Unconfined Compressive Strength Of Soil Research Articles

Utilisation of Athel Leaves to Improve the Unconfined Compressive Strength of Soil

Athel Leaves (AL) are plentiful agricultural waste that might result in a detrimental effect on the environment owing to inadequate disposal. The purpose of this paper is to experimentally assess the impact of Athel leaves as a novel sustainable waste application on the unconfined compressive strength (UCS) of soil. For the purpose of fulfilling this main objective, five AL percentages (1%, 2%, 3%, 4% and 5% by dry weight of soil) and four curing periods (3, 7, 14 and 28 days) were selected. The failure pattern was also studied to better understand the ultimate behaviour of the improved soil. Assessment of the derived conclusions revealed that the inclusion of AL into soil enhanced the UCS. Careful inspection of the stress-strain relationships showed that inclusion of AL resulted in increased peak stress at large strains. Additionally, it was found that bulging and shear were the two patterns observed in this research. This study confirms the possibility of incorporating AL in geotechnical applications with significant environmental benefits.

Investigating the Soil Unconfined Compressive Strength Based on Laser-Induced Breakdown Spectroscopy Emission Intensities and Machine Learning Techniques.

Laser-induced breakdown spectroscopy (LIBS) is a remarkable elemental identification and quantification technique used in multiple sectors, including science, engineering, and medicine. Machine learning techniques have recently sparked widespread interest in the development of calibration-free LIBS due to their ability to generate a defined pattern for complex systems. In geotechnical engineering, understanding soil mechanics in relation to the applications is of paramount importance. The knowledge of soil unconfined compressive strength (UCS) enables engineers to identify the behaviors of a particular soil and propose effective solutions to given geotechnical problems. However, the experimental techniques involved in the measurements of soil UCS are incredibly expensive and time-consuming. In this work, we develop a pioneering technique to estimate the soil unconfined compressive strength using artificial intelligent methods based on the spectra obtained from the LIBS system. Decision tree regression (DTR) and support vector regression learners were initially employed, and consequently, the adaptive boosting method was applied to improve the performance of the two single learners. The prediction power of the established models was determined using the standard performance evaluation metrics such as the root-mean-square error, CC between the predicted and actual soil UCS values, mean absolute error, and R2 score. Our results revealed that the boosted DTR exhibited the highest coefficient of correlation of 99.52% and an R2 value of 99.03% during the testing phase. To validate the models, the UCS values of soils stabilized with lime and cement were predicted with an optimum degree of accuracy, confirming the models' suitability and generalization strength for soil UCS investigations.

Micro- and nano- bentonite to improve the strength of clayey sand as a nano soil-improvement technique

Nano-additives results in the formation of nano-cementation (NC). This process is recently used to improve the durability of various building materials. NC used to improve the strength of untreated soil materials, also known as nano soil-improvement (NSI). In few years, the role of nano-additives in various types of soils were developed. In this research, the role of micro- and nano- size of bentonite as soil stabilizer was evaluated as first few research to improve geotechnical properties of soils. Nano-additives prepared by micro- and nano- sizes of bentonite were blend with four formulations. These formulations of micro- and nano- additives at concentrations of 0, 1, 2, and 3%, namely 0% Micro-Bentonite, 1% Micro-Bentonite, 2% Micro-Bentonite, 3% Micro-Bentonite, 0% Nano-Bentonite, 1% Nano-Bentonite, 2% Nano-Bentonite, and 3% Nano-Bentonite, respectively. These formulations of micro- and nano- additives were separately added to soil. Specimens with 3% nano-bentonite showed significant improvement in unconfined compressive strength (UCS) of soil that was more than 2.3-times higher than control specimen in 7-d curing time. Also the performance of micro-bentonite resulted in improvement in UCS of soil that was more than 1.1-times higher than control specimen at 7-d curing time. The secant modulus at 50% of peak stress (E50) of the samples treated with micro- and nano- additives increased in comparison to untreated specimens. Further, X-ray fluorescence (XRF), scanning electron microscopy, and X-ray diffraction analyses characterized micro- and nano- structures of soil specimens, and showed the performance of nano-additives in improving strength of soils. Results show that nano-bentonite as a type of nano-additives is an effective means of increasing the strength of soils. This research shows the significant of nano-bentonite in soil improvement, as a NSI technique.

