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 (${f^{\prime }}_{\mathrm{c}}$), splitting tensile strength ($f_{\mathrm{st}}$), 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 ${f^{\prime }}_{\mathrm{c}}$, $f_{\mathrm{st}}$, 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 ${f^{\prime }}_{\mathrm{c}}$ and $f_{\mathrm{st}}$.

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).

Cracking is one of the main ways that concrete ages, allowing pollutants to seep within and potentially lowering the physical and mechanical strength and endurance of concrete structures. One of the healing procedures that merits research is the use of bacterially generated calcium carbonate precipitation in concrete mixtures to mend concrete cracks. The impact of different variables, including the nucleation location, bacterial type, concentration, uratolytic activities, pH, nutrition, and temperature on the bio-mineralization of calcium carbonate are discussed in this review article. ATR-IR (Attenuated Internal Reflectance Fourier Transform Infrared Spectroscopy)/FTIR (Fourier Transform Infrared Spectroscopy)/NMR (Nuclear Magnetic Resonance) and FESEM (Field Emission Scanning Electron Microscope) are among the micro test techniques reviewed along with the biosynthetic pathway of bio mineralized calcium carbonate. The sealing ability and recovery of mechanical and durability properties of bio-mineralized concrete specimen is discussed. Moreover, we discussed the corrosion, damages, and challenges and their detection methods. Also, in-depth knowledge on the use, advancements, and drawbacks of bio-mineralized calcium carbonate is presented. Future potential for bio-mineralized (MICP) self-healing concrete are discussed in the final section.

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.

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.

Water treatment plants produce a huge amount of sludge, which are ultimately disposed to the nearest water channel, leading to harmful effects. This unmanaged wastewater treatment plant sludge (WTS) results in social and environmental concerns. Therefore, the utilization of WTS in construction activities can be a viable option for the management of waste sludge, leading to sustainable infrastructures. The main aim of this study was to investigate the potential of WTS in the manufacturing of clay bricks at an industrial scale. WTS was procured from the Rawal Lake water treatment plant, Pakistan. Clay was collected from a local industrial brick kiln site. Brick specimens with varying percentages of WTS (i.e., 5%, 10%, 15%, 20%, 30% and 40%) were casted and their mechanical and durability characteristics were evaluated. It was observed that the bricks incorporating WTS showed higher compressive and flexural strengths compared to that of the normal clay bricks. For instance, brick specimens incorporating 5% WTS by weight of clay showed a 10% increase in compressive strength. Furthermore, brick specimens incorporating 20% of WTS by clay weight satisfied the strength requirements as per local building codes for masonry construction. Scanning electronic microscopic (SEM) images confirm the porous microstructure of brick specimens manufactured with WTS, which results in 12% lighter clay bricks as compared to conventional clay bricks. Moreover, the durability characteristics of brick specimens incorporating WTS showed better performance. It can be concluded that bricks fabricated with a high proportion of WTS (i.e., 20%) will minimize the environmental overburden and lead to more durable and economical masonry construction.