Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches.

Graphene nanoplatelets (GrNs) emerge as promising conductive fillers to significantly enhance the electrical conductivity and strength of cementitious composites, contributing to the development of highly efficient composites and the advancement of non-destructive structural health monitoring techniques. However, the complexities involved in these nanoscale cementitious composites are markedly intricate. Conventional regression models encounter limitations in fully understanding these intricate compositions. Thus, the current study employed four machine learning (ML) methods such as decision tree (DT), categorical boosting machine (CatBoost), adaptive neuro-fuzzy inference system (ANFIS), and light gradient boosting machine (LightGBM) to establish strong prediction models for compressive strength (CS) of graphene nanoplatelets-based materials. An extensive dataset containing 172 data points was gathered from published literature for model development. The majority portion (70%) of the database was utilized for training the model while 30% was used for validating the model efficacy on unseen data. Different metrics were employed to assess the performance of the established ML models. In addition, SHapley Additve explanation (SHAP) for model interpretability. The DT, CatBoost, LightGBM, and ANFIS models exhibited excellent prediction efficacy with R-values of 0.8708, 0.9999, 0.9043, and 0.8662, respectively. While all the suggested models demonstrated acceptable accuracy in predicting compressive strength, the CatBoost model exhibited exceptional prediction efficiency. Furthermore, the SHAP analysis provided that the thickness of GrN plays a pivotal role in GrNCC, significantly influencing CS and consequently exhibiting the highest SHAP value of + 9.39. The diameter of GrN, curing age, and w/c ratio are also prominent features in estimating the strength of graphene nanoplatelets-based cementitious materials. This research underscores the efficacy of ML methods in accurately forecasting the characteristics of concrete reinforced with graphene nanoplatelets, providing a swift and economical substitute for laborious experimental procedures. It is suggested that to improve the generalization of the study, more inputs with increased datasets should be considered in future studies.

Non-linear finite element analysis of SFRC beam-column joints under cyclic loading: enhancing ductility and structural integrity.

Brittle shear failure of beam-column joints, especially during seismic events poses a significant threat to structural integrity. This study investigates the potential of steel fiber reinforced concrete (SFRC) in the joint core to enhance ductility and overcome construction challenges associated with traditional reinforcement. A non-linear finite element analysis (NLFEA) using ABAQUS software was conducted to simulate the behavior of SFRC beam-column joints subjected to cyclic loading. Ten simulated specimens were analyzed to discern the impact of varying steel fiber volume fraction and aspect ratio on joint performance. Key findings reveal that a 2% volume fraction of steel fibers in the joint core significantly improves post-cracking behavior by promoting ductile shear failure, thereby increasing joint toughness. While aspect ratio variations showed minimal impact on load capacity, long and thin steel fibers effectively bridge cracks, delaying their propagation. Furthermore, increasing steel fiber content resulted in higher peak-to-peak stiffness. This research suggests that strategically incorporating SFRC in the joint core can promote ductile shear failure, enhance joint toughness, and reduce construction complexities by eliminating the need for congested hoops. Overall, the developed NLFEA model proves to be a valuable tool for investigating design parameters in SFRC beam-column joints under cyclic loading.

Development of sustainable alkali activated composite incorporated with sugarcane bagasse ash and polyvinyl alcohol fibers.

The infrastructure boom has driven up cement demand to 30 billion tons annually. To address this and promote sustainable construction, researchers are developing solutions for carbon-neutral building practices, aiming to transform industrial waste into an eco-friendly alternative. This study aims to develop and enhance the mechanical and durability properties of alkali-activated composites (AACs) by incorporating varying amounts (5, 10, 15, and 20%) of finely ground bagasse ash (GBA) and polyvinyl alcohol (PVA) fibers. Results indicate that higher GBA content initially reduces the 7th and 14th-day strength but results in increased strength at later ages. The optimum 28-day strength is achieved with a 10% GBA content, leading to a 10% increase in compressive strength, 8% increase in tensile strength, and 12% increase in flexural strength. Additionally, the incorporation of GBA enhanced the resistance of the composite to chloride ingress, thus reducing its conductance and increasing the overall durability. This study demonstrated the potential of GBA as an eco-friendly material, emphasizing the significance of tailored AACs formulations for durable and sustainable construction practices.

Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand.

The use of waste foundry sand (WFS) in concrete production has gained attention as an eco-friendly approach to waste reduction and enhancing cementitious materials. However, testing the impact of WFS in concrete through experiments is costly and time-consuming. Therefore, this study employs machine learning (ML) models, including support vector regression (SVR), decision tree (DT), and AdaBoost regressor (AR) ensemble model to predict concrete properties accurately. Moreover, SVR was employed in conjunction with three robust optimization algorithms: the firefly algorithm (FFA), particle swarm optimization (PSO), and grey wolf optimization (GWO), to construct hybrid models. Using 397 experimental data points for compressive strength (CS), 146 for elastic modulus (E), and 242 for split tensile strength (STS), the models were evaluated with statistical metrics and interpreted using the SHapley Additive exPlanation (SHAP) technique. The SVR-GWO hybrid model demonstrated exceptional accuracy in predicting waste foundry sand concrete (WFSC) strength characteristics. The SVR-GWO hybrid model exhibited correlation coefficient values (R) of 0.999 for CS and E, and 0.998 for STS. Age was found to be a significant factor influencing WFSC properties. The ensemble model (AR) also exhibited comparable prediction accuracy to the SVR-GWO model. In addition, SHAP analysis revealed an optimal content of input variables in the concrete mix. Overall, the hybrid and ensemble models showed exceptional prediction accuracy compared to individual models. The application of these sophisticated soft computing prediction techniques holds the potential to stimulate the widespread adoption of WFS in sustainable concrete production, thereby fostering waste reduction and bolstering the adoption of environmentally conscious construction practices.

Development of innovative alkali activated paste reinforced with polyethylene fibers for concrete crack repair.

Concrete structures are susceptible to cracking, which can compromise their integrity and durability. Repairing them with ordinary Portland cement (OPC) paste causes shrinkage cracks to appear in the repaired surface. Alkali-activated binders offer a promising solution for repairing such cracks. This study aims to develop an alkali-activated paste (AAP) and investigate its effectiveness in repairing concrete cracks. AAPs, featuring varying percentages (0.5%, 0.75%, 1%, 1.25%, 1.5%, and 1.75%) of polyethylene (PE) fibers, are found to exhibit characteristics such as strain hardening, multiple plane cracking in tension and flexure tests, and stress-strain softening in compression tests. AAP without PE fibers experienced catastrophic failure in tension and flexure, preventing the determination of its stress-strain relationship. Notably, AAPs with 1.25% PE fibers demonstrated the highest tensile and flexural strength, exceeding that of 0.5% PE fiber reinforced AAP by 100% in tension and 70% in flexure. While 1% PE fibers resulted in the highest compressive strength, surpassing AAP without fibers by 17%. To evaluate the repair performance of AAP, OPC cubes were cast with pre-formed cracks. These cracks were induced by placing steel plates during casting and were designed to be full and half-length with widths of 1.5 mm and 3 mm. AAP both with and without PE fibers led to a substantial improvement in compressive strength, reducing the initial strength loss of 30%-50% before repair to a diminished range of 2%-20% post-repair. The impact of PE fiber content on the compressive strength of repaired OPC cube is marginal, providing more flexibility in using AAP with any fiber percentage while still achieving effective concrete crack repair. Considering economic and environmental factors, along with observed mechanical enhancements, AAPs show promising potential for widespread use in concrete repair and related applications, contributing valuable insights to the field of sustainable construction materials.

Development of machine learning models for forecasting the strength of resilient modulus of subgrade soil: genetic and artificial neural network approaches.

