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This article discusses the simultaneous influence of thermophoresis, Brownian motion, and the dispersion of nanoparticles on thermal enhancement in non-Newtonian fluid existing over a surface stretching with non-uniform velocity. The modeled problems describing the transport of heat by the fluid are boundary value problems and are solved by the finite element method (FEM). The mesh-free analysis is done and accuracy is checked. The numerical solutions are computed and used for depicting flow field behaviors. It is visualized that Cu− Prandtl fluid and Cu−Al2O3− Prandtl fluid flow are affected in opposing ways by Prandtl and elastic factors. As a result, momentum in the Newtonian fluid does not penetrate as quickly as that does in Prandtl fluid. The Brownian motion parameter is responsible for a decrease in the Nusselt number whereas the thermophoresis parameter causes a decrease in the Sherwood number. Thus, it is essential to note that the wall heat transfer rate in the fluid can be enhanced by making arrangements to reduce Brownian motion. Similarly, wall mass flux can be controlled through the thermophoresis parameter. Nusselt and Sherwood number has shown an increasing tendency when the Prandtl parameter is increased. Thus, fluids with higher Prandtl fluid parameters are suitable for higher wall heat and mass fluxes. The temperature of mono and hybrid nano-Prandtl fluids is increased by caused by thermophoresis and Brownian motion.

Improper disposal of plastic waste (P-waste) poses significant pollution and health risks to the environment. Additionally, steel rebar corrosion in concrete structures reduces their strength and serviceability. To address these issues, this study explores using glass fiber-reinforced polymer (GFRP) rebars and replacing natural coarse aggregates with P-waste aggregates for sustainable and eco-friendly construction. This work investigates the axial performance of polypropylene structural fiber-reinforced P-waste aggregate concrete (SFPC) compressive components having GFRP-reinforcement (GSFPC) under various loading conditions. For comparison, steel-reinforced SFPC compressive components (SSFPC) were also fabricated. Eighteen circular components with 1200 mm height and 300 mm diameter were tested and compared under different loading conditions. SSFPC components exhibited up to 21.9 % higher axial strength but up to 27.4 % lower ductility compared to GSFPC components. Eccentric loading similarly reduced axial strength in both GSFPC and SSFPC components. A 3-D finite element analysis (FEA) of GSFPC components was proposed using a modified damaged plastic model for SFPC which showed deviations of 2.3 % in axial strength and 7.7 % in equivalent axial shortening, demonstrating a good accuracy of the FEA model.

Motile gyrotactic organism activity in micropolar fluid subjected to simultaneous heat and mass transfer in the presence of thermal diffusion and diffusion thermo effects is modeled. The base fluid is taken as kerosene oil. The immersion of nano-sized particles of Cu−TiO2 kerosene oil makes its rheological characteristics of micropolar fluid because nano-sized particles serve as micro-structures. In this case couple stress, vortex and spin gradient viscosities become significant and simultaneously linear and angular momenta become equally remarkable. So, micropolar theory is the best to model the immersion of nano-structures in the fluid. Similarity variables are used to transform the problems into their dimensionless forms. The mathematical models are solved numerically by applying the finite element method (FEM). The results are validated and numerical experiments are performed to analyze the key parameters of the unknown field variables. In order to analyze the impact of physical parameters on field variables, numerous simulations are performed. The micro-rotation of microparticles and nanoparticles due to fluid deformation motivates fluids particles to rotate faster. Therefore, linear motion is observed to be increased for higher values of vortex viscosity. The curvature parameter has significantly impacted the linear motion of the fluid particles. The impact of the curvature parameter on the movement of nanofluid is lesser than the impact of the curvature parameter on the motion of hybrid nanoparticles. Concentration gradient supports the diffusion of heat in the fluid regime and numerical experiments with variation of Dufour (that measures the effects of concentration gradient on heat diffusion) have shown that concentration gradient is more effective in the case of hybrid nanofluid relative to the mono nanofluid. The angular motion of fluid particles increases when vortex viscosity is increased. Numerical simulations have predicted that the angular motion of particles of hybrid nanofluid has a lesser angular speed than that of the angular motion of particles of mono nanofluid. The Soret number determines the impact of temperature gradient on the diffusion of solute in the fluid. Simulations for various values of Soret number have predicted that the impact of temperature gradient on the diffusion of solute in hybrid nanofluid is stronger than the impact of temperature gradient on the diffusion of solute in mono nanofluid.

