Although free-cement-based alkali-activated paste, mortar, and concrete have been recognised as sustainable and environmental-friendly materials, a considerable amount of effort is still being channeled to ascertain the best binary or ternary binders that would satisfy the requirements of strength and durability as well as environmental aspects. In this study, the mechanical properties of alkali-activated mortar (AAM) made with binary binders, involving fly ash (FA) and granulated blast-furnace slag (GBFS) as well as bottle glass waste nano-silica powder (BGWNP), were opti-mised using both experimentally and optimisation modelling through three scenarios. In the first scenario, the addition of BGWNP varied from 5% to 20%, while FA and GBFS were kept constant (30:70). In the second and third scenarios, BGWNP (5–20%) was added as the partial replacement of FA and GBFS, separately. The results show that the combination of binary binders (FA and GBFS) and BGWNP increased AAM’s strength compared to that of the control mixture for all scenarios. In addