Debonding of Thin Bonded Rubberised Fibre-Reinforced Cement-Based Repairs under Monotonic Loading: Experimental and Numerical Investigation.

In this study, the durability of cement-based repairs was observed, especially at the interface of debonding initiation and propagation between the substrate-overlay of thin-bonded cement-based material, using monotonic tests experimentally and numerically. Overlay or repair material (OM) is a cement-based mortar with the addition of metallic fibres (30 kg/m3) and rubber particles (30% as a replacement for sand), while the substrate is a plain mortar without any addition, known as control. Direct tension tests were conducted on OM in order to obtain the relationship between residual stress-crack openings (σ-w law). Similarly, tensile tests were conducted on the substrate-overlay interface to draw the relationship between residual stress and opening of the substrate-overlay interface. Three-point monotonic bending tests were performed on the composite beam of the substrate-overlay in order to observe the structural response of the repaired beam. The digital image correlation (DIC) method was utilized to examine the debonding propagation along the interface. Based on the different parameters obtained through the above-mentioned experiments, a three-point bending monotonic test was modelled through finite elements using a software package developed in France called CAST3M. Structural behaviour of repaired beams observed by experimental results and that analysed by numerical simulation are in coherence. It is concluded from the results that the hybrid use of fibres and rubber particles in repaired material provides a synergetic effect by improving its strain capacity, restricting crack openings by the transfer of stress from the crack. This enhances the durability of repair by controlling propagation of the interface debonding.

Advanced Machine Learning Modeling Approach for Prediction of Compressive Strength of FRP Confined Concrete Using Multiphysics Genetic Expression Programming.

The purpose of this article is to demonstrate the potential of gene expression programming (GEP) in anticipating the compressive strength of circular CFRP confined concrete columns. A new GEP model has been developed based on a credible and extensive database of 828 data points to date. Numerous analyses were carried out to evaluate and validate the presented model by comparing them with those presented previously by different researchers along with external validation comparison. In comparison to other artificial intelligence (AI) techniques, such as Artificial Neural Networks (ANN) and the adaptive neuro-fuzzy interface system (ANFIS), only GEP has the capability and robustness to provide output in the form of a simple mathematical relationship that is easy to use. The developed GEP model is also compared with linear and nonlinear regression models to evaluate the performance. Afterwards, a detailed parametric and sensitivity analysis confirms the generalized nature of the newly established model. Sensitivity analysis results indicate the performance of the model by evaluating the relative contribution of explanatory variables involved in development. Moreover, the Taylor diagram is also established to visualize how the proposed model outperformed other existing models in terms of accuracy, efficiency, and being closer to the target. Lastly, the criteria of external validation were also fulfilled by the GEP model much better than other conventional models. These findings show that the presented model effectively forecasts the confined strength of circular concrete columns significantly better than the previously established conventional regression-based models.

Optimization-Based Economical Flexural Design of Singly Reinforced Concrete Beams: A Parametric Study.

In the past, many studies have been conducted on the optimization of reinforced concrete (RC) structures. These studies have demonstrated the effectiveness of different optimization techniques to obtain an economical design. However, the use of optimization techniques to an obtain economical design is not so practical due to the difficulty in applying most of the optimization techniques to achieve an optimal solution. The RC beam is one of the most common structural elements encountered by a practising design engineer. The current study is designed to highlight the potential of the Solver tool in MS Excel as an easy-to-use option for optimizing the design of simply supported RC beams. A user-friendly interface was developed in a spreadsheet in which beam design parameters from a typical design can be entered and an economical design can be obtained using the Evolutionary Algorithm available in the MS Excel Solver tool. To demonstrate the effectiveness of the developed optimization tool, three examples obtained from the literature have been optimized. The results showed that up to 24% economical solution can be obtained by keeping the same material strengths that were assumed in the original design. However, if material strength is also considered as a variable, up to 44% of the economical solution can be obtained. A parametric study was also conducted to investigate the effect of different design variables on the economical design of simply supported RC beams and to derive useful rules of thumb for their design and proportioning, with the objective of cost minimization. The results of the parametric study suggest that the grade of the reinforcing steel is one of the most influential factors that affect the cost of simply supported RC beams. Practicing engineers can use the trends derived from this research to further refine their optimal designs.

Evaluation of Artificial Intelligence Methods to Estimate the Compressive Strength of Geopolymers.

The depletion of natural resources and greenhouse gas emissions related to the manufacture and use of ordinary Portland cement (OPC) pose serious concerns to the environment and human life. The present research focuses on using alternative binders to replace OPC. Geopolymer might be the best option because it requires waste materials enriched in aluminosilicate for its production. The research on geopolymer concrete (GPC) is growing rapidly. However, substantial effort and expenses are required to cast specimens, cures, and tests. Applying novel techniques for the said purpose is the key requirement for rapid and cost-effective research. In this research, supervised machine learning (SML) techniques, including two individual (decision tree (DT) and gene expression programming (GEP)) and two ensembled (bagging regressor (BR) and random forest (RF)) algorithms were employed to estimate the compressive strength (CS) of GPC. The validity and comparison of all the models were made using the coefficient of determination (R2), k-fold, and statistical assessments. It was noticed that the ensembled SML techniques performed better than the individual SML techniques in forecasting the CS of GPC. However, individual SML model results were also in the reasonable range. The R2 value for BR, RF, GEP, and DT models was 0.96, 0.95, 0.93, and 0.88, respectively. The models' lower error values such as mean absolute error (MAE) and root mean square errors (RMSE) also verified the higher precision of ensemble SML methods. The RF (MAE = 2.585 MPa, RMSE = 3.702 MPa) and BR (MAE = 2.044 MPa, RMSE = 3.180) results are better than the DT (MAE = 4.136 MPa, RMSE = 6.256 MPa) and GEP (MAE = 3.102 MPa, RMSE = 4.049 MPa). The application of SML techniques will benefit the construction sector with fast and cost-effective methods for estimating the properties of materials.

SDS-Aluminum Oxide Nanofluid for Enhanced Oil Recovery: IFT, Adsorption, and Oil Displacement Efficiency.

Surfactant flooding is one of the most promising chemical enhanced oil recovery (CEOR) methods to produce residual oil in reservoirs. Recently, nanoparticles (NPs) have attracted extensive attention because of their significant characteristics and capabilities to improve oil recovery. The aim of this study is to scrutinize the synergistic effect of sodium dodecyl sulfate (SDS) as an anionic surfactant and aluminum oxide (Al2O3) on the efficiency of surfactant flooding. Extensive series of interfacial tension and surfactant adsorption measurements were conducted at different concentrations of SDS and Al2O3 NPs. Furthermore, different surfactant adsorption isotherm models were fitted to the experimental data, and constants for each model were calculated. Additionally, oil displacement tests were performed at 25 °C and atmospheric pressure to indicate the suitability of SDS-Al2O3 for CEOR. Analysis of this study shows that the interfacial tension (IFT) reduction between aqueous phase and crude oil is enhanced considerably by 76%, and the adsorption density of SDS onto sandstone rock is decreased remarkably from 1.76 to 0.49 mg/g in the presence of these NPs. Although the effectiveness of NPs gradually increases with the increase of their concentration, there is an optimal value of Al2O3 NP concentration. Moreover, oil recovery was increased from 48.96 to 64.14% by adding 0.3 wt % NPs to the surfactant solution, which demonstrates the competency of SDS-Al2O3 nanofluids for CEOR.

Plastic Waste Management Strategies and Their Environmental Aspects: A Scientometric Analysis and Comprehensive Review.

