The results further show that optimal performance of the CBR depended on D50 and D60 for the design of subgrade, subbase, and liner purposes and also during the performance monitoring phase of the constructed flexible pavement foundations and compacted earth liners.

Efficacy of Acacia Gum Biopolymer in Strength Improvement of Silty and Clay Soils under Varying Curing Conditions

Acacia gum (AG), a polysaccharide biopolymer, has been adopted to improve the strength of three cohesive soils by subjecting them to diverse environmental aging conditions. Being a polysaccharide and a potentially sustainable construction material, the AG yielded flexible film-like threads after 48 h upon hydration, and its pH value of 4.9 varied marginally with the aging of the stabilized soils. The soil samples for the geotechnical evaluation were subjected to wet mixing and were tested under their Optimum Moisture Content (OMC), as determined by the light compaction method. The addition of AG modified the consistency indices of the soils due to the presence of hydroxyl groups in AG, which also led to a rise in OMC and reduction in Maximum Dry Unit weight (MDU). The Unconfined Compressive Strength (UCS) and California Bearing Ratio (CBR) were determined under thermal curing at 333 K as well as on the same day of sample preparation. The least performing condition of the soil’s CBR was evaluated under submerged conditions after allowing the AG-stabilized specimens to air-cure for a period of 1 week. The UCS specimens tested after 7 days were subjected to the initial 7 days of thermal curing at 333 K. A dosage of 1.5% of AG yielded the UCS of 2530 kN/m2 and CBR of 98.3%, respectively, for the low compressible clay (LCC) after subjecting the sample to 333 K temperature for 1 week. The viscosity of the AG was found to be 214.7 cP at 2% dosage. Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), and average particle size determination revealed the filling of pores by AG gel solution, adsorption, and hydrogen bonding, which led to improvements in macroproperties.

Predictive Modelling and Functional Group of Clay Soil Treated with Steel Slag and Calcium Carbide Residue

Swell-shrink subgrade soils like clay are rich in sulphate and exhibit a high rate of volume change by swelling and shrinking during wet and dry cycles, respectively, leading to heaves, cracks, and differential settlement of pavement layers. The aim of this study was to establish predictive simulation models and functional group of clay soil treated with steel slag and calcium carbide residue. Physical and geotechnical tests (such as California bearing ratio (CBR), compaction characteristics, and consistency parameters) were carried out on the unstabilised and stabilised clay soil. Using SPSS statistical software (version 23), multiple linear regression analysis was applied to the experimental data that relate CBR to compaction parameters and additive contents (steel slag and calcium carbide residues). The generated models gave a reliable coefficient of determination, R2 , of 0.990, and 0.998, which can be used to successfully predict both unsoaked and soaked CBR of the stabilised clay soil, respectively. Fourier transform infrared spectroscopy (FTIR) analysis was examined on the clay soil, the additives, and the stabilised clay soils. The FTIR analysis showed weak peaks at 1115 and 1103 cm-1 for all the stabilised clay soils, and this revealed that both additives drastically reduced the sulphate content in the clay soil, which consequently reduced the expansion, cracking, and disintegration rates. As a result, the clay soil's chemical bonding was enhanced through physico- chemical mechanisms.

Application of Gum Arabic on the Geotechnical Properties of Subgrade Materials

This study investigates the impact of Gum Arabic on the geotechnical properties of subgrade soil materials, a non-traditional soil stabilization technique. Given the need for sustainable and locally available alternatives in road construction, the study aims to assess how different percentages of Gum Arabic affect the physical and mechanical behavior of soil. The research aims to provide sustainable and locally available alternatives in road construction. Three soil samples were treated with varying percentages of Gum Arabic, (1.5%, 3%, 6% and 12%), and standard geotechnical tests were conducted under both soaked and unsoaked conditions. The results showed that the average natural moisture content of the soils was 7.9%, 2.2%, and 4.6%. The addition of Gum Arabic increased the peak maximum dry density of the soil samples by 8.02%, 1.88, and 7.88%. The maximum unsoaked California Bearing Ratio (CBR) values of soil samples were 32.1%, 81.7%, and 48.7%, respectively. Whereas, maximum soaked CBR values of soils S1, S2 and S3 obtained at 1.5%, 6% and 1.5% additions of gum Arabic were 8.4%, 28.7% and 16.9% respectively. The study recommends using 3% Gum Arabic to improve the CBR property of soil samples. The application of Gum Arabic showed significant improvements in soil behavior under both soaked and unsoaked conditions.

