Reinforced concrete (RC) has been commonly used as a construction material for decades due to its high compressive strength and moderate tensile strength. However, these two properties of RC are frequently hampered by degradation. The main degradation processes in RC structures are carbonation and the corrosion of rebars. The scientific community is divided regarding the process by which carbonation causes structural damage. Some researchers suggest that carbonation weakens a structure and makes it prone to rebar corrosion, while others suggest that carbonation does not damage structures enough to cause rebar corrosion. This paper is a review of the research work carried out by different researchers on the carbonation and corrosion of RC structures. The process of carbonation and the factors that contribute to this process will be discussed, alongside recommendations for improving structures to decrease the carbonation process. The corrosion of rebars, damage to passive layers, volume expansion due to steel oxidation, and crack growth will also be discussed. Available protection methods for reducing carbonation, such as rebar structure coating, cathodic protection, and modifier implementation, will also be reviewed. The paper concludes by describing the most significant types of damage caused by carbonation, testing protocols, and mitigation against corrosion damage.

Every year, millions of tons of red mud (RDM) are created across the globe. Its storage is a major environmental issue due to its high basicity and tendency for leaching. This material is often kept in dams, necessitating previous attention to the disposal location, as well as monitoring and maintenance during its useful life. As a result, it is critical to develop an industrial solution capable of consuming large quantities of this substance. Many academics have worked for decades to create different cost-effective methods for using RMD. One of the most cost-effective methods is to use RMD in cement manufacture, which is also an effective approach for large-scale RMD recycling. This article gives an overview of the use of RMD in concrete manufacturing. Other researchers’ backgrounds were considered and examined based on fresh characteristics, mechanical properties, durability, microstructure analysis, and environmental impact analysis. The results show that RMD enhanced the mechanical properties and durability of concrete while reducing its fluidity. Furthermore, by integrating 25% of RDM, the environmental consequences of cumulative energy demand (CED), global warming potential (GWP), and major criteria air pollutants (CO, NO_X, Pb, and SO₂) were minimized. In addition, the review assesses future researcher guidelines for concrete with RDM to improve performance.

In recent years, concerns about the depletion of nonrenewable resources, infrastructure sustainability, and environmental impact in pavement engineering have led to substituting petroleum-based paving materials with their biobased counterparts. Research efforts have been attempted to produce asphalt from renewable bioresources. As a special modifier for asphalt, petroleum-based epoxy resin has been used in a few asphalt paving projects that require superior performance of asphalt mixtures. This paper presents an attempt to develop a biobased epoxy modifier for asphalt using an epoxidized soybean oil (ESO) and a biobased curing agent, maleic anhydride (MA). The proper proportions of ESO, MA, and a base asphalt were determined to achieve a homogenous biobased epoxy asphalt binder (BEAB) with the desired properties evaluated by a rotational viscosity test, a penetration test, and a dynamic shear rheometer test. Pavement performance–related properties of asphalt mixtures using such a BEAB were also preliminarily evaluated using a Marshall stability test. At an optimum ratio of $0.45:1:8$ for $\mathrm{MA}:\mathrm{ESO}:\text{asphalt}$ , the BEAB showed higher Marshall stability than a mixture containing the base asphalt. Further research, however, is still needed to evaluate the asphalt mixtures with more pavement performance–based tests and to refine the BEAB formula.

The conventional method for determining the Marshall Stability (MS) and Marshall Flow (MF) of asphalt pavements entails laborious, time-consuming, and expensive laboratory procedures. In order to develop new and advanced prediction models for MS and MF of asphalt pavements the current study applied three soft computing techniques: Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multi Expression Programming (MEP). A comprehensive database of 343 data points was established for both MS and MF. The nine most significant and straightforwardly determinable geotechnical factors were chosen as the predictor variables. The root squared error (RSE), Nash–Sutcliffe efficiency (NSE), mean absolute error (MAE), root mean square error (RMSE), relative root mean square error (RRMSE), coefficient of determination (R²), and correlation coefficient (R), were all used to evaluate the performance of models. The sensitivity analysis (SA) revealed the rising order of input significance of MS and MF. The results of parametric analysis (PA) were also found to be consistent with previous research findings. The findings of the comparison showed that ANN, ANFIS, and MEP are all reliable and effective methods for the estimation of MS and MF. The mathematical expressions derived from MEP represent the novelty of MEP and are relatively reliable and simple. R_overall values for MS and MF were in the order of MEP > ANFIS > ANN with all values over the permissible range of 0.80 for both MS and MF. Therefore, all the techniques showed higher performance, possessed high prediction and generalization capabilities, and assessed the relative significance of input parameters in the prediction of MS and MF. In terms of training, testing, and validation data sets and their closeness to the ideal fit, i.e., the slope of 1:1, MEP models outperformed the other two models. The findings of this study will contribute to the choice of an appropriate artificial intelligence strategy to quickly and precisely estimate the Marshall Parameters. Hence, the findings of this research study would assist in safer, faster, and more sustainable predictions of MS and MF, from the standpoint of time and resources required to perform the Marshall tests.

The chloride diffusion coefficient (Dcl) is one of the most important characteristics of concrete durability. This study aimed to develop a prediction model for the Dcl of concrete incorporating supplemental cementitious material. The datasets of concrete containing supplemental cementitious materials (SCMs) such as tricalcium aluminate (C₃A), ground granulated blast furnace slag (GGBFS), and fly ash were used in developing the model. Five machine learning (ML) algorithms including adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), support vector machine (SVM), and extreme learning machine (ELM) were used in the model development. The performance of the developed models was tested using five evaluation metrics, namely, normalized reference index (RI), coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). The SVM models demonstrated the highest prediction accuracy with R² values of 0.955 and 0.951 at the training and testing stage, respectively. The prediction accuracy of the machine learning (ML) algorithm was checked using the Taylor diagram and Boxplot, which confirmed that SVM is the best ML algorithm for estimating Dcl, thus, helpful in establishing reliable tools in concrete durability design.

The use of superabsorbent polymers, sometimes known as SAP, is a tremendously efficacious method for reducing the amount of autogenous shrinkage (AS) that occurs in high-performance concrete. This study utilizes support vector regression (SVR) as a standalone machine-learning algorithm (MLA) which is then ensemble with boosting and bagging approaches to reduce the bias and overfitting issues. In addition, these ensemble methods are optimized with twenty sub-models with varying the n^th estimators to achieve a robust R². Moreover, modified bagging as random forest regression (RFR) is also employed to predict the AS of concrete containing supplementary cementitious materials (SCMs) and SAP. The data for modeling of AS includes water to cement ratio (W/C), water to binder ratio (W/B), cement, silica fume, fly ash, slag, the filer, metakaolin, super absorbent polymer, superplasticizer, super absorbent polymer size, curing time, and super absorbent polymer water intake. Statistical and k-fold validation is used to verify the validation of the data using MAE and RMSE. Furthermore, SHAPLEY analysis is performed on the variables to show the influential parameters. The SVM with AdaBoost and modified bagging (RF) illustrates strong models by delivering R² of approximately 0.95 and 0.98, respectively, as compared to individual SVR models. An enhancement of 67% and 63% in the RF model, while in the case of SVR with AdaBoost, it was 47% and 36%, in RMSE and MAE of both models, respectively, when compared with the standalone SVR model. Thus, the impact of a strong learner can upsurge the efficiency of the model.