Analysis of Alkali-Induced Soil Heaving in Non-Expansive Soil Using Electrokinetic Model

An attempt has been made in this paper to conduct an Electrokinetic (EK) enhanced large-scale model study to analyze the heaving phenomena observed in fields. The application of the EK technique on fields to study alkali-induced heaving has been simulated in the laboratory using a rectangular and circular model. The EK technique was mainly employed to facilitate alkali soil interaction. Analysis of the geometry of the model boundary on the various physiochemical as well as geotechnical properties of the soil was conducted. Before that, a simple heaving analysis was also performed in an oedometer without the EK technique. Compare to the maximum heaving of 5.55% observed in the oedometer the soil in EK-equipped circular and rectangular models showed the heaving of 5.42% and 4.21% respectively. The heaving pressure recorded for the oedometer was 67.5 t.m-2 while for the circular and rectangular models these values were 37.7 t.m-2 and 18.8 t.m-2 respectively. Further, the value of unconfined compressive strength of soil decreases from 141 kPa to 80 kPa after interaction with alkali and the decrease was more prominent in the circular EK model. However, there was an increase in the friction angle and a decrease in cohesion value after alkali interaction. The structural alteration due to alkali solution was examined by SEM and XRD analysis.

Durability of fly-ash geopolymer binder in deep-mixed expansive soils

This study investigates the durability and leachate behaviour of an alkali-activated fly ash (FA) as a binder to stabilise expansive soils in deep-mixing applications. To activate FA, a liquid alkali activator (LAA) to binder ratio (LAA–FA) was introduced and varied from 1.0 to 1.5. The effects on swelling–shrinkage, consolidation and unconfined compressive strength (UCS) characteristics under wetting–drying cycles (durability) were studied on deep-mixed expansive soils. Leachate studies were conducted to determine the environmental impact of alkali-activated FA. Heavy metal and sodium ion concentrations were measured in each leachate cycle. To examine the permanent microstructural and phase changes in the mixes, scanning electron microscopy (SEM) and x-ray diffraction (XRD) studies were conducted. The swelling–shrinkage behaviour of soil could be marginally controlled for LAA/FA = 1.0 and 1.25 due to heavy weight loss during the wetting–drying cycles. However, even after 12 durability cycles, the UCS of soil with LAA/FA = 1.5 retained 1.0 MPa and showed non-critical swelling–shrinkage behaviour. The heavy metals and sodium ion concentrations were in acceptable range for LAA/FA = 1.25 and 1.5. XRD and SEM studies revealed that soil treated with a binder ratio of LAA/FA = 1.5, showed high crystalline peaks and aggregated structures.

Comparative Investigation on the Curing Behavior of GS-Stabilized and Cemented Soils at Macromechanical and Microstructural Scales

Abstract In recent years, transportation engineering and construction of urban infrastructures have been developing rapidly in China. On the other hand, green construction demands recycling materials from industrial facilities and exploring the potential uses of waste during a second life cycle. Emerging soil stabilized materials, as alternatives of traditional cement materials, greatly improve the recycling of waste materials and the ground performance. Gypsum-slag (GS) curing agent is a new type of water-hardening inorganic gelling material composed of cement, steel slag, mineral slag, desulfurization gypsum, and additive. The unconfined compressive strength of soils stabilized by GS and by cement was investigated in this study. Meanwhile, the microstructure of stabilized soils was detected by means of scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS) and X-ray diffraction (XRD) tests. The relationship between apparent porosity and unconfined compressive strength was established by the Python image processing method. The results show that presence of the GS soil hardening agent changes the microstructure and increases the unconfined compressive strength of the original soils. A colloidal substance is generated to envelop the soil particles and fill the pores when the curing agent and soil are fully mixed and reacted. In comparison, the effect of stabilization generated by GS is proven to be greater than that of cement in view of both microstructure and macroscopic strength.

Editorial: Special Issue on Smart and Green Infrastructures with Optimum Life Cycle Solutions

Editorial: Special Issue on Smart and Green Infrastructures with Optimum Life Cycle Solutions

Reduction, stabilization, and solidification of Cr(VI) in contaminated soils with a sustainable by-product-based binder

