Prediction of UCS and STS of Kaolin clay stabilized with supplementary cementitious material using ANN and MLR

Awareness of environmental sustainability is driving the shift from conventional Portland cement, a major contributor to carbon dioxide emissions, to more sustainable alternatives. This study focuses on developing a geopolymer concrete by optimizing geopolymer concrete mixtures with various ratios of Ground Granulated Blast Furnace Slag (GGBS) and pulverized fly ash (PFA) as precursors, aiming to find a mix that maximizes strength while minimizing environmental impacts. The precursor was activated using a laboratory-synthesized silica fume (SF)-derived sodium silicate solution in combination with NaOH at a molarity of 10M. This study aims to find the optimal geopolymer concrete mix with a 0.55 water-to-binder ratio, a 0.40 alkali-to-precursor ratio, and a 1:1 sodium silicate to sodium hydroxide ratio. Ordinary Portland cement was used as the control mix binder (C), while the geopolymer mixes included varying GGBS-PFA compositions [CL0 (50% GGBS-50% PFA), CL1 (60% GGBS-40% PFA), CL2 (70% GGBS-30% PFA), CL3 (80% GGBS-20% PFA), and CL4 (90% GGBS-10% PFA)]. The engineering performance of the mixtures was assessed using slump, unconfined compressive strength, split tensile, and flexural strength tests in accordance with their relevant standards. Observations showed that GPC specimens exhibited similar or slightly higher strength values than conventional concrete using PC. In addition to strength, geopolymers have a smaller environmental footprint, consuming less energy and reducing greenhouse gas emissions. These qualities make geopolymer concrete a sustainable construction option that aligns with global efforts to reduce carbon emissions and conserve resources.

The utilisation of end-of-life tires nowadays has become a matter of great distress across the globe when left unattended. These tires have been proved to be a malady on the environment causing severe pollution and health hazards. Hence, the potential use of end-of-life tires has been crucial, especially due to the uncontrolled population rise in urban areas which demands construction on undesirable soil. This paper examines the load–deformation behaviour of rubber fibre–reinforced cemented clayey soil through laboratory tests such as unconfined compressive strength (UCS) and split tensile strength (STS) tests. The clayey soil was stabilised with 3 and 6% cement content, and the inclusion level of rubber fibres was kept at 0, 2.5, 5, 7.5 and 10%. A total of 360 specimens, 180 specimens each for the UCS and STS tests, were prepared in accordance with their optimum moisture content and maximum dry unit weight and cured for 7, 14 and 28 days. The study revealed that adding rubber fibres (up to 2.5%) to clayey soil leads to marginal improvement in UCS and STS. Note that by inclusion of rubber fibres to cemented clayey soil causes a decrease of UCS and STS. Maximum increase of absolute toughness and toughness index of cemented clayey soil was observed with the addition of 7.5% rubber fibres. Test results showed that inspite of the decrease in UCS and STS of cemented clayey soil with the augmentation of rubber fibre content, there were decreases in initial stiffness and improvements on ductility and hardening in the post-peak region. From this study, it could be concluded that rubber fibres up to 7.5% can be incorporated in the cemented clayey soil.

A reasonable knowledge of rock's physical and mechanical properties could save the cost of drilling and production of a reservoir to a large extent by selection of proper operating parameters. In addition, a master development plan (MDP) for each oilfield may contain many enhanced oil recovery procedures that take advantage of rock mechanical data and principles. Thus, an integrated rock mechanical study can be considered an investment in field development. The unconfined compressive strength (UCS) of rocks is the important rock mechanical parameter and plays a crucial role when drilling an oil or gas well. A drilling operation is an interaction between the rock and the bit and the rock will fail when the resultant stress is greater than the rock strength. UCS is actually the stress level at which rock is broken down when it is under a uniaxial stress. It can be used for bit selection, real-time wellbore stability analysis, estimation an optimized time for pulling up the bit, design of enhanced oil recovery (EOR) procedures, and reservoir subsidence studies. Rock strength can be estimated along a drilled wellbore using different approaches, including laboratory tests, core–log relationships, and penetration model approaches. Although this rock strength profile can be used for future investigation of formations around the wellbore, they are actually dead information. Dead rock strength data may not be useful for designing a well in a blind location (infill drilling). Rock strength should be predicted prior to drilling operations. These sort of data are helpful in proposing a drilling program for a new well. In this research, new equations for estimation of rock strength in Ahwaz oilfield are formulated based on statistical analysis. Then, they are utilized for estimation of the rock strength profile of 36 wells in a Middle Eastern oilfield. An artificial neural network is then utilized for prediction of UCS in any predefined well trajectory. Cross-validation tests showed that the results of the network were compatible with reality. This approach has proven to be useful for estimation of any designed well trajectory prior to drilling.

