Review on carbonation study of reinforcement concrete incorporating with bacteria as self-healing approach

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

Use of Artificial Intelligence Methods for Predicting the Strength of Recycled Aggregate Concrete and the Influence of Raw Ingredients

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

A Biomineralization, Mechanical and Durability Features of Bacteria-Based Self-Healing Concrete—A State of the Art Review

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

Impact of Openings on the In-Plane Strength of Confined and Unconfined Masonry Walls: A Sustainable Numerical Study

While openings are an essential requirement in buildings as a source of access, fresh air and sunlight, these openings cause a reduction in the lateral stiffness and torsional resistance of masonry wall units. A detailed numerical investigation was carried out to explore the impact of the opening percentage on the in-plane stiffness and lateral strength of unconfined and confined masonry wall panels prepared using calcium silicate bricks, for sustainable masonry structures. A commercially available FEM package (ANSYS) was used to carry out comparative analysis of ten wall panels, five of each type (confined and unconfined masonry walls) with concentrically located openings of varying sizes (0% to 16.5%). A simplified micro-modeling technique following the Newton Raphson Algorithm was adopted. Results revealed that the confined masonry approach unveiled a more reliable anti-seismic response along with improved in-plane strength in the case of confined masonry walls. The failure type shifted from pure flexural to more of a blend of shear and flexure after the opening percentage increased to 10.09% in unconfined masonry walls, which was not the case where confinement was provided. Based on the outcomes, it is strongly recommended to adopt confined masonry in highly seismic-prone areas to avoid catastrophic damage caused by earthquakes

Investigating the Ultrasonic Pulse Velocity of Concrete Containing Waste Marble Dust and Its Estimation Using Artificial Intelligence

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

Impact of Openings on the In-Plane Strength of Confined and Unconfined Masonry Walls: A Sustainable Numerical Study

While openings are an essential requirement in buildings as a source of access, fresh air and sunlight, these openings cause a reduction in the lateral stiffness and torsional resistance of masonry wall units. A detailed numerical investigation was carried out to explore the impact of the opening percentage on the in-plane stiffness and lateral strength of unconfined and confined masonry wall panels prepared using calcium silicate bricks, for sustainable masonry structures. A commercially available FEM package (ANSYS) was used to carry out comparative analysis of ten wall panels, five of each type (confined and unconfined masonry walls) with concentrically located openings of varying sizes (0% to 16.5%). A simplified micro-modeling technique following the Newton Raphson Algorithm was adopted. Results revealed that the confined masonry approach unveiled a more reliable anti-seismic response along with improved in-plane strength in the case of confined masonry walls. The failure type shifted from pure flexural to more of a blend of shear and flexure after the opening percentage increased to 10.09% in unconfined masonry walls, which was not the case where confinement was provided. Based on the outcomes, it is strongly recommended to adopt confined masonry in highly seismic-prone areas to avoid catastrophic damage caused by earthquakes

Self-Healing Bio-Concrete Using <i>Bacillus subtilis</i> Encapsulated in Iron Oxide Nanoparticles

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)

A Biomineralization, Mechanical and Durability Features of Bacteria-Based Self-Healing Concrete—A State of the Art Review

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

Using Machine Learning Algorithms to Estimate the Compressive Property of High Strength Fiber Reinforced Concrete

The low tensile strain capacity and brittle nature of high-strength concrete (HSC) can be improved by incorporating steel fibers into it. Steel fibers&rsquo; 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&rsquo; 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

Numerical Analysis of Shallow Foundations with Varying Loading and Soil Conditions

The load–deformation relationship under the footing is essential for foundation design. Shallow foundations are subjected to changes in hydrological conditions such as rainfall and drought, affecting their saturation level and conditions. The actual load–settlement response for design and reconstructions is determined experimentally, numerically, or utilizing both approaches. Ssettlement computation is performed through large-scale physical modeling or extensive laboratory testing. It is expensive, labor intensive, and time consuming. This study is carried out to determine the effect of different saturation degrees and loading conditions on settlement shallow foundations using numerical modeling in Plaxis 2D, Bentley Systems, Exton, Pennsylvania, US. Plastic was used for dry soil calculation, while fully coupled flow deformation was used for partially saturated soil. Pore pressure and deformation changes were computed in fully coupled deformation. The Mohr–Columb model was used in the simulation, and model parameters were calculated from experimental results. The study results show that the degree of saturation is more critical to soil settlement than loading conditions. When a 200 KPa load was applied at the center of the footing, settlement was recored as 28.81 mm, which was less than 42.96 mm in the case of the full-depth shale layer; therefore, settlement was reduced by 30% in the underlying limestone rock layer. Regarding settlement under various degrees of saturation (DOS), settlment is increased by an increased degree of saturation, which increases pore pressure and decreases the shear strength of the soil. Settlement was observed as 0.69 mm at 0% saturation, 1.93 mm at 40% saturation, 2.21 mm at 50% saturation, 2.77 mm at 70% saturation, and 2.84 mm at 90% saturation of soil