This study evaluated the use of a sustainable GFD binder for the stabilization/solidification (S/S) of chromium VI (Cr(VI))-contaminated soil. The GFD binder was composed of ground granulated blast furnace slag (GGBFS), fly ash and desulfurization ash, named after the initials of the three materials. The effects of curing time and binder dosage on soil unconfined compressive strength (UCS), Cr leachability, soil pH, and reduction ratio of Cr (VI) were tested. The immobilization mechanisms of Cr(VI) in contaminated soil were further explored using X-ray diffraction (XRD), scanning electron microscopy (SEM), and sequential extraction procedure (SEP). The results showed that the UCS and pH of the soil increased substantially after the GFD binder was added. After 28 days of curing with a 20% binder dosage, the leached total Cr concentration decreased from 34.4 mg/L in the contaminated soil to 1.44 mg/L in the treated soil, and the leached Cr(VI) concentration decreased from 28.0 mg/L to 0.45 mg/L. A Cr(VI) reduction ratio of 96.2% was achieved, indicating the strong reducibility of GGBFS. XRD revealed that the main hydration products of the GFD binder were hydrated calcium silicate (C–S–H) and ettringite. SEM results showed that the formation of hydration products and Cr-bearing precipitates filled the soil pores, resulting in a dense soil structure. The SEP results demonstrated that the levels of the unstable fraction F1 decreased considerably, and that the levels of the stable fractions F3 and F5 increased after treatment. Encapsulation by C–S–H, reduction by sulfides, adsorption of C–S–H, and precipitation of Cr-bearing hydroxides were the main mechanisms involved in Cr immobilization using the GFD binder.

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

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.

Effect of root system of the Dicranopteris dichotoma on the soil unconfined compressive strength of collapsing walls in hilly granite area of South China

Benggang is a unique soil erosion landform in the subtropical hilly granite area of southern China. The stability of the collapsing walls is directly related to the severity of the Benggang erosion. In this study, four soil layers of the collapsing walls of Benggang of South China were investigated, and the influence of the moisture content and root weight density on the unconfined compressive strength of different soil layers was analyzed. The results indicated the following: With increasing moisture content, the unconfined compressive strength of the four soil layers first increased and then decreased. The unconfined compressive strength followed the sequence of the laterite layer, surface layer, transition layer and sandy layer, in descending order, which demonstrated that the Benggang was stable at the top and unstable at the bottom; furthermore, this type of structure could easily lead to the headward erosion of Benggang. The presence of Dicranopteris dichotoma root systems improved the soil unconfined compressive strength. The optimal root weight density for the four soil layers was 0.75 g/100 cm3. The unconfined compressive strength of the root-soil complex and rootless soil exhibited a positive linear correlation, and the regression equation was y = 1.29x − 8.02 (R2 = 0.999). The moisture content and root weight density obviously interacted and affected the soil unconfined compressive strength. These two factors acted synergistically within a certain range and jointly increased the soil strength. Two-factor regression simulation and variance analysis revealed that the unconfined compressive strength of each soil layer significantly changed (p < 0.05). Models of an unconfined compressive strength attained a satisfactory adaptability, and the optimal moisture content and root weight density combination was obtained for each soil layer. Our conclusions indicated that the influence of the soil-fixing effect of plant roots was limited to shallow soil, other measures should be implemented in deep soil of Benggang.

Impact of Crumb Rubber on Stabilization of Soils

The quantity of waste tires, an undesirable surplus, has been growing each year. The probabilities to take away scrap rubber tires to utilize these as a soil stabilizer, which increases the bearing strength of soil and also improved the bearing capacity of the soil. Trouble related to the socio-financial improvement of a rustic is waste tire decomposition. It has been a hard job to dispose of waste rubber tires safely. In this work, the opportunity of the usage of powder form of rubber tires as a supplement to enhance the shear strength of soft soil became evaluated. The bearing capacity of stabilized soils had been an advanced way of means of growing chances of crumb rubber. Besides this improvement, the effects of this kind of stabilizer and its distinctive portions on effluent traits also are analyzed. The addition of processed waste tires which are powdered form in each soil suggests acceptable adjustments in permeability. This research paper affords the improvement of soils the usage of fragmented waste tires at various proportions. The compressibility and unconfined compression strength of soil had been made used to measure the conduct &amp; overall execution of the improved soils. Along with the test carried out with the soil Specimen, it became found, the highest dry unit weight and most efficient water content, material lower together with a boom in the proportion of stabilizer on soils. The value of shear strength increased with an increase in the proportion of mixtures and the highest values had been found at percentages of 5% &amp; 15% for the specimens, S-1&amp; S-2 severally.

Development of Evaluation Framework for the Unconfined Compressive Strength of Soils Based on the Fundamental Soil Parameters Using Gene Expression Programming and Deep Learning Methods

AbstractThe unconfined compressive strength (UCS) of soils is essential for both researchers and practitioner geotechnical engineers. UCS is well-understood and standardized for laboratory and fiel...