Plastic consumption increases with the growing population worldwide and results in increased quantities of plastic waste. There are various plastic waste management strategies; however, the present management progress is not sustainable, and plastic waste dumping in landfills is still the most commonly employed strategy. Being nonbiodegradable, plastic waste dumping in landfills creates several environmental and human health problems. Numerous research studies have been conducted recently to determine safe and ecologically beneficial methods of plastic waste handling. This article performed a bibliographic analysis of the available literature on plastic waste management using a computational approach. The highly used keywords, most frequently cited papers and authors, actively participating countries, and sources of publications were analyzed during the bibliographic analysis. In addition, the various plastic waste management strategies and their environmental benefits have been discussed. It has been concluded that among the six plastic waste management techniques (landfills, recycling, pyrolysis, liquefaction, road construction and tar, and concrete production), road construction and tar and concrete production are the two most effective strategies. This is due to significant benefits, such as ease of localization, decreased greenhouse gas emissions, and increased durability and sustainability of manufactured materials, structures, and roadways. Conversely, using landfills is the most undesirable strategy because of the associated environmental and human health concerns. Recycling has equal benefits and drawbacks. In comparison, pyrolysis and liquefaction are favorable due to the production of char and fuel, but high energy requirements limit their benefits. Hence, the use of plastic waste for construction applications is recommended.

Machine Learning Prediction Models to Evaluate the Strength of Recycled Aggregate Concrete.

Compressive and flexural strength are the crucial properties of a material. The strength of recycled aggregate concrete (RAC) is comparatively lower than that of natural aggregate concrete. Several factors, including the recycled aggregate replacement ratio, parent concrete strength, water-cement ratio, water absorption, density of the recycled aggregate, etc., affect the RAC's strength. Several studies have been performed to study the impact of these factors individually. However, it is challenging to examine their combined impact on the strength of RAC through experimental investigations. Experimental studies involve casting, curing, and testing samples, for which substantial effort, price, and time are needed. For rapid and cost-effective research, it is critical to apply new methods to the stated purpose. In this research, the compressive and flexural strengths of RAC were predicted using ensemble machine learning methods, including gradient boosting and random forest. Twelve input factors were used in the dataset, and their influence on the strength of RAC was analyzed. The models were validated and compared using correlation coefficients (R2), variance between predicted and experimental results, statistical tests, and k-fold analysis. The random forest approach outperformed gradient boosting in anticipating the strength of RAC, with an R2 of 0.91 and 0.86 for compressive and flexural strength, respectively. The models' decreased error values, such as mean absolute error (MAE) and root-mean-square error (RMSE), confirmed the higher precision of the random forest models. The MAE values for the random forest models were 4.19 MPa and 0.56 MPa, whereas the MAE values for the gradient boosting models were 4.78 MPa and 0.64 MPa, for compressive and flexural strengths, respectively. Machine learning technologies will benefit the construction sector by facilitating the evaluation of material properties in a quick and cost-effective manner.

Fly Ash Application as Supplementary Cementitious Material: A Review.

This study aimed to expand the knowledge on the application of the most common industrial byproduct, i.e., fly ash, as a supplementary cementitious material. The characteristics of cement-based composites containing fly ash as supplementary cementitious material were discussed. This research evaluated the mechanical, durability, and microstructural properties of FA-based concrete. Additionally, the various factors affecting the aforementioned properties are discussed, as well as the limitations associated with the use of FA in concrete. The addition of fly ash as supplementary cementitious material has a favorable impact on the material characteristics along with the environmental benefits; however, there is an optimum level of its inclusion (up to 20%) beyond which FA has a deleterious influence on the composite's performance. The evaluation of the literature identified potential solutions to the constraints and directed future research toward the application of FA in higher amounts. The delayed early strength development is one of the key downsides of FA use in cementitious composites. This can be overcome by chemical activation (alkali/sulphate) and the addition of nanomaterials, allowing for high-volume use of FA. By utilizing FA as an SCM, sustainable development may promote by lowering CO2 emissions, conserving natural resources, managing waste effectively, reducing environmental pollution, and low hydration heat.

Predicting the Lateral Load Carrying Capacity of Reinforced Concrete Rectangular Columns: Gene Expression Programming.

This research presents a novel approach of artificial intelligence (AI) based gene expression programming (GEP) for predicting the lateral load carrying capacity of RC rectangular columns when subjected to earthquake loading. To achieve the desired research objective, an experimental database assembled by the Pacific Earthquake Engineering Research (PEER) center consisting of 250 cyclic tested samples of RC rectangular columns was employed. Seven input variables of these column samples were utilized to develop the coveted analytical models against the established capacity outputs. The selection of these input variables was based on the linear regression and cosine amplitude method. Based on the GEP modelling results, two analytical models were proposed for computing the flexural and shear capacity of RC rectangular columns. The performance of both these models was evaluated based on the four key fitness indicators, i.e., coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), and root relative squared error (RRSE). From the performance evaluation results of these models, R2, RMSE, MAE, and RRSE were found to be 0.96, 53.41, 38.12, and 0.20, respectively, for the flexural capacity model, and 0.95, 39.47, 28.77, and 0.22, respectively, for the shear capacity model. In addition to these fitness criteria, the performance of the proposed models was also assessed by making a comparison with the American design code of concrete structures ACI 318-19. The ACI model reported R2, RMSE, MAE, and RRSE to be 0.88, 101.86, 51.74, and 0.39, respectively, for flexural capacity, and 0.87, 238.74, 183.66, and 1.35, respectively, for shear capacity outputs. The comparison depicted a better performance and higher accuracy of the proposed models as compared to that of ACI 318-19.

Forecasting the Mechanical Properties of Plastic Concrete Employing Experimental Data Using Machine Learning Algorithms: DT, MLPNN, SVM, and RF.

Increased population necessitates an expansion of infrastructure and urbanization, resulting in growth in the construction industry. A rise in population also results in an increased plastic waste, globally. Recycling plastic waste is a global concern. Utilization of plastic waste in concrete can be an optimal solution from recycling perspective in construction industry. As environmental issues continue to grow, the development of predictive machine learning models is critical. Thus, this study aims to create modelling tools for estimating the compressive and tensile strengths of plastic concrete. For predicting the strength of concrete produced with plastic waste, this research integrates machine learning algorithms (individual and ensemble techniques), including bagging and adaptive boosting by including weak learners. For predicting the mechanical properties, 80 cylinders for compressive strength and 80 cylinders for split tensile strength were casted and tested with varying percentages of irradiated plastic waste, either as of cement or fine aggregate replacement. In addition, a thorough and reliable database, including 320 compressive strength tests and 320 split tensile strength tests, was generated from existing literature. Individual, bagging and adaptive boosting models of decision tree, multilayer perceptron neural network, and support vector machines were developed and compared with modified learner model of random forest. The results implied that individual model response was enriched by utilizing bagging and boosting learners. A random forest with a modified learner algorithm provided the robust performance of the models with coefficient correlation of 0.932 for compressive strength and 0.86 for split tensile strength with the least errors. Sensitivity analyses showed that tensile strength models were least sensitive to water and coarse aggregates, while cement, silica fume, coarse aggregate, and age have a substantial effect on compressive strength models. To minimize overfitting errors and corroborate the generalized modelling result, a cross-validation K-Fold technique was used. Machine learning algorithms are used to predict mechanical properties of plastic concrete to promote sustainability in construction industry.

Recycling of Waste Facial Masks as a Construction Material, a Step towards Sustainability.