The demand for sustainable concrete is a priority in the current era. The construction industry is actively working to mitigate the environmental impact of cement production in concrete by incorporating alternative and supplementary cementitious materials and reducing carbon emissions. Metakaolin (MK) and rice husk ash (RHA) have become popular in this context due to their pozzolanic properties. This study aims to predict the compressive strength (CS), split tensile strength (STS), and flexural strength (FS) of concrete incorporating MK and RHA with the addition of steel fibers. It examines the influence of several factors, including cement, MK, RHA, water, fine aggregate, coarse aggregate, steel fiber, superplasticizer usage, and concrete age. To achieve this, the study combined experimental work with existing data from the literature. Various machine learning (ML) models, including linear regression, support vector machines, Gaussian process regression, and artificial neural networks (ANN), were employed to analyze and assess the impact on strength characteristics including CS, STS, and FS. The dataset was divided into training and testing subsets, and statistical analyses were conducted to explain the relationships between the input parameters and CS. The ANN model demonstrated superior precision in predicting CS, STS, and FS compared to the other ML models. The findings suggest that the ANN model, utilizing the identified input parameters, can accurately estimate the CS, STS, and FS of concrete with MK, RHA, and steel fibers. The application of such technologies in the construction sector can enable the rapid and cost-effective assessment of material properties and the influence of various input parameters.

Correlation of soil parameters has undeniable benefit in the determination of engineering properties of soil to solve problems in geotechnical Engineering area. The tests were conducted within geotechnical laboratory. These tested soil parameters, used in the correlation analysis are unconfined compressive strength, bulk unit weight and dry unit weight. The aim of this study is proposing a relationship in between the strength parameter with some of the index properties of soils using statistical regression analysis. The linear regression analyses have been done for prediction of unconfined compressive strength (qu ) from bulk and dry unit weight as model-1 and model-2 respectively. And dry unit weight was predicted from bulk unit weight as model-3. Model-4 represents the multiple linear regression analysis to predict qu . The health of developed models is measured by coefficient of determination (R 2) values. Though, model-1, model-2, model-3 and model-4 have R – squared values of 0.9112, 0.9333, 0.9109 and 0.9452 respectively. Therefore, they are correlated strongly and positively. The prediction of unconfined compressive strength of these soils correlated in linear regression, are fairly determined with Model-2 compared with model-1 and model-4 (MLR).

Limited studies have been conducted on low-aluminum and rich-iron-calcium fly ash (LARICFA)-based geopolymer concrete with increased strength. This study aims to investigate the mechanical characteristics of LARICFA-based geopolymer concrete, including its compressive strength, split tensile strength, and ultimate moment. The steps of this study include material preparation and testing, concrete mix design and casting, specimen curing and testing, and the analysis of testing results. Furthermore, the specimen tests consist of the bending, compressive, and split tensile strength tests. The results show that the average compressive strength and the ultimate moment of the geopolymer concrete are 38.20 MPa and 22.90 kN·m, respectively, while the average ratio between the split tensile and compressive strengths is around 0.09. Therefore, the fly ash-based geopolymer concrete can be used in structural components.