This article models the diffusion process of heat and mass transfer in synovial fluid (SF) which is viscous non-Newtonian and is present in the articular cartilage of the synovial joint. The viscosity of SF is a function of the concentration of hyaluronan (HA) and shear rate. The physicochemical properties of HA and the use of antioxidants is the main motivation for considering Brownian motion and thermophoresis in the synovial liquid. The mathematical models for viscosity depending upon concentration and shear rate dependent are coupled with conservation governing equations. To explore the behavior of material parameters on the lubricant nature of the SF models governing laws are solved. The impacts of parameters on friction between joints are examined which helps to analyze the lubricant nature of SFs. Subsequently, it also noted that, in a quantitative sense, viscosity models (model-I and model-II) behave slightly differently under parametric variation. Brownian motion (BM) of particles for the case of the model-I is higher than that for the case of the model-II. The concentration field decreases by increasing the BM of particles. However, the concentration field increases when thermophoresis effects are increased. It is also found from the simulations that thermophoresis effects for the case of a model-II are stronger than those for the case of model-I. BM of particles is responsible for an increase in the temperature for the case of both models.

Heat and mass transfer have numerous industrial applications. The classical heat and mass transfer laws (Fourier and Fick laws) do not predict thermal and solute relaxation time phenomena. However, in this article, generalized modeling related to simultaneous heat and mass transfer in non-Newtonian fluid in the presence of chemical reaction and heat generation is presented and models are numerically solved by the finite element method (FEM). Hybrid nanoparticles Ag andFe3O4 are considered and novel correlations are inserted during numerical simulations. The present results have a good agreement with already published benchmark. Thermal relaxation time is the characteristics of the fluid due to which it avoids or tries to avoid the thermal changes. The fluid with thermal relaxation characteristic tries to restore the thermal equilibrium and hence the temperature of the fluid is decreased. The solute relation is incorporated in the concentration equation from generalized Fick's law and solute relaxation time has shown a decreasing tendency in the concentration field. The solute boundary layer region can be controlled via an increase in the solute relaxation parameter. Ohmic dissipation in hybrid nanofluid Ag−Fe3O4− Prandtl fluid) is stronger than that in mono nanofluid (Ag− Prandtl fluid). Hybrid nanofluid (Ag−Fe3O4− Prandtl fluid) produces more heat due to Joule heating than that produced by mono nanofluid (Ag− Prandtl fluid).

Novel models for heat flux with laws of linear and angular momenta and convection-diffusion equation for heat are used for the formulation of system of problems associated with the flow of polymer (micropolar fluid) over a cylindrical heated body elongating in a radial direction with non-uniform velocity. Spin gradient and vortex effects with couple stresses are taken into account. Problems are solved using the finite element method (FEM) with Galerkin approximation. Grid-independent and convergent solutions are derived. The results for special cases are compared and validated with published benchmarks. Simulations are used to assess the impact of parameters on flow variables based on the convergent solutions. Simulated results are displayed in the form of velocity, temperature and concentration graphs. Thermal relaxation time corresponds to thermal elastic behaviour and is responsible to make the fluid to restore the thermal equilibrium. This thermal relaxation phenomenon helps in reducing the width of the thermal region. The angular motion of fluid particles has been shown to be affected less by vortex viscosity. Vortex viscosity has shown a greater impact on the motion of Cu−Ag− polymer relative that on Cu− polymer. Thermal performance of Cu−Ag− polymer is better than the thermal enhancement of Cu− polymer. Due to Joule heating, hybrid nanofluid dissipates more heat relative to the nanofluid. Vortex viscosity plays vital role in controlling the thermal region. Heat flux has shown an increase as a function of thermal relaxation time.

Field inspections indicate that corrosion damages steel bridge girders more at the ends as compared to the central region. Any significant loss of metal around the bearing region of the girder may cause an abrupt change of failure behavior and strength loss. The present numerical study is focused to evaluate the response of local corrosion damage at steel plate girder, with different end panels, that is, no end post, nonrigid, and rigid end post. Local corrosion damage was considered by reducing the bearings stiffener and adjacent web uniformly for a maximum damage depth of 100 mm with various corrosion damage levels (i.e., 75%, 50%, and 25%). The numerical analysis was performed on a computer package ABAQUS, using modified Riks analysis. A 4-node shell element (S4R) with reduced integration was used to simulate the model. The study concludes that local corrosion damage is more critical in nonrigid end post than in rigid end post. Furthermore, the steel girder with no end post is more susceptible to crippling failure due to local corrosion damage as compared to the other types of end post.

For the creation of healable cement concrete matrix, microbial self-healing solutions are significantly more creative and potentially successful. The current study investigates whether gram-positive “Bacillus subtilis” (B. subtilis) microorganisms can effectively repair structural and non-structural cracks caused at the nano- and microscale. By creating an effective immobilization strategy in a coherent manner, the primary challenge regarding the viability of such microbes in a concrete mixture atmosphere has been successfully fulfilled. The iron oxide nanoparticles were synthesized. The examined immobilizing medium was the iron oxide nanoparticles, confirmed using different techniques (XRD, SEM, EDX, TGA, and FTIR). By measuring the average compressive strength of the samples (ASTM C109) and evaluating healing, the impact of triggered B. subtilis bacteria immobilized on iron oxide nanoparticles was examined. The compressive strength recovery of cracked samples following a therapeutic interval of 28 days served as a mechanical indicator of the healing process. In order to accurately correlate the recovery performance as a measure of crack healing duration, the pre-cracking load was set at 80% of the ultimate compressive stress, or “f c,” and the period of crack healing was maintained at 28 days. According to the findings, B. subtilis bacteria greatly enhanced the compressive strength and speed up the healing process in cracked cement concrete mixture. The iron oxide nanoparticles were proven to be the best immobilizer for keeping B. subtilis germs alive until the formation of fractures. The bacterial activity-driven calcite deposition in the generated nano-/micro-cracks was supported by micrographic and chemical investigations (XRD, FTIR, SEM, and EDX).

The mechanical properties of concrete are the important parameters in a design code. The amount of laboratory trial batches and experiments required to produce useful design data can be decreased by using robust prediction models for the mechanical properties of concrete, which can save time and money. Portland cement is frequently substituted with metakaolin (MK) because of its technical and environmental advantages. In this study, three mechanical properties of concrete with MK, i.e., compressive strength (f′c), splitting tensile strength (fst), and flexural strength (FS) were modelled by using four machine learning (ML) techniques: gene expression programming (GEP), artificial neural network (ANN), M5P model tree algorithm, and random forest (RF). For this purpose, a comprehensive database containing detail of concrete mixture proportions and values of f′c, fst, and FS at different ages was gathered from peer-reviewed published documents. Various statistical metrics were used to compare the predictive and generalization capability of the ML techniques. The comparative study of ML techniques revealed that RF has better predictive and generalization capability as compared with GEP, ANN, and M5P model tree algorithm. Moreover, the sensitivity and parametric analysis (PA) was carried out. The PA showed that the most suitable proportions of MK as partial cement replacement were 10% for FS and 15% for both f′c and fst.

This study carried out a comprehensive review to determine the carbonation process that causes the most deterioration and destruction of concrete. The carbonation mechanism involved using carbon dioxide (CO2) to penetrate the concrete pore system into the atmosphere and reduce the alkalinity by decreasing the pH level around the reinforcement and initiation of the corrosion process. The use of bacteria in the concrete was to increase the pH of the concrete by producing urease enzyme. This technique may help to maintain concrete alkalinity in high levels, even when the carbonation process occurs, because the CO2 accelerates to the concrete and then converts directly to calcium carbonate, CaCO3. Consequently, the self-healing of the cracks and the pores occurred as a result of the carbonation process and bacteria enzyme reaction. As a result of these reactions, the concrete steel is protected, and the concrete properties and durability may improve. However, there are several factors that control carbonation which have been grouped into internal and external factors. Many studies on carbonation have been carried out to explore the effect of bacteria to improve durability and concrete strength. However, an in-depth literature review revealed that the use of bacteria as a self-healing mechanism can still be improved upon. This review aimed to highlight and discuss the possibility of applying bacteria in concrete to improve reinforcement concrete.