Accurately predicting the Modulus of Resilience (MR) of subgrade soils, which exhibit non-linear stress-strain behaviors, is crucial for effective soil assessment. Traditional laboratory techniques for determining MR are often costly and time-consuming. This study explores the efficacy of Genetic Programming (GEP), Multi-Expression Programming (MEP), and Artificial Neural Networks (ANN) in forecasting MR using 2813 data records while considering six key parameters. Several Statistical assessments were utilized to evaluate model accuracy. The results indicate that the GEP model consistently outperforms MEP and ANN models, demonstrating the lowest error metrics and highest correlation indices (R2). During training, the GEP model achieved an R2 value of 0.996, surpassing the MEP (R2 = 0.97) and ANN (R2 = 0.95) models. Sensitivity and SHAP (SHapley Additive exPlanations) analysis were also performed to gain insights into input parameter significance. Sensitivity analysis revealed that confining stress (21.6%) and dry density (26.89%) are the most influential parameters in predicting MR. SHAP analysis corroborated these findings, highlighting the critical impact of these parameters on model predictions. This study underscores the reliability of GEP as a robust tool for precise MR prediction in subgrade soil applications, providing valuable insights into model performance and parameter significance across various machine-learning (ML) approaches.

Intelligent prediction modeling for flexural capacity of FRP-strengthened reinforced concrete beams using machine learning algorithms.

Fiber-reinforced polymers (FRP) are widely utilized to improve the efficiency and durability of concrete structures, either through external bonding or internal reinforcement. However, the response of FRP-strengthened reinforced concrete (RC) members, both in field applications and experimental settings, often deviates from the estimation based on existing code provisions. This discrepancy can be attributed to the limitations of code provisions in fully capturing the nature of FRP-strengthened RC members. Accordingly, machine learning methods, including gene expression programming (GEP) and multi-expression programming (MEP), were utilized in this study to predict the flexural capacity of the FRP-strengthened RC beam. To develop data-driven estimation models, an extensive collection of experimental data on FRP-strengthened RC beams was compiled from the experimental studies. For the assessment of the accuracy of developed models, various statistical indicators were utilized. The machine learning (ML) based models were compared with empirical and conventional linear regression models to substantiate their superiority, providing evidence of enhanced performance. The GEP model demonstrated outstanding predictive performance with a correlation coefficient (R) of 0.98 for both the training and validation phases, accompanied by minimal mean absolute errors (MAE) of 4.08 and 5.39, respectively. In contrast, the MEP model achieved a slightly lower accuracy, with an R of 0.96 in both the training and validation phases. Moreover, the ML-based models exhibited notably superior performances compared to the empirical models. Hence, the ML-based models presented in this study demonstrated promising prospects for practical implementation in engineering applications. Moreover, the SHapley Additive exPlanation (SHAP) method was used to interpret the feature's importance and influence on the flexural capacity. It was observed that beam width, section effective depth, and the tensile longitudinal bars reinforcement ratio significantly contribute to the prediction of the flexural capacity of the FRP-strengthened reinforced concrete beam.

Experimental and machine learning approaches to investigate the effect of waste glass powder on the flexural strength of cement mortar.

Using solid waste in building materials is an efficient approach to achieving sustainability goals. Also, the application of modern methods like artificial intelligence is gaining attention. In this regard, the flexural strength (FS) of cementitious composites (CCs) incorporating waste glass powder (WGP) was evaluated via both experimental and machine learning (ML) methods. WGP was utilized to partially substitute cement and fine aggregate separately at replacement levels of 0%, 2.5%, 5%, 7.5%, 10%, 12.5%, and 15%. At first, the FS of WGP-based CCs was determined experimentally. The generated data, which included six inputs, was then used to run ML techniques to forecast the FS. For FS estimation, two ML approaches were used, including a support vector machine and a bagging regressor. The effectiveness of ML models was assessed by the coefficient of determination (R2), k-fold techniques, statistical tests, and examining the variation amongst experimental and forecasted FS. The use of WGP improved the FS of CCs, as determined by the experimental results. The highest FS was obtained when 10% and 15% WGP was utilized as a cement and fine aggregate replacement, respectively. The modeling approaches' results revealed that the support vector machine method had a fair level of accuracy, but the bagging regressor method had a greater level of accuracy in estimating the FS. Using ML strategies will benefit the building industry by expediting cost-effective and rapid solutions for analyzing material characteristics.

Evaluating the effectiveness of waste glass powder for the compressive strength improvement of cement mortar using experimental and machine learning methods.