The unconfined compressive strength is the most widely used parameter to measure the strength of the subgrade or foundation soil for cohesive soils. Due to its time-consuming and cost-effective nature, most of the time-correlation equations have been used to correlate unconfined compressive strength with compaction parameters and soil index properties. The current study was conducted in Burayu town where fifty soil samples were collected and experimental geotechnical soil tests were carried out based on the American Society for Testing and Materials (ASTM) standards. The correlation and regression analyses were done using the experimental results obtained for unconfined compressive strength (UCS) and compaction characteristics. The regression analysis resulted in a fair coefficient of correlation of 0.61 and 0.78 for single linear regression of UCS with maximum dry density (MDD) and optimum moisture content (OMC), respectively, while R2 = 0.83 for multiple linear regression analysis of UCS with MDD and OMC. After further emphasis, the equation developed using multiple linear regression (UCS = −3105 + 1625 MDD + 40.9 OMC, R2 = 0.83) which was chosen as a prediction equation. After validation of the established model using control tests, the statistical regression analysis shows that the correlation is 97% accurate in the UCS determination for multiple regression analysis. This implies that the established model could be used to predict the UCS in the study area.

In the present investigation, a series of laboratory compaction and unconfined compressive strength laboratory tests has been performed. To determine the effect of compaction energy, type of sand, and fly ash content, compaction tests have been performed with varying compaction energy (2700 kJ/m3- 300 kJ/m3), types of sand, and fly ash content (0% to 40%) respectively. From the experimental results, it has been found that the optimum value of unconfined compressive strength obtained for a sand-fly ash mixture comprised of 65% sand and 35% fly ash. Based on the data obtained in the present investigation, a linear mathematical model has been developed to predict the OMC of sand-fly ash mixture.

Experimental studies on compressive and tensile strength of cement-stabilized soil reinforced with rice husks and polypropylene fibers

Modern strategy for the development of plant protection substances includes computer-aided molecular design as a rational approach used for screening, optimization, and the design of new potent agents in plant protection. Coumarins, secondary plant metabolites, and their derivatives demonstrated a wide range of biological activities on different organisms, as well as their applications in agriculture as eco-friendly plant protection agents. Coumarin derivatives have been reported as strong agents against pathogenic species of fungi. 46 Novel coumarin derivatives have been evaluated against four pathogen fungi (Macropomina phaseolina, Sclerotinia sclerotiorum, Fusarium oxysporum, Fusarium culmorum). Observed compounds have shown activity against two fungi, M. phaseolina, S. sclerotiorum. Quantitative structure-activity relationships (QSAR) analysis has been performed on obtained experimental data. Since the quality models were not obtained by multiple linear regression (MLR), the artificial neural networks (ANN) analysis was performed using four descriptors appearing in the best MLR models. For the antifungal activity against S. sclerotiorum, ANN analysis was performed using descriptors: 3D-MoRSE (Mor19v); Moran autocorrelation (MATS7v); relative negative charge (RNCG AM1); and E-States, the sum of (- CH2 -) (SssCH2), while for M. phaseolina: geometrical symmetry (SYMM2); MATS4m; MATS5m; and the sum of (= C<) (SdssC). Nonlinearities of the best ANN model compared with the linear model improved the coefficient of determination to 76.2 % for S. sclerotiorum and 93.6 % for M. phaseolina, and showed a better external predictive ability 92.44 % and 87.81 %, respectively. ANN could be performed for further research of more effective coumarin agents against the pathogen fungi.

