Nanomaterials are incorporated to improve the characteristics of conventional concrete materials. Among them, Titanium dioxide (TiO₂) nanoparticles exhibit sustainable concrete features such as optimization of materials, improvement of structures, recycling, and innovative waste management in materials and processes. This paper thoroughly elaborates on the effect of TiO₂ nanoparticles on the composites of cement which in turn modifies the physical, mechanical, and ability to resist any chemical action, climate change, and abrasion; Furthermore, this study emphasizes the small fragment size, increased surface area and photocatalytic properties of cementitious composites. The key parameters for choosing TiO₂ nanoparticles encompass their strength, antimicrobial characteristics, and stability against external environmental factors, covering a wide range of compatibility issues and exploring the fundamental characteristics of the material imbibing in cement. The present review includes the basic features of TiO₂ nanoparticles, their utilization in cement, the fabrication processes, and influential factors on the workability of concrete. The review also comprises the workability of fresh concrete, mechanical qualities related to compressive, flexural, and tensile strength, and durability variables such as electrical resistivity, permeability, carbonation resistance, freeze-thaw cycles, and sulfate attack resistance. The report further investigates the ideal dosage of TiO₂ nanoparticles and compares it to other nanomaterials utilized in cementitious compositions. More importantly, it emphasizes nano-titanium dioxide's ability to deal with pollutants in urban areas, notably nitrogen oxides (NOx), via its photocatalytic characteristics. The study explains how nano-TiO₂ affects the microstructure of cement-based materials, resulting in improved durability, performance, and mechanical characteristics. Finally, this study outlines present problems and recommends future research using nano-TiO₂ in cement-based materials.

Tetra-hybrid nanofluids with Al₂O₃, Cu, SiO₂ and TiO₂ offer better control over heat transfer rate. These nanofluids are useful for thermal applications in engineering, biomedical material science and the energy sector. Different nanoparticle shapes also influence the heat transfer performance of tetra-hybrid nanofluids and allow the fluid to be tailored for specific industrial needs. This study examines the flow aspects of a radiative magnetohydrodynamics tetra-hybrid nanofluid flowing past a stretching surface in a porous medium. The flow system is further interpreted under the influence of first- order velocity and thermal slip conditions. Initially formulated flow governing equations are highly nonlinear partial differential equations that are changed into a set of linear ordinary differential equations adopting relevant similarity transformations. The subsequent equations are then dealt with using both numerical and analytical approaches. Specifically, we employ the MATLAB-based bvp4c solver for the numerical solution and the optimal auxiliary functions method for the analytical treatment. The responses of key governing flow parameters on velocity, temperature, skin friction, and Nusselt number are examined and presented through both graphs and tabulated data. Raising the porosity parameter from $$Ps=0.1$$ to $$Ps=0.4$$ results in a noticeable decline in momentum profiles, dropping from $$f'\left( {\eta =0.6} \right)=0.49860$$ to $$f'\left( {\eta =0.6} \right)=0.33058$$ . Enhancing the nanoparticle volume fraction $$\left( {0.00 \leqslant \phi \leqslant 0.05} \right)$$ elevates the temperature distribution from $$\theta \left( {\eta =2} \right)=0.06349$$ to $$\theta \left( {\eta =1} \right)=0.21466$$ . The trend of decreasing skin friction with enhancing inputs of nanoparticle shape factor and magnetic parameter remains consistent in both no-slip and slip conditions. Slip effects significantly impact the heat transfer rate compared to the no-slip condition under the impact of Eckert number and nanoparticle shape factors.