This study utilized both experimental testing and machine learning (ML) strategies to assess the effectiveness of waste glass powder (WGP) on the compressive strength (CS) of cement mortar. The cement-to-sand ratio was kept 1:1 with a water-to-cement ratio of 0.25. The superplasticizer content was 4% by cement mass, and the proportion of silica fume was 15%, 20%, and 25% by cement mass in three different mixes. WGP was added to cement mortar at replacement contents from 0 to 15% for sand and cement with a 2.5% increment. Initially, using an experimental method, the CS of WGP-based cement mortar at the age of 28 days was calculated. The obtained data were then used to forecast the CS using ML techniques. For CS estimation, two ML approaches, namely decision tree and AdaBoost, were applied. The ML model's performance was assessed by calculating the coefficient of determination (R2), performing statistical tests and k-fold validation, and assessing the variance between the experimental and model outcomes. The use of WGP enhanced the CS of cement mortar, as noted from the experimental results. Maximum CS was attained by substituting 10% WGP for cement and 15% WGP for sand. The findings of the modeling techniques demonstrated that the decision tree had a reasonable level of accuracy, while the AdaBoost predicted the CS of WGP-based cement mortar with a higher level of accuracy. Utilizing ML approaches will benefit the construction industry by providing efficient and economic approaches for assessing the properties of materials.

An Integrated Approach to Using Sheep Wool as a Fibrous Material for Enhancing Strength and Transport Properties of Concrete Composites.

An important goal to achieve sustainable development is to use raw materials that are easily recyclable and renewable, locally available, and eco-friendly. Sheep wool, composed of 60% animal protein fibers, 10% fat, 15% moisture, 10% sheep sweat, and 5% contaminants on average, is an easily recyclable, easily renewable, and environmentally friendly source of raw material. In this study, slump testing, compressive and flexural strengths, ultrasonic pulse velocity, sorptivity, and chloride penetration tests were investigated to assess the influence of wool fibers on the strength and transport properties of concrete composites. Ordinary Portland cement was used to make five concrete mixes incorporating conventional wool fibers (WFs) ranging from 0.5 to 2.5% and a length of 70 mm. The wool fibers were modified (MWFs) via a pre-treatment technique, resulting in five different concrete compositions with the same fiber content. The addition of WF and MWF to fresh concrete mixes resulted in a decrease in slump values. The compressive strength of concrete was reduced when wool fibers were added to the mix. The MWF mixes, however, achieved compressive strength values of more than 30 MPa after a 90-day curing period. Furthermore, by including both WF and MWF, the flexural strength was higher than that of plain concrete. In addition, adding fibers with volume fractions of up to 2% reduced the concrete composite's sorptivity rate and chloride penetration depths for both WF and MWF content mixes. Consequently, biomass waste like sheep wool could be recycled and returned to the field following the circular economy and waste valorization principles.

Scientometric Analysis and Research Mapping Knowledge of Coconut Fibers in Concrete.

Biodegradable materials are appropriate for the environment and are gaining immense attention worldwide. The mechanical properties (such as elongation at break, density, and failure strain) of some natural fibers (such as Coir, Hemp, Jute, Ramie, and Sisal) are comparable with those of some synthetic fibers (such as E glass, aramid, or Kevlar). However, the toughness of coconut fibers is comparatively more than other natural fibers. Numerous studies suggest coconut fibers perform better to improve the concrete mechanical properties. However, the knowledge is dispersed, making it difficult for anyone to evaluate the compatibility of coconut fibers in concrete. This study aims to perform a scientometric review of coconut fiber applications in cementitious concrete to discover the various aspects of the literature. The typical conventional review studies are somehow limited in terms of their capacity for linking different literature elements entirely and precisely. Science mapping, co-occurrence, and co-citation are among a few primary challenging points in research at advanced levels. The highly innovative authors/researchers famous for citations, the sources having the highest number of articles, domains that are actively involved, and co-occurrences of keywords in the research on coconut-fiber-reinforced cementitious concrete are explored during the analysis. The bibliometric database with 235 published research studies, which are taken from the Scopus dataset, are analyzed using the VOSviewer application. This research will assist researchers in the development of joint ventures in addition to sharing novel approaches and ideas with the help of a statistical and graphical description of researchers and countries/regions that are contributing. In addition, the applicability of coconut fiber in concrete is explored for mechanical properties considering the literature, and this will benefit new researchers for its use in concrete.

