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.

Sustainability concerns over the management and handling of the growing volume of waste tires have necessitated the exploration of potential applications for the reuse and recycling of this resource, as they are categorized as hazardous wastes and are typically incinerated through thermal processing or dumped in landfills, resulting in significant environmental issues. The recycled steel and textile fibers from tires can be incorporated in concrete to assist in mitigating this impending environmental calamity, primarily by enhancing the efficacy of concrete. The present study aims to investigate the effect of using recycled tire steel fibers (RTSF) and recycled tire textile fibers (RTTF) in concrete, as economically viable and environmentally friendly alternatives to commercially available fibers. Although literature on the use of recycled fibers in concrete is available, the research is very limited in terms of their hybrid use and with minimal environmental analysis. Consequently, to address the gaps, this research concentrates on the use of RTSF and RTTF as a hybrid mix in concrete with life cycle assessment (LCA) to balance the mechanical performance and environmental sustainability. The experimental work is formulated to suggest an optimum dose of RTSF and RTTF, as a hybrid mix form, to be incorporated in concrete that imparts sufficient strength and workability. The fibers were integrated with dosages of 0.75%, 1%, and 1.25% for RTSF and 0.25%, 0.5%, and 0.75% for RTTF, respectively, by volume in non-hybrid form, while in hybrid form, they were reinforced as four different combinations (1%:0.5%, 0.75%, 0.75%, 0.5%, 0.5%:0.5%, and 0.75%:0.25%) by volume of RTSF and RTTF, respectively. Fresh and hardened properties of concrete were tested according to the ASTM standards. The results showed that concrete with hybrid fibers outperformed the concrete with normal individual fibers in both fresh and hardened states tests. The mechanical strength results showed that the synergistic use of RTSF and RTTF can enhance the strength, toughness, ductility, and crack resistance of the concrete. The hybrid mix H1 comprising 1% RTSF and 0.5% RTTF was ascertained as the optimal mix showing the highest mechanical performance with embodied CO₂ and energy values only slightly higher than the control mix, while offering the significant sustainability benefit of utilizing recycled fibers.

Portland cement is the primary source of CO₂ emissions in concrete production due to the energy required for the calcination of limestone, the release of CO₂ from fuel combustion during cement manufacturing, and the hydration process during setting. To mitigate the environmental challenges associated with cement production, the use of industrial waste as cementitious material can significantly reduce both the volume of waste generated and its disposal in landfills, thereby freeing up land for other purposes. Concrete has traditionally incorporated natural pozzolans, waste and recycled materials, and industrial byproducts as partial replacements for Portland cement. Among these, Ground Granulated Blast Furnace Slag (GGBFS) and Fly Ash (FA) are the most commonly used supplementary cementitious materials (SCMs), known for enhancing the mechanical strength, flow ability, and durability of concrete. SCMs improve the concrete matrix's resistance to chemical attacks, reduce permeability, and contribute to long-term strength development. This review highlights the scientific literature on the feasibility and effectiveness of using GGBFS and FA as sustainable alternatives to cement in mortar and concrete production. GGBFS is a byproduct of the iron-making process, while FA is a fine particulate material generated from coal-fired power plants. The paper presents a detailed discussion of their manufacturing processes, physical characteristics, and their impact when used as partial cement replacements. It also summarizes findings from previous studies regarding optimal replacement percentages, which vary depending on the source, mix design, and particle size distribution of the materials. Finally, this review proposes process improvement strategies to optimize the use of GGBFS and FA in future sustainable concrete applications.

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.

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.