Laser-Induced Spectroscopy-Based Prediction of Soil Unconfined Compressive Strength: A Machine Learning Approach

Laser-Induced Spectroscopy-Based Prediction of Soil Unconfined Compressive Strength: A Machine Learning Approach

Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support vector machines

This study aims to improve the unconfined compressive strength of soils using additives as well as by predicting the strength behavior of stabilized soils using two artificial-intelligence-based models. The soils used in this study are stabilized using various combinations of cement, lime, and rice husk ash. To predict the results of unconfined compressive strength tests conducted on soils, a comprehensive laboratory dataset comprising 137 soil specimens treated with different combinations of cement, lime, and rice husk ash is used. Two artificial-intelligence-based models including artificial neural networks and support vector machines are used comparatively to predict the strength characteristics of soils treated with cement, lime, and rice husk ash under different conditions. The suggested models predicted the unconfined compressive strength of soils accurately and can be introduced as reliable predictive models in geotechnical engineering. This study demonstrates the better performance of support vector machines in predicting the strength of the investigated soils compared with artificial neural networks. The type of kernel function used in support vector machine models contributed positively to the performance of the proposed models. Moreover, based on sensitivity analysis results, it is discovered that cement and lime contents impose more prominent effects on the unconfined compressive strength values of the investigated soils compared with the other parameters.

Experimental study on unconfined compressive and cyclic triaxial test behavior of agar biopolymer–treated silty sand

The need for space for infrastructure development has plummeted studies in ground improvement to an eminent research topic. But in the present day where the need for sustainability is of utmost importance, efficient and environment-friendly improvement methods have been coming up among which the use of biopolymers as a soil additive have gained popularity. In the current research, various strength aspects of agar biopolymer–treated silty sand soil have been explored. The unconfined compressive strength property of treated silty sand at varying agar dosages (0.5%, 1%, and 2%) at different curing periods (3 days, 7 days, and 28 days) was analyzed using laboratory investigations. Furthermore, the effect of frequency of loading on dynamic properties and pore pressure response of the agar-treated silty sand was also investigated by performing a set of strain-controlled cyclic triaxial test at different frequencies (0.2 Hz, 1 Hz, 1.5 Hz, and 2 Hz). The tests were performed at constant axial strain amplitude of 1% and at confining pressure of 100 kPa. The results demonstrated that with the increase in agar content and curing time, the unconfined compressive strength of soil also increased. Besides, a decrease in pore pressure build-up, decrease in secant shear modulus, and an increase in damping was observed on increasing the frequency of cyclic loading.

Comparison of artificial neural network (ANN) and linear regression modeling with residual errors to predict the unconfined compressive strength and compression index for Erbil City soils, Kurdistan-Iraq

The focus of this research is to develop models for predicting the unconfined compressive strength and compression index (Cc) of clay soils according to Atterberg limits, natural water content, dry density, void ratio, and fine content. The soil unconfined compressive strength (UCS) ranged from 24 to 340 kPa and has been quantified with great precision using tested data and data collected from published research studies. The soil compression index ranged from 0.014 to 0.831 which is also correlated by the easily measurable soil properties of Atterberg limits, natural moisture content, density, void ratio, and fine contents (passes no. 200 sieves). A wide experimental test results (a total of 253 tested soils) were combined with more than 350 data collected from different academic research studies, and total data were statistically analyzed and modeled using a linear regression model (LR). According to the correlation determination (R2), mean absolute error (MAE), and the root mean square error (RMSE), the unconfined compressive strength and compression index of soil can be well predicted in terms of liquid limit (LL), plasticity index (PI), moisture content (MC), dry density (γd), void ratio, and percentage passing no. 200 sieve using linear simulation techniques. The performance of the LR models was compared using artificial neural networks (ANN) models with different hidden layers and input layers. Based on the statistical assessments such as R2, RMSE, and MAE the LR models were close to the ANN model to predict the UCS and Cc of the soils. The sensitivity study concludes that the dry density and void ratio are the most influential parameter in determining the unconfined compressive strength and compression index with the training dataset.