Enhanced Acoustic Properties of a Novel Prepacked Aggregates Concrete Reinforced with Waste Polypropylene Fibers.

This research aimed to investigate the performance of prepacked aggregates fiber-reinforced concrete (PAFRC) with adequate acoustic characteristics for various applications. PAFRC is a newly developed concrete made by arranging and packing aggregates and short fibers in predetermined formworks, then inserting a grout mixture into the voids amongst the aggregate particles using a pump or gravity mechanism. After a one-year curing period, the effects of utilizing waste polypropylene (PP) fibers on the strength and acoustic characteristics of PAFRC mixes were examined. Compressive and tensile strengths, ultrasonic pulse velocity (UPV), sound absorption, and transmission loss were investigated on plain concrete and PAFRC mixtures comprising 0-1% PP fibers. The results revealed that the use of PP fibers slightly decreased the compressive strength and UPV of PAFRC mixes. The inclusion of waste PP fibers also significantly increased the tensile strength and sound insulation coefficient of PAFRC mixes, especially at higher fiber dosages. In the medium-to-high frequency ranges, more than 60% acoustic absorption coefficient was observed, indicating that PAFRC specimens have good sound insulation properties.

Effects of Sulfate and Sulfuric Acid on Efficiency of Geopolymers as Concrete Repair Materials.

Various geopolymer mortars (GPMs) as concrete repairing materials have become effective owing to their eco-friendly properties. Geopolymer binders designed from agricultural and industrial wastes display interesting and useful mechanical performance. Based on this fact, this research (experimental) focuses on the feasibility of achieving a new GPM with improved mechanical properties and enhanced durability performance against the aggressive sulfuric acid and sulfate attacks. This new ternary blend of GPMs can be achieved by combining waste ceramic tiles (WCT), fly ash (FA) and ground blast furnace slag (GBFS) with appropriate proportions. These GPMs were designed from a high volume of WCT, FA, and GBFS to repair the damaged concretes existing in the construction sectors. Flexural strength, slant shear bond strength, and compatibility of the obtained GPMs were compared with the base or normal concrete (NC) before and after exposure to the aggressive environments. Tests including flexural four-point loading and thermal expansion coefficient were performed. These GPMs were prepared using a low concentration of alkaline activator solution with increasing levels of GBFS and FA replaced by WCT. The results showed that substitution of GBFS and FA by WCT in the GPMs could enhance their bond strength, mechanical characteristics, and durability performance when exposed to aggressive environments. In addition, with the increase in WCT contents from 50 to 70%, the bond strength performance of the GPMs was considerably enhanced under sulfuric acid and sulfate attack. The achieved GPMs were shown to be highly compatible with the concrete substrate and excellent binders for various civil engineering construction applications. It is affirmed that the proposed GPMs can efficiently be used as high-performance materials to repair damaged concrete surfaces.

Recycled Untreated Rubber Waste for Controlling the Alkali-Silica Reaction in Concrete.

Recycled rubber waste (RW) is produced at an alarming rate due to the deposition of 1.5 billion scrap tires annually around the globe, which causes serious threats to the environment due to its open land filling issues. This study investigates the potential application of RW in concrete structures for mitigating the alkali-silica reaction (ASR). Various proportions of RW (5%, 10%, 15%, 20%, and 25%) partially replaced the used aggregates. RW was procured from a local rubber recycling unit. Cubes, prisms, and mortar bar specimens were prepared using a mixture design recommended by ASTM C1260 and tested for evaluating the compressive and flexural strengths and expansion in an ASR conducive environment for specimens incorporating RW. It was observed that the compressive and flexural strength decreased for specimens incorporating RW compared to that of the control specimens without RW. For example, an 18% and an 8% decrease in compressive and flexural strengths, respectively, were observed for specimens with 5% of RW by aggregates volume at 28 days. Mortar bar specimens without RW showed an expansion of 0.23% and 0.28% at 14 and 28 days, respectively, indicating the potential ASR reactivity in accordance with ASTM C1260. A decrease in expansion was observed for mixtures incorporating RW. Specimens incorporating 20% of RW by aggregate volume showed expansions of 0.17% at 28 days, within the limit specified by ASTM C1260. Moreover, specimens incorporating RW showed a lower reduction in compressive and flexural strengths under an ASR conducive environment compared to that of the control specimen without RW. Micro-structural analysis also showed significant micro-cracking for specimens without RW due to ASR. However, no surface cracks were observed for specimens incorporating RW. It can be argued that the use of RW in the construction industry assists in reducing the landfill depositing issues with the additional benefit of limiting the ASR expansion.