This study examines the potential of high-volume Supplementary Cementitious Materials (SCMs) in advancing sustainable, low-cost, low-carbon Self-Compacting Concrete (SCC) through a quaternary binder design. Ground Granulated Blast Furnace Slag (GGBFS), Class F Fly Ash (FA), and Silica Fume (SF) were incorporated as partial replacements for Ordinary Portland Cement (OPC) to formulate Quaternary Blended Self-Compacting Concrete (QBSCC). Twenty-eight mixes were developed, including one control and twenty-seven quaternary blends, at a constant water-to-binder ratio of 0.4. The SCMs substitution ranged from 42.5% to 77.5%, with GGBFS serving as the dominant component. Rheological compatibility of the quaternary binder composition and superplasticizer dosage was assessed using mini-slump and Marsh cone tests. Fresh-state properties of QBSCC were evaluated through slump flow, L-box, V-funnel, and J-ring tests. Mechanical performance was characterized by compressive, split tensile, and flexural strength tests, and durability was assessed using Rapid Chloride Permeability and electrical resistivity methods. Economic and environmental impacts were quantified via cost analysis and carbon footprint assessment. The results demonstrate that all QBSCC mixtures exhibited superior flowability compared to the reference mix. Compressive strength improved modestly (1– 4%) at 28 days for mixes with 42.5–50% SCMs replacement and more substantially (up to 7%) at 56 days for 42.5–57.5% replacements. Enhancements in tensile and flexural strength followed similar trends. Durability was significantly improved, evidenced by reduced chloride permeability and elevated resistivity. Additionally, QBSCC achieved cost savings of 18– 43% and carbon emission reductions of 38–70%. These outcomes highlight the efficacy of quaternary SCMs systems in producing high-performance, low-carbon SCC with demonstrable technical, economic, and environmental advantages.

This research paper is aimed to briefly highlight the correlation between unconfined compressive strength and point load index for jointed rocks based on joint asperity & orientation. In this observe, specimens were tested to obtain their unconfined compression strength and point load index for a different joint condition. The different joint conditions considered for this study were clean joint and joint filled condition. For both clean joint and joint filled specimens were prepared by various asperity angles of 30°, 45°, 60° and 90° with different orientation angles such as 0°, 30°, 45°, 60°, 90°. Plaster of Paris was used as model material to simulate weak rock mass in the field. By testing intact model specimens for unconfined compressive strength leads to revealing of optimum moisture content for further testing. The curing period for the model specimens is 3 days at room temperature. To simulate jointed rocks, various moulds of different orientation of joint with respect to major principal stress are prepared separately. The inner diameter of the mould is 50 mm and height is 100 mm. After casting, a rough joint was created by cutting the prepared sample using the cutter. The specimens are tested for both clean joint and joint filled condition to determine the favorable joint orientation and asperity angle. After curing, the specimens are tested for unconfined compressive strength and Point load index. The new multi-linear correlation for determining unconfined compressive strength with the help of point load index is developed and cross checked with equations formed for actual rock. On comparing both results it is found that the new equation can suitable for assessing the unconfined compressive strength of limestone and serpentinite rocks through point load index value.

Prediction of intrinsic solubility of generic drugs using MLR, ANN and SVM analyses

Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks

PurposeRecycling construction waste is a promising way towards sustainable development in construction. Recycled aggregate (RA) is obtained from demolished concrete structures, laboratory crushed concrete, concrete waste at a ready mix concrete plant and the concrete made from RA is known as RA concrete. The purpose of this study is to apply multiple linear regressions (MLRs) and artificial neural network (ANN) to predict the mechanical properties, such as compressive strength (CS), flexural strength (FS) and split tensile strength (STS) of concrete at the age of 28 days curing made completely from the recycled coarse aggregate (RCA).Design/methodology/approachMLR and ANN are used to develop a prediction model. The model was developed in the training phase by using data from a previously published research study and a developed model was further tested by obtaining data from laboratory experiments.FindingsANN shows more accuracy than MLR with an R2-value of more than 0.8 in the training phase and 0.9 in a testing phase. The high R2-value indicates strong relation between the actual and predicted values of mechanical properties of RCA concrete. These models will help construction professionals to save their time and cost in predicting the mechanical properties of RCA concrete at 28 days of curing.Originality/valueANN with rectified linear unit transfer function and backpropagation algorithm for training is used to develop a prediction model. The outcome of this study is the prediction model for CS, FS and STS of concrete at 28 days of curing.