The integration of artificial intelligence (AI) and machine learning (ML) has revolutionised civil engineering, enhancing predictive accuracy, decision-making, and sustainability across domains such as structural health monitoring, geotechnical analysis, transportation systems, water management, and sustainable construction. This paper presents a detailed review of peer-reviewed publications from the past decade, employing bibliometric mapping and critical evaluation to analyse methodological advances, practical applications, and limitations. A novel taxonomy is introduced, classifying AI/ML approaches by civil engineering domain, learning paradigm, and adoption maturity to guide future development. Key applications include pavement condition assessment, slope stability prediction, traffic flow forecasting, smart water management, and flood forecasting, leveraging techniques such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Support Vector Machines (SVMs), and hybrid physics-informed neural networks (PINNs). The review highlights challenges, including limited high-quality datasets, absence of AI provisions in design codes, integration barriers with IoT-based infrastructure, and computational complexity. While explainable AI tools like SHAP and LIME improve interpretability, their practical feasibility in safety-critical contexts remains constrained. Ethical considerations, including bias in training datasets and regulatory compliance, are also addressed. Promising directions include federated learning for data privacy, transfer learning for data-scarce regions, digital twins, and adherence to FAIR data principles. This study underscores AI as a complementary tool, not a replacement, for traditional methods, fostering a data-driven, resilient, and sustainable built environment through interdisciplinary collaboration and transparent, explainable systems.

The demand for concrete has led to increased use of raw materials and significant waste generation. Recycled aggregate concrete (RAC) offers a viable approach to sustainable concrete; however, the use of weakly bonded mortar on aggregate leads to low strength and crack formation. Fiber reinforcement, specifically hybrid fiber reinforcement combining steel, glass, basalt, and polypropylene fibers, can increase the tensile and flexural properties of RAC. This study developed machine learning models to enable the prediction of hybrid fiber-reinforced RAC’s compressive, splitting tensile, and flexural strength performance; these new models overcome the limitations of previous research, which relied on only one fiber type and regular methods of optimization. Two models (a deep neural network (DNN) and an XGBoost model) were trained and optimized using bald eagle search (BES), particle swarm optimization (PSO), and the Bayesian optimization (BO) algorithm to improve performance. Among the three optimization analyses, PSO-XGBoost achieved the highest accuracy for compressive strength and splitting tensile strength, while BES-XGBoost achieved the highest accuracy for flexural strength. The most significant influences on the compressive strength were curing age and silica fume, while the main drivers of splitting tensile strength and flexural strength were fiber volume and fiber characteristics. The use of SHAP-based methodology with a user-friendly interface further improved the design of RAC mixtures, reducing waste from raw materials, enhancing the structural performance of RAC, and enabling data-driven decision-making in the manufacturing of eco-friendly concrete products.

Biochar has emerged as a sustainable additive in concrete production, offering potential for improved concrete performance and waste valorization. An experimental investigation was conducted using wood waste biochar as a partial cement replacement at 0%, 2%, 4%, and 6% by weight. Compressive strength (CS) and split tensile strength (STS) were determined at 7 and 28 days, while flexural strength (FS) was determined at 28 days. The experimental results demonstrated that 2 to 4% biochar replacement enhanced CS by 9.67% and FS by 15.40%, while STS showed optimal improvement at 2% replacement by 6.24%. To extend these findings across diverse feedstocks and mix designs, a comprehensive database of 318 mixes incorporating 13 biochar types was compiled from literature to develop machine learning (ML) models for predicting all three strength properties simultaneously. Random Forest (RF) and Gradient Boosting (GBR) algorithms were optimized using nested 5-fold cross-validation and compared against a Ridge regression baseline. The optimized RF model (n_estimators = 1000) achieved a nested cross-validated R² of 0.817 ± 0.072 and a 32.5% reduction in RMSE compared to the baseline, with testing R² values of 0.894 for CS, 0.828 for FS, and 0.537 for STS. (SHapley Additive exPlanations) (SHAP) analysis identified cement content, coarse aggregate (CA) content, and biochar dosage as the most influential features. Biochar effect curves, based on the most reliable datasets (rice husk, n = 69; wood, n = 52), demonstrated that rice husk biochar consistently enhanced all three strength properties, while wood biochar showed superior performance for FS and STS. Experimental validation using wood waste biochar confirmed that model predictions closely matched measured strengths, with 90% prediction intervals reliably encompassing experimental values. The developed models offer a practical decision-support tool for sustainable concrete mix design, significantly reducing experimental effort while providing evidence-based guidance for biochar feedstock selection and dosage optimization, keeping the cement usage at a minimum.