Predicting Compressive and Splitting Tensile Strengths of Silica Fume Concrete Using M5P Model Tree Algorithm.

Compressive strength (CS) and splitting tensile strength (STS) are paramount parameters in the design of reinforced concrete structures and are required by pertinent standard provisions. Robust prediction models for these properties can save time and cost by reducing the number of laboratory trial batches and experiments needed to generate suitable design data. Silica fume (SF) is often used in concrete owing to its substantial enhancements of the engineering properties of concrete and its environmental benefits. In the present study, the M5P model tree algorithm was used to develop models for the prediction of the CS and STS of concrete incorporating SF. Accordingly, large databases comprising 796 data points for CS and 156 data records for STS were compiled from peer-reviewed published literature. The predictions of the M5P models were compared with linear regression analysis and gene expression programming. Different statistical metrics, including the coefficient of determination, correlation coefficient, root mean squared error, mean absolute error, relative squared error, and discrepancy ratio, were deployed to appraise the performance of the developed models. Moreover, parametric analysis was carried out to investigate the influence of different input parameters, such as the SF content, water-to-binder ratio, and age of the specimen, on the CS and STS. The trained models offer a rapid and accurate tool that can assist the designer in the effective proportioning of silica fume concrete.

Coupled Effect of Polypropylene Fibers and Slag on the Impact Resistance and Mechanical Properties of Concrete.

The disposal of steel slag leads to the occupation of large land areas, along with many environmental consequences, due to the release of poisonous substances into the water and soil. The use of steel slag in concrete as a sand-replacement material can assist in reducing its impacts on the environment and can be an alternative source of fine aggregates. This is the very first paper that seeks to experimentally investigate the cumulative effect of steel slag and polypropylene fibers, particularly on the impact resistance of concrete. Various concrete mixes were devised by substituting natural sand with steel slag at volumetric replacement ratios of 0%, 10%, 20%, 30%, and 40%, with and without fibers. Polypropylene fibers of 12 mm length were introduced into the steel slag concrete at 0%, 0.5%, and 1.0% by weight of cement as a reinforcing material. Performance evaluation of each mix through extensive experimental testing indicated that the use of steel slag as partial substitution of natural sand, up to a certain optimum replacement level of 30%, considerably improved the compressive strength, flexural strength, and tensile strength of the concrete by 20.4%, 23.8%, and 17.0%, respectively. Furthermore, the addition of polypropylene fibers to the steel slag concrete played a beneficial role in the improvement of strength characteristics, particularly the flexural strength and final drop weight impact energy, which had a maximum rise of 48.1% and 164%, correspondingly. Moreover, integral structure and analytical analyses have also been performed in this study to validate the experimental findings. The results obtained encourage the use of fiber-reinforced steel slag concrete (FRSLC) as a potential impact-resistant material considering the environmental advantages, with the suggested substitution, of an addition ratio of 30% and 1.0% for steel slag and polypropylene fibers, respectively.

Evaluation of Mechanical and Permeability Characteristics of Microfiber-Reinforced Recycled Aggregate Concrete with Different Potential Waste Mineral Admixtures.

Plain recycled aggregate concrete (RAC) struggles with issues of inferior mechanical strength and durability compared to equivalent natural aggregate concrete (NAC). The durability issues of RAC can be resolved by using mineral admixtures. In addition, the tensile strength deficiency of RAC can be supplemented with fiber reinforcement. In this study, the performance of RAC was evaluated with individual and combined incorporation of microfibers (i.e., glass fibers) and various potential waste mineral admixtures (steel slag, coal fly ash (class F), rice husk ash, and microsilica). The performance of RAC mixtures with fibers and minerals was appraised based on the results of mechanical and permeability-related durability properties. The results showed that generally, all mineral admixtures improved the efficiency of the microfibers in enhancing the mechanical performance of RAC. Notably, synergistic effects were observed in the splitting tensile and flexural strength of RAC due to the combined action of mineral admixtures and fibers. Microsilica and rice husk ash showed superior performance compared to other minerals in the mechanical properties of fiber-reinforced RAC, whereas slag and fly ash incorporation showed superior performance compared to silica fume and husk ash in the workability and chloride penetration resistance of RAC. The combined incorporation of microsilica and glass fibers can produce RAC that is notably stronger and more durable than conventional NAC.