Prediction of California Bearing Ratio of nano-silica and bio-char stabilized soft sub-grade soils using explainable machine learning

This study investigates the prediction of the California Bearing Ratio (CBR) for nano-silica and bio-char stabilized soft sub-grade soils using explainable machine learning (ML) models. The research involves experimentally determining CBR values for soft sub-grade soils treated with varying proportions of nano-silica and bio-char. This data, along with soil properties such as grain size distribution, moisture content, and nano-silica and bio-char content, serve as inputs for training and testing various ML models. Among the 12 ML models evaluated, the Gradient Boosting Regression exhibits superior performance, achieving high accuracy (R2 = 0.92) and low error rates (MSE = 0.45). The utilization of explainable artificial intelligence (XAI) techniques provides insight into the significant input features influencing CBR predictions, thereby enhancing the interpretability and reliability of the models.. The research findings, highlight the efficacy of machine intelligence in accurately predicting the CBR values of nano-silica and bio-char stabilized soft sub-grade soils. This research has significant implications for geotechnical engineering, offering a data-driven methodology to optimize soil stabilization practices and contribute to sustainable infrastructure development.

Utilization of calcium carbide residue based soil stabilizer in road construction: Strength, environmental impact, and field application

This research paper delves into the utilization of calcium carbide residue (CCR)based stabilizer (designated as CSC) in road subgrade soil stabilization, with a comprehensive evaluation of its mechanical strength, environmental safety, and efficacy in field applications. Amid growing environmental concerns and the push for sustainable construction materials, this study presents an innovative approach by repurposing CCR, a by-product of the acetylene production process, in enhancing the structural integrity of road foundations. Through a series of laboratory tests, the investigation reveals significant improvements in the unconfined compressive strength (UCS) and California bearing ratio (CBR) of CSC-stabilized soils, compared to traditional lime stabilization methods. The field test DCPI results indicate that the CBR value of the field test CSC stabilised soil is 50.19% after three days, whereas the CBR value of the field test lime stabilised soil is only 29.51% after the same period. The CBR value of the CSC stabilised soil is significantly higher than that of the lime stabilised soil. This study also tested the leachate of the stabilised soil, analysing the concentration of heavy metals. The results indicated that the leachate from the stabilised soil complies with heavy metal leaching standards, demonstrating the ecological benefits of CSC utilisation. Furthermore, field experiments conducted on the G312 National Highway in Jiangsu Province, China, further validated the laboratory test results. The findings from the field trials indicated that sections stabilised with CSC exhibited superior performance in terms of reduced soil deformation and enhanced load-bearing capacity. Additionally, a life cycle carbon emission analysis was conducted for the field trial section, comparing the carbon emissions of the CSC binder with those of the lime binder throughout the entire life cycle of road construction. The results from the trial section demonstrated that the use of the CSC binder significantly reduced carbon emissions. The study underscores the potential of CSC as a cost-effective, environmentally friendly stabilizing agent, offering a pragmatic solution to the dual challenges of industrial waste management and soil stabilization in road construction.

Empirical Correlation of Hossaena Town Soil Index Properties with California Bearing Ratio (CBR) Values

California bearing ratio (CBR) is a key input factor in pavement structural design that is used to determine character size, the strength of unbound materials, and subgrade soils. The construction of a straightforward and logical regression model to forecast the CBR of unbound materials as a function of the index properties of the material. This work explains the relationship between compaction parameters and Atterberg limit tests. Laboratory test was conducted to get the independent (percent finer, liquid limit, plastic limit, plastic index, maximum dry density and moisture content), and dependent variables (CBR). A statistical package for social science software (SPSS) used to develop the mathematical model both in single and multiple regression. The viability of using multiple linear regression analysis to relate CBR values to soil index features was investigated. In this study, SPSS is used to examine each independent variable's significance individually. The multiple linear regression analysis was used to create the correlation, which had an eighteen-person sample size and a modest determination coefficient of R2 = 0.821. The CBR has good relation with maximum dry density and optimum moisture content compared to other parameters. This model applicable for small structure in the specified soil type, MH.