Amid the COVID-19 pandemic, a sudden surge in the production and utilization of disposable, single-use facial masks has been observed. Delinquency in proper disposal of used facial masks endangers the environment with a new form of non-biodegradable plastic waste that will take hundreds of years to break down. Therefore, there is an urgent need for the resourceful recycling of such waste in an environmentally friendly way. This study presents an efficient solution by using waste masks in fibered or crushed form to produce environmentally friendly and affordable green concrete. This investigation assessed the mechanical and durability properties of waste masks-incorporated concrete. A total of six mixes were prepared for standardized tests to determine compressive strength, split cylinder tensile strength and rapid chloride penetration test (RCPT), and freeze-thaw resistance. The percentage of mask fibers used were 0.5, 1, 1.5, and 2% of concrete by volume, while crushed masks were used at 0.5% only. The mask waste in both forms was found suitable to be used in concrete. One percent of waste mask fibers was found as an optimum value to increase compressive and tensile strength, reduce chloride permeability, and increase freeze-thaw resistance. Besides this, 0.5% crushed mask fiber also performed well, especially for producing less permeable and highly durable concrete. It is thus corroborated that waste masks that increase pollution worldwide can be utilized sustainably to help build green buildings. By reutilizing waste masks to produce improved concrete with better strengths and higher durability, circular economy and sustainability are achieved, along with efficient waste management.

Effect of Silicon Carbide and Tungsten Carbide on Concrete Composite.

Flexural strength of concrete is an important property, especially for pavements. Concrete with higher flexural strength has fewer cracking and durability issues. Researchers use different materials, including fibers, polymers, and admixtures, to increase the flexural strength of concrete. Silicon carbide and tungsten carbide are some of the hardest materials on earth. In this research, the mechanical properties of carbide concrete composites were investigated. The silicon carbide and tungsten carbide at different percentages (1%, 2%, 3%, and 4%) by weight of cement along with hybrid silicon carbide and tungsten carbide (2% and 4%) were used to produce eleven mixes of concrete composites. The mechanical tests, including a compressive strength test and flexural strength test, along with the rapid chloride permeability test (RCPT), were conducted. It was concluded that mechanical properties were enhanced by increasing the percentages of both individual and hybrid carbides. The compressive strength was increased by 17% using 4% tungsten carbide, while flexural strength was increased by 39% at 4% tungsten carbide. The significant effect of carbides on flexural strength was also corroborated by ANOVA analysis. The improvement in flexural strength makes both carbides desirable for use in concrete pavement. Additionally, the permeability, the leading cause of durability issues, was reduced considerably by using tungsten carbide. It was concluded that both carbides provide promising results by enhancing the mechanical properties of concrete and are compatible with concrete to produce composites.

Effectiveness of Ternary Blend Incorporating Rice Husk Ash, Silica Fume, and Cement in Preparing ASR Resilient Concrete.

Although the disposal of waste ashes causes environmental hazards, recycling them helps in reducing their harmful impacts and improves the characteristics of building materials. The present study explores the possible use of locally available waste ashes including Rice husk ash (RHA)and Silica Fumes (SF) as a partial replacement for cement in concrete to counter the negative impact of alkali-silica reactions (ASRs). In the present study, ternary blends including RHA (0-30%), SF (5% and 10%) and Portland cement were investigated. The amorphous behavior of RHA and SF was confirmed by conducting an X-ray diffraction analysis. A petrography analysis was carried out to ensure the reactive nature of aggregates used to prepare the concrete specimen. Accelerated mortar bar tests were performed in accordance with ASTM C 1260 for up to 90 days. It was revealed that specimens incorporating a ternary blend of SF, RHA, and Portland cement exhibited less expansion compared to the control specimens without SF and RHA. The incorporation of 5% SF along with 20% RHA exhibited a 0.13% expansion at 28 days and 10% SF, along with 5% RHA which exhibited 0.18% expansion at 28 days which is within the range specified by ASTM C 1260, with the lowest compromise of the mechanical properties of concrete. Thus, the utilization of SF and RHA in the partial replacement of cement in concrete may be considered a practical approach to mitigate ASR effects as well as to reduce the environmental burden.

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.