Physics-informed modeling (PIM) using advanced machine learning (ML) represents a paradigm shift in the field of concrete technology, offering a potent blend of scientific rigor and computational efficiency. By harnessing the synergies between physics-based principles and data-driven algorithms, PIM-ML not only streamlines the design process but also enhances the reliability and sustainability of concrete structures. As research continues to refine these models and validate their performance, their adoption promises to revolutionize how concrete materials are engineered, tested, and utilized in construction projects worldwide. In this research work, an extensive literature review, which produced a global representative database for the splitting tensile strength (Fsp) of recycled aggregate concrete, was indulged. The studied concrete components such as C, W, NCAg, PL, RCAg_D, RCAg_P, RCAg_wa, Vf, and F_type were measured and tabulated. The collected 257 records were partitioned into training set of 200 records (80%) and validation set of 57 records (20%) in line with a more reliable partitioning of database. Five advanced machine learning techniques created using the “Weka Data Mining” software version 3.8.6 were applied to predict the Fsp and the Hoffman & Gardener method and performance metrics were also used to evaluate the sensitivity and performance of the variables and ML models, respectively. The results show the Kstar model demonstrates the highest level of performance and reliability among the models, achieving exceptional accuracy with an R² of 0.96 and Accuracy of 94%. Its RMSE and MAE are both low at 0.15 MPa, indicating minimal deviations between predicted and actual values. Additional metrics such as WI (0.99), NSE (0.96), and KGE (0.96) further confirm the model’s superior efficiency and consistent performance, making it the most dependable tool for practical applications. Also the sensitivity analysis shows that Water content (W) exerts the most significant impact at 40%, demonstrating that the amount of water in the mix is a critical factor for achieving optimal tensile strength. This underscores the need for careful water management to balance workability and strength in sustainable concrete production. Coarse natural aggregate (NCAg) has a substantial impact of 38%, indicating its essential role in maintaining the structural integrity of the concrete mix.

Machine learning prediction of the mechanical properties of self-compacting concrete (SCC) reinforced with hybrid fibers, incorporating industrial wastes like fly ash and blast furnace slag, and cured under elevated heat provides a reliable and efficient alternative to traditional laboratory experiments. In this work, extensive literature review leading to the collection, sorting and curation of a global database representative of the mechanical properties of self-compacting concrete reinforced with hybrid fiber mixed with industrial wastes for sustainable construction was conducted. The collected database constituted traditional concrete components and admixtures such as Cement (C), Fly ash (FA), Slag (BFS), Fine Aggregate (FAg), Coarse Aggregate (CAg), Water (W), Superplasticizer (PL), Fiber (Fi), and Temperature (Temp.) studied under the mechanical properties such as the Compressive Strength (Fc), Tensile Strength (Fsp), and Flexural Strength (Ff). The collected 114 records were divided into training set (90 records = 80%) and validation set (24 records = 20%) following the guidelines for data partitioning for optimal performance in machine learning predictions. Different advanced machine learning methods created using “Weka Data Mining” software version 3.8.6 were applied such as “Semi-supervised classifier (Kstar)”, “M5 classifier (M5Rules), “Elastic net classifier (ElasticNet), “Correlated Nystrom Views (XNV)”, and “Decision Table (DT)” to predict the output. The Hoffman/Gardener and SHAP techniques are used to estimate the sensitivity of the input parameter on the output. Finally, various performance metrics are used to evaluate the reliability of the models. The results show that the machine learning models show varying degrees of predictive accuracy, with the Kstar and XNV models consistently outperforming others across all mechanical properties. However, Kstar with accuracies of 96.5%, 96.0%, and 97.0% for Fc, Fsp, and Ff predictions, respectively proposed the most decisive model. Also, the Hoffman and Gardener method highlights the role of the binders, chemical additives, and curing, whereas SHAP attributes greater importance to aggregates and binder interactions.

The goal of this research is to deploy artificial neural networks (ANNs) to exploit the bioconvective flow of Casson nanofluid across a heated Riga plate with swimming microbes. The ANNs furnish a versatile computational framework that is very helpful in intricate domains, including biotechnology and biological computing. The impressions of nonlinear thermal radiation, heat generation/consumption and viscous dissipation are also addressed. In this study, thermophoresis and Brownian motion are modeled using the well-known Buongiorno nanofluid framework. The suitable alteration is provided to convert the governing flow expressions into an ordinary differential equations. Analytical solutions are derived with the homotopy analysis method. Our outcomes are consistent with the results reported in previous literature. The physical parameters, accompanied by modeled equations, are illustrated graphically alongside corresponding physical outcomes. The graphical values of velocity, thermal, nanofluid concentration, microorganisms, surface drag force, heat transmittal gradient, mass transmittal gradient, and motile density microorganisms are calculated and detailed for various emerging factors. The velocity field improved when mounting the quantities of mixed convection parameter and modified Hartmann number. The microorganisms field declines as the quantity of bioconvective Lewis number upsurges. The heat transmittal rate slumps when enhancing the value of the porosity parameter and the reverse trend is obtained for the mass transmittal rate. The Levenberg–Marquardt approach is employed to optimize the data by training (60%), testing (20%) and validating (20%) to verify the data’s accuracy.