Determinants of environmental changes in human-modified ecosystems: Effects of plastics on moisture gradients, nutrients, and clay properties

Plastic pollution poses a significant threat to ecosystem health worldwide. This study examines the determinants of environmental changes in human-modified ecosystems through a quantitative-qualitative system dynamics modeling approach: field experiments conducted on a 310 m2 unsaturated clay-rich bed and a 2.5 m2 clay-rich shore of a plastic-impacted pond in Shenzhen, China, and a 1.17 ha plastic-impacted clay pit in Musanze, Rwanda; laboratory experiments involving Modified Proctor (MP) and California Bearing Ratio (CBR) tests on natural clay reinforced with polyethylene terephthalate (PET) microplastics, with diameters ranging from 0.25 to 5 mm and at concentrations of 1.25 %, 2.5 %, 3.75 %, 5 %, and 10 % by weight of clay; and plastic dynamic flows analyzed by modeling the life cycle of PET. Field experiments showed that mulch type and thickness were critical factors influencing crack distribution in a plastic-impacted pond bed. Specifically, cracks were dominant in areas with pronounced desiccation and lacking filamentous green algae and PET-dominated plastic waste. Along the 2.5 m moisture gradient in a plastic-impacted pond bed, temperature and moisture significantly influenced nutrients, particularly in pronounced desiccation zones. Laboratory experiments showed that microplastics altered the structural properties of natural clay, decreasing moisture content while increasing dry density and load-bearing capacity. The plastic life cycle underscored the roles of industrial and consumer practices, environmental conditions, and waste management and recycling inefficiencies in driving environmental changes in human-modified ecosystems. The findings underscore the need for effective plastic waste management and recycling to mitigate the ecological impacts of plastic pollution in ecosystems.

Impact of delayed compaction on the geoengineering properties of stabilised pond ash

ABSTRACT The impact of delayed compaction on the geoengineering properties of pond ash (PA) treated with geopolymer, Portland cement, and hydrated lime is presented in this paper. The gradation, compacted dry density (CDD), unconfined compressive strength (UCS), California bearing ratio (CBR), hydraulic conductivity, and compressibility index of PA treated with 3%, 9%, and 15% additive contents were evaluated at 0, 3, 6, 12, 24, 48, and 72 h delay periods. The mineralogical and morphological changes in the stabilised material were assessed using X-ray diffraction and scanning electron microscope analysis. The results show an enhancement in the particle size of PA with delay time due to the development of cementitious products and agglomeration of particles. Delay in compaction causes a reduction in dry density and strength properties, whereas hydraulic conductivity and compressibility index increase with delay time. The formation of cementitious products and agglomeration during delay periods leads to improper compaction and deteriorates the mechanical performance. The formations of both sodium-based geopolymer compounds and calcium-based hydration products contribute to the superior geoengineering properties of geopolymer-stabilised PA compared to cement and lime-stabilised PA, which have Ca-based hydration products alone. The developed mathematical models predict the engineering properties of stabilised PA with higher R-square values (>0.90). Based on this study, it is concluded that the geopolymer is more effective as a stabiliser than lime and cement.

Partial replacement of sand by fine-grained crushed waste glass along with fly ash stabilization for geotechnical applications in pavement

Natural sand (NS) is one of the most used engineering materials in almost all types of construction worldwide. Considering environmental sustainability, the replacement of natural sand with crushed glass waste (CWG) can provide a solution for both geo-environmental problems of natural sand depletion and waste glass disposal at a time, since sand and glass share almost similar chemical components. This research aimed to investigate the mechanical behaviors of natural sand replaced with fine CWG particles by 5 %, 10 %, 15 %, 20 %, 25 %, 30 %, and 35 % of dry weight. NS-CWG optimum composition was then stabilized by adding Class C fly ash (FA) from 5 to 35 % of NS-CWG to find out the optimum FA content to stabilize NS-CWG. The effects were analyzed through compaction, direct shear, and California Bearing Ratio (CBR) tests. Test results showed that maximum dry density increased, and optimum moisture content decreased due to adding CWG, and a contrasting effect was found after adding FA. 25 % FA significantly stabilized the NS-CWG mixture's strength parameters, such as increased cohesion, angle of internal friction, improved shear strength by 55.67 %, and increased CBR by 44 %. The decreased roundness index and scanning electronic microscopy (SEM) test results further evaluated and supported the improved geotechnical properties of NS + CWG + FA. The findings of the study can be useful in producing recycled materials for constructing road pavements of sustainable transport network development.

Modeling the service life of temporary airfield operational surfaces under multi-pass aircraft trafficking

Expeditionary Airfield (EAF) surfacing systems are designed to create temporary aircraft operating surfaces. Modeling the service life of EAF surfacing systems including the matting system, aircraft, and subgrade, has historically proven difficult, exacerbated by variability between systems and the multitude of mechanisms that can constitute failure. The study presented herein outlines the development and implementation of a performance modeling approach that includes a multi-scale scheme that accounts for local characteristics of the connection points of the EAF matting system, coupled to the global characteristics of the matting array to predict cyclic passes to failure. Finite element studies were conducted for an EAF surfacing system brickwork configuration subjected to aircraft strut loads over varying California Bearing Ratio (CBR) subgrades to calibrate a transfer function to full-scale trafficking experiments. The proposed framework is then used to predict the rate of subgrade deformation for additional lay patterns, which successfully ranked the performance of each relative to full-scale trafficking experiments. An approach is proposed to couple the rate of subgrade deformation with local finite element models to capture increasing joint damage as permanent deformation accumulates, and supplemented by a variable amplitude cycle counting and damage accumulation algorithm that yields reasonable agreement with full-scale experiments while capturing the transition in failure mechanisms at higher CBR values. The results of the study presented herein captures the propensity for end connector and subgrade failure over a range of subgrade CBRs and shows promise for a broader performance framework that can be extended to other EAF surfacing systems, aircraft types, and specific matting lay patterns.

Resilient roads in challenging terrain: a case study of Siddhartha highway in Nepal

Nepal is a country known for its diverse and challenging topography, and it relies heavily on a robust road infrastructure network to connect its remote regions and urban centers. This study addresses the critical need for enhanced road safety and infrastructure resilience on the Siddhababa road section of the Siddhartha Highway, Nepal, notorious for its high accident rates and susceptibility to landslides. Given the road's strategic importance in connecting remote regions and its challenging topographical conditions, our research aimed to identify the most suitable pavement type to mitigate these issues. Through a detailed examination incorporating eight different soil tests, alongside evaluations of traffic loads, weather conditions, and existing pavement performance, we adopted a comparative analysis methodology to assess the viability of flexible versus rigid pavements within this unique context. Results revealed that the soil composition and environmental conditions of the Siddhababa section significantly influence pavement performance, with specific gravity, moisture content, and California Bearing Ratio (CBR) tests indicating a nuanced suitability for both pavement types under varying circumstances. Our analysis concluded that, despite the economic and staged reinforcement benefits of flexible pavements, the durability, safety, and maintenance considerations favor the adoption of rigid pavement for the Siddhababa road section. However, acknowledging the economic constraints, a hybrid approach is recommended, emphasizing rigid pavements for the most vulnerable sections and flexible pavements elsewhere. This study contributes to the pavement engineering field by providing a model for pavement type selection in mountainous regions, aiming to enhance road safety and durability amidst challenging environmental conditions.

Microstructural analysis and prediction of strength response of black expansive soil

Amelioration studies using waste residues are gaining popularity in the construction sector, as they offer dual effective management and utilization of waste residue. This practice is of interest to the growth of any nation due to environmental protection and the economic advantages it offers. For that reason, this present-day study seeks the sustainable utilization of calcium carbide residue (CCR) and palm oil fuel residue (POFR) in expansive soil amelioration. After an in-depth characterization of the parent soil, several number of California bearing ratio (CBR) tests was performed on the parent and additive-treated soil. For this purpose, a total of twenty mixes were prepared possessing various combinations of CCR, POFR, water, and soil. CBR testing for soaked and unsoaked conditions was carried out to assess the strength performance and effectiveness of the additives. Through this approach, optimal California bearing ratio (CBR) values of 41 and 61% were achieved at trial runs designated Y4 for soaked and unsoaked conditions, indicating a strong mechanical performance. These experimental outcomes were subsequently validated using statistical indicators such as ANOVA (F crit > F) and student t-test (t crit > t stat). A step further, microstructural testing such as transmission electron microscopy (TEM), scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FTIR) were performed on the natural soil and soil treated with the optimal level of additives obtained at trial runs designated Y4. The SEM micrographs evidence the transformation of the loosely packed structure of untreated soil into a dense soil composite. Similarly, diverging molecular functional groups were recorded in the FTIR examination with the incorporation of blended CCR-POFR. Finally, the findings suggest that carefully designed mixtures of CCR and POFR can offer a sustainable, efficient approach to soil amelioration, potentially revolutionizing construction practices.

Evaluating the strength of subgrade soil for pavement design: an analysis using support vector regression model optimized by bayesian algorithm

Determining the strength of subgrade soil, crucial for flexible pavement design, traditionally relies on time-consuming and labour-intensive California Bearing Ratio (CBR) tests. The limitations of linear and non-linear regression-based prediction methods have led to an increased importance of soft computing techniques in providing flexible and consistent CBR predictions for practical engineering. This paper proposes a hybrid model that combines the Bayesian Optimization algorithm (BOA) with support vector regression (SVR) to predict CBR for subgrade soil in pavement design. A database of 238 soil test datasets from northern Algeria was utilised to develop the BOA-SVR model. Using the BOA technique to optimize hyperparameters can improve the model’s performance. The developed hybrid model closely matched experimental results, with a coefficient of determination of 89% and a mean squared error of 5.77, indicating high predictive accuracy. Comparative analysis shows that the BOA-SVR model performs better than conventional and other machine learning models in predicting CBR in tested subgrade soils, in terms of accuracy and robustness. Finally, the optimal subset of features from the initial dataset combined with the BOA-SVR model is determined using an exhaustive feature selection method that includes cross-validation. The results of the experiment indicate that the accuracy of the hybrid model that has been developed is improved by a subset of only seven features from the twelve initial features, this allows for the reduction of computing time and storage space.

Strength, durability, and toxicity characteristics of water treatment sludge based geopolymers for subgrade application

This study examined the strength, durability and toxicity characteristics of geopolymer made from water treatment sludge (WTS) using fly ash (FA) and ground granulated blast furnace slag (GGBS) as binding materials for the subgrade application. The mechanical properties of WTS-FA-GGBS geopolymers were assessed considering various parameters such as binder proportions, curing period, curing condition, Sodium silicate (SS) to Sodium hydroxide (SH) ratio (i.e. SS/SH), and Sodium hydroxide (NaOH) concentration. The results showed that increasing binder proportions, NaOH concentration, and curing period positively impacted the strength of the WTS-FA-GGBS geopolymer. The unconfined compressive strength (UCS) of the geopolymer samples with an SS/SH ratio of 2.5 showed higher compressive strength than those at an SS/SH ratio of 1. WTS-FA-GGBS geopolymers cured at 100°C showed increased UCS values after 1 to 3 days, but strength decreased after 7 days due to a faster geopolymerisation reaction, resulting in excessive moisture loss and microcracks in the geopolymerized samples. The study also found that all the geopolymer samples met the minimum strength criteria for cement-stabilized mix with a maximum mass loss of 8.17 % are well within the specified limits (<14 %) as per IRC:37–2018. The California Bearing Ratio (CBR) values of WTS-FA-GGBS geopolymers, which range from 41.11 % to 260.32 %, surpass the minimum criteria for cement stabilized subgrade or subbase material, as specified by IRC: SP:89–2010. Additionally, the changes during geopolymerization were confirmed by FESEM, EDS, and XRD analysis. The toxicity characteristic leaching procedure (TCLP) test results showed that the amounts of heavy metals in the geopolymer leachate were within the USEPA-mandated limits.