Researchers and engineers are presently focusing on efficient waste material utilization in the construction sector to reduce waste. Waste marble dust has been added to concrete to minimize pollution and landfills problems. Therefore, marble dust was utilized in concrete, and its prediction was made via an artificial intelligence approach to give an easier way to scholars for sustainable construction. Various blends of concrete having 40 mixes were made as partial substitutes for waste marble dust. The ultrasonic pulse velocity of waste marble dust concrete (WMDC) was compared to a control mix without marble dust. Additionally, this research used standalone (multiple-layer perceptron neural network) and supervised machine learning methods (Bagging, AdaBoost, and Random Forest) to predict the ultrasonic pulse velocity of waste marble dust concrete. The models’ performances were assessed using R2, RMSE, and MAE. Then, the models’ performances were validated using k-fold cross-validation. Furthermore, the effect of raw ingredients and their interactions using SHAP analysis was evaluated. The Random Forest model, with an R2 of 0.98, outperforms the MLPNN, Bagging, and AdaBoost models. Compared to all the other models (individual and ensemble), the Random Forest model with greater R2 and lower error (RMSE, MAE) has a superior performance. SHAP analysis revealed that marble dust content has a positive and direct influence on and relationship to the ultrasonic pulse velocity of concrete. Using machine learning to forecast concrete properties saves time, resources, and effort for scholars in the engineering sector.

Cracking is one of the main problems in concrete structures and is affected by various parameters. The step-by-step laboratory method, which includes casting specimens, curing for a certain period, and testing, remains a source of worry in terms of cost and time. Novel machine learning methods for anticipating the behavior of raw materials on the ultimate output of concrete are being introduced to address the difficulties outlined above such as the excessive consumption of time and money. This work estimates the splitting-tensile strength of concrete containing recycled coarse aggregate (RCA) using artificial intelligence methods considering nine input parameters and 154 mixes. One individual machine learning algorithm (support vector machine) and three ensembled machine learning algorithms (AdaBoost, Bagging, and random forest) are considered. Additionally, a post hoc model-agnostic method named SHapley Additive exPlanations (SHAP) was performed to study the influence of raw ingredients on the splitting-tensile strength. The model’s performance was assessed using the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Then, the model’s performance was validated using k-fold cross-validation. The random forest model, with an R2 of 0.96, outperformed the AdaBoost models. The random forest models with greater R2 and lower error (RMSE = 0.49) had superior performance. It was revealed from the SHAP analysis that the cement content had the highest positive influence on the splitting-tensile strength of the recycled aggregate concrete and the primary contact of cement is with water. The feature interaction plot shows that high water content has a negative impact on the recycled aggregate concrete (RAC) splitting-tensile strength, but the increased cement content had a beneficial effect.

The low tensile strain capacity and brittle nature of high-strength concrete (HSC) can be improved by incorporating steel fibers into it. Steel fibers’ addition in HSC results in bridging behavior which improves its post-cracking behavior, provides cracks arresting and stresses transfer in concrete. Using machine learning (ML) techniques, concrete properties prediction is an effective solution to conserve construction time and cost. Therefore, sophisticated ML approaches are applied in this study to predict the compressive strength of steel fiber reinforced HSC (SFRHSC). To fulfil this purpose, a standalone ML model called Multiple-Layer Perceptron Neural Network (MLPNN) and ensembled ML algorithms named Bagging and Adaptive Boosting (AdaBoost) were employed in this study. The considered parameters were cement content, fly ash content, slag content, silica fume content, nano-silica content, limestone powder content, sand content, coarse aggregate content, maximum aggregate size, water content, super-plasticizer content, steel fiber content, steel fiber diameter, steel fiber length, and curing time. The application of statistical checks, i.e., root mean square error (RMSE), determination coefficient (R2), and mean absolute error (MAE), was also performed for the assessment of algorithms’ performance. The study demonstrated the suitability of the Bagging technique in the prediction of SFRHSC compressive strength. Compared to other models, the Bagging approach was more accurate as it produced higher, i.e., 0.94, R2, and lower error values. It was revealed from the SHAP analysis that curing time and super-plasticizer content have the most significant influence on the compressive strength of SFRHSC. The outcomes of this study will be beneficial for researchers in civil engineering for the timely and effective evaluation of SFRHSC compressive strength.