Effects of Plastic Waste Materials on Geotechnical Properties of Clayey Soil

Recently, the use of plastic products, such as polyethylene (PE) bottles and polypropylene (PP), has been significantly increased, which may lead to many environmental issues. Therefore, it is important to find methods to manage these waste materials without causing any ecological hazards. One of these methods is to use plastic wastes as soil stabiliser materials. In this study, PE and PP have been used in the form of fibres. The effect of the stabilisation was evaluated through carrying out standard laboratory tests. These tests have been conducted on natural and stabilised soils with four fibre contents (1%, 2%, 3%, and 4%) of the soil weight. The tests included the standard compaction test, unconfined compressive strength (UCS) test, California Bearing Ratio (CBR) test, and resilient modulus (Mr) tests. In all these tests, the fibre content was added in two lengths, which were 1.0 cm and 2.0 cm. Laboratory test results revealed that the plastic pieces decrease maximum dry density (MDD) and optimum moisture content (OMC) of the stabilised soils, which are required for the construction of embankments of lightweight materials. In addition, there was a significant improvement in the UCS of soils by 76.4 and 96.6% for both lengths of PE fibres and 57.4% and 73.0% for both lengths of PP fibres, respectively. Results of the CBR tests demonstrated that the inclusion of plastic fibres in clayey soils improves the strength and deformation behaviour of the soil especially with 4% fibre content for both lengths 1.0 cm and 2.0 cm, respectively, to a figure of 185 to 150% for PE and PP, respectively. Furthermore, the results of the Mr tests demonstrated that the mechanical properties improved to an extent. For an increase in fibre content, the resilient modulus increased by about 120% at 4% fibre content for PE. However, for PP, improvement in resilient modulus declined at 3% fibre content. Therefore, for soil stabilisation with fibre material, optimum fibre content shall be sought.

Soil Unconfined Compressive Strength Prediction Using Random Forest (RF) Machine Learning Model

Aims: Understanding the mechanical performance and applicability of soils is crucial in geotechnical engineering applications. This study investigated the possibility of application of the Random Forest (RF) algorithm – a popular machine learning method to predict the soil unconfined compressive strength (UCS), which is one of the most important mechanical properties of soils. Methods: A total number of 118 samples collected and their tests derived from the laboratorial experiments carried out under the Long Phu 1 power plant project, Vietnam. Data used for modeling includes clay content, moisture content, specific gravity, void ratio, liquid limit and plastic limit as input variables, whereas the target is the UCS. Several assessment criteria were used for evaluating the RF model, namely the correlation coefficient (R), root mean squared error (RMSE) and mean absolute error (MAE). Results: The results showed that RF exhibited a strong capability to predict the UCS, with the R value of 0.914 and 0.848 for the training and testing datasets, respectively. Finally, a sensitivity analysis was conducted to reveal the importance of input parameters to the prediction of UCS using RF. The specific gravity was found as the most affecting variable, following by clay content, liquid limit, plastic limit, moisture content and void ratio. Conclusion: This study might help in the accurate and quick prediction of the UCS for practice purpose.

Prediction of soil unconfined compressive strength using Artificial Neural Network Model

The main objective of the present study is to apply Artificial Neural Network (ANN), which is one of the most popular machine learning models, to accurately predict the soil unconfined compressive strength (qu) for the use in designing of foundations of civil engineering structures. For the development of model, data of 118 soil samples were collected from Long Phu 1 power plant project, Soc Trang Province, Vietnam. The database of physicomechanical properties of soils was prepared for the model study, where 70% data was used for the training and 30% for the testing of the model. Standard statistical indices, namely Root Mean Squared Error (RMSE) and Pearson Correlation Coefficient (R) were used in the validation of the model’s performance. In addition, Partial Dependence Plots (PDP) was used to evaluate the importance of the input variables used for modeling. Results showed that the ANN model performed well for the prediction of the qu (RMSE = 0.442 and R = 0.861). The PDP analysis showed that the liquid limit is the most important input factor for modeling of the qu. The present study demonstrated that the ANN is a promising tool that can be used for quick and accurate prediction of the qu, which can be used in designing the civil engineering structures like bridges, buildings, and powerhouses.

Unconfined compression strength of soil using silica sand waste and dust sand foundry as a stabilizer

The need to improve the subgrade of a highway becomes urgent when the vehicle load exceeds the carrying capacity of the soil. Industrial waste, that increase every day, was proposed in this study as an additive or stabilizer to improve the carrying capacity of the subgrade. Testing was done through laboratory experiments of the mixing original soil with Silica Sand waste and Dust Sand Foundry waste in certain compositions. To test the mechanical properties, the Unconfined Compression Strength Test based on ASTM standards was conducted. The unconfined compression strength (qu) value of the original soil was 0.289 kg/cm2. The highest qu value was shown in the composition of the soil mixture with 2.5% SS and 7.5% DSF which was 0.347 kg/cm2. The qu value increased by 20.07% from the qu value of the original soil, and showed a mechanical improvement in stabilizing soil.