A step towards sustainable glass fiber reinforced concrete utilizing silica fume and waste coconut shell aggregate.

Today, it's getting harder to find natural resources for concrete production. Utilization of the waste materials not just helps in getting them used in concrete, cement, and other construction materials, but also has various secondary advantages, for example, saving in energy, decrease in landfill cost, and protecting climate from pollution. Considering this in the development of modern structural design, utilizing waste materials instead of natural aggregate is a good option to make concrete that is sustainable and eco-friendly. The present research aims to find the impact of adding glass fiber into sustainable concrete made with silica fume, as a partial replacement of cement, and coconut shell added with different ratios as a replacement of coarse aggregate, on concrete mechanical and durability aspects. Various blends were made, with coconut shell as a substitution of coarse aggregates with different ratios. Portland cement was substituted with silica fume at 5%, 10%, 15%, and 20% by cement weight in all concrete blends. The volume ratios of glass fibers utilized in this study were 0.5%, 1.0%, 1.5% and 2.0%. Adding glass fibers increases concrete density to some extent and then marginally reduces the density of coconut shell concrete. When the percentage of glass fibers increases, the compressive, flexural and split tensile strength of coconut shell concrete also increases. From the lab results and SEM images of the present research display that glass fibers might be utilized in coconut shell concrete to enhance its mechanical and durability attributes, to accomplish sustainable concrete with acceptable strength with ease.

A Study on the Mechanical Characteristics of Glass and Nylon Fiber Reinforced Peach Shell Lightweight Concrete.

In the current study, the utilization of glass and nylon fibers in various percentages are added to enhance the mechanical performance of peach shell lightweight concrete. Glass and nylon fibers were added at 2%, 4%, 6%, and 8% by cement weight. The results showed that, as we added the glass and nylon fibers, the density of peach shell concrete was reduced by 6.6%, and the compressive, split tensile and flexural strength were enhanced by 10.20%, 60.1%, and 63.49%. The highest strength that was obtained in compressive, split tensile, and flexural strength at 56 days was 29.4 MPa, 5.2 MPa, and 6.3 MPa, respectively, with 6% of glass fiber in peach shell concrete. Mechanical test results showed that post-failure toughness and modulus of elasticity of peach shell concrete is enhanced with the utilization of fibers. To verify our lab results, a statistical analysis, such as response surface methodology, was performed to make a statistical model, it was confirmed by both lab results and statistical analysis that the mechanical performance of peach shell concrete could be significantly improved by adding glass fibers as compared to nylon fibers. With the use of fibers, the water absorption and porosity were slightly increased. Hence, the glass and nylon fibers can be used to improve the peach shell concrete mechanical properties to make concrete eco-friendly, sustainable, and lightweight.

A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash.

Artificial intelligence and machine learning are employed in creating functions for the prediction of self-compacting concrete (SCC) strength based on input variables proportion as cement replacement. SCC incorporating waste material has been used in learning approaches. Artificial neural network (ANN) support vector machine (SVM) and gene expression programming (GEP) consisting of 300 datasets have been utilized in the model to foresee the mechanical property of SCC. Data used in modeling consist of several input parameters such as cement, water-binder ratio, coarse aggregate, fine aggregate, and fly ash (FA) in combination with the superplasticizer. The best predictive models were selected based on the coefficient of determination (R2) results and model validation. Empirical relation with mathematical expression has been proposed using ANN, SVM, and GEP. The efficiency of the models is assessed by permutation features importance, statistical analysis, and comparison between regression models. The results reveal that the proposed machine learning models achieved adamant accuracy and has elucidated performance in the prediction aspect.