Soil characterization, CBR modeling, and spatial variability analysis for road subgrade: a case study of Danchuwa – Jajere Road, Yobe State, Nigeria

Road construction projects require a thorough understanding of soil properties to ensure the stability and longevity of the infrastructure. This study investigates soil properties along a proposed 34 km road alignment in Yobe State, Nigeria, to characterize soil variability for road construction and develop a predictive model for California Bearing Ratio (CBR). Of the 34 soil samples analyzed, 30 were classified as A-3(1) and four as A-1(1) according to the AASHTO system. Geotechnical testing, including particle size distribution (grading percentages: gravel 0.02%–75.34%, sand 15.5%–90.88%, fines 8.92%–34.84%), Atterberg limits (liquid limits 17%–33%, plastic limits 14%–27%, plasticity index <12%), specific gravity (2.01 to 2.73), compaction (maximum dry density 1.83–2.19 Mg m−3, optimum moisture content 7.29%–14.42%), and CBR tests (values ranging from 5%–62%), were conducted. Correlation analyses revealed strong positive relationships between maximum dry density (r = 0.82) and specific gravity (r = 0.89) with CBR values. Cluster analysis segmented the samples into four distinct groups: Cluster 0 (11 samples), Cluster 1 (9 samples), Cluster 2 (5 samples), and Cluster 3 (9 samples). A linear regression model predicted CBR using maximum dry density and specific gravity (mean squared error = 9.82, R2 = 0.92). Based on CBR criteria, 8 out of 34 samples (CBR 20%–53%) satisfied subbase requirements, while none met the recommended minimum CBR of 80% for base course materials. This study enhances road construction planning through soil variability analysis, effective soil categorization via cluster analysis, and a reliable CBR prediction model. While on-site materials are unsuitable for subgrade and subbase layers, alternative materials or ground improvement techniques are recommended for the base course layer to enhance bearing capacity.

Artificial intelligence-driven enhanced CBR modeling of sandy soils considering broad grain size variability

The soil packing, influenced by variations in grain size and the gradation pattern within the soil matrix, plays a crucial role in constituting the mechanical properties of sandy soils. However, previous modeling approaches have overlooked incorporating the full range of representative parameters to accurately predict the soaked California bearing ratio (CBRs) of sandy soils by precisely articulating soil packing in the modeling framework. This study presents an innovative artificial intelligence (AI)-based approach for modeling the CBRs of sandy soils, considering grain size variability meticulously. By synthesizing extensive data from multiple sources, i.e. extensive tailored testing program undertaking multiple tests and extant literature, various modeling techniques including genetic expression programming (GEP), multi-expression programming (MEP), support vector machine (SVM), and multi-linear regression (MLR) are utilized to develop models. The research explores two modeling strategies, namely simplified and composite, with the former incorporating only sieve analysis test parameters, while the latter includes compaction test parameters alongside sieve analysis data. The models' performance is assessed using statistical key performance indicators (KPIs). Results indicate that genetic AI-based algorithms, particularly GEP, outperform SVM and conventional regression techniques, effectively capturing complex relationships between input parameters and CBRs. Additionally, the study reveals insights into model performance concerning the number of input parameters, with GEP consistently outperforming other models. External validation and Taylor diagram analysis demonstrate the GEP models' superiority over existing literature models on an independent dataset from the literature. Parametric and sensitivity analyses highlight the intricate relationships between grain sizes and CBRs, further emphasizing GEP's efficacy in modeling such complexities. This study contributes to enhancing CBRs modeling accuracy for sandy soils, crucial for pertinent infrastructure design and construction rapidly and cost-effectively.