Recycled concrete aggregate (RC A) is considered a sustainable material; however, its porosity and interfacial properties are poor due to adhering mortar. This study investigates the influence of synergistic surface treatments in terms of improving RCA quality and the resulting compressive strength of recycled aggregate concrete (RAC). A machine learning (ML) model was also developed to predict the compressive strength of recycled aggregate concrete (RAC) with different surface treatments, not just untreated RCA. In this study, three different RCA surface treatments were investigated. In this regard, acetic acid, silica fume, and sodium silicate treatments were combined. The properties of concrete and fresh concrete were investigated using slump and compressive tests at 28 and 90 days. The performance of various ML models, incorporating Gradient Boosting, Random Forest, XGBoost, and Extra Trees, was investigated. The performance of different models was also evaluated using R², MAE, and RMSE. SHAP analysis was used to evaluate the performance of different models. It has been observed that the use of surface treatment leads to lower water absorption values and higher interfacial bonding, as well as substantial improvements in compressive strength. Specifically, the use of acetic acid and silica fume for treating RCA produced compressive strengths similar to those achieved from natural aggregates at lower costs. XGBoost has the highest accuracy among all models. The R² value of XGBoost was 0.909. The SHAP analysis indicates that cement and curing age are the main features. RCA treatment parameters are considered modifiers. A user-friendly online tool was created to estimate compressive strength using different types of RCA treatment. The RCA treatment with sodium silicate and silica fume performed best in terms of embodied carbon among the treated mixes; it was deemed the best alternative from an environmental standpoint.

Concrete contributes significantly to global CO₂ emissions due to high energy demand for cement production. This research integrates multiple advanced ensemble ML-based prediction models by combining experimental evaluation, explainable framework, and life cycle sustainability analysis for SCM (supplementary cementitious materials)-incorporated concrete mixtures. A comprehensive experimental program was conducted to evaluate the compressive and tensile strength of concrete revealing that the hybrid mix of GF4 with a 40% replacement level of cement with fly ash (FA) and ground granulated blast furnace slag (GGBFS) exhibited optimum synergistic performance due to balanced hydration kinetics and improved microstructure characteristics. For computational model development, a k-fold cross validation technique was deployed to evaluate robustness across multiple data partitions and to control overfitting in models. Model performance was assessed through multiple metrics including R², RMSE, and MAE with particular emphasis on the gap between training and testing performance. The best performing model was optimized using Particle Swarm Optimization (PSO) and Bayesian Optimization (BO) techniques providing an additional safeguard against overfitting. Shapley Additive Explanation (SHAP) interpretation revealed w/b ratio and curing age as key parameters for compressive strength, while fine aggregate content and curing age influenced tensile strength. For compressive strength, XGBoost model performed well with an R² value of 0.879 which was increased to 0.918 with the PSO optimization technique. For tensile strength, the Gradient Boosting model was selected with an R² value of 0.840 which was optimized to 0.879 after the PSO optimization technique. Moreover, life cycle assessment was performed to evaluate the environmental impacts in terms of embodied carbon and energy associated with concrete mixes. The hybrid GF4 mix demonstrated a 36% reduction in embodied carbon compared to the control mix, indicating strong potential for low carbon concrete applications. This integrated research contributes to the advancement of green construction practices and supports global efforts to reduce atmospheric impacts through the circular use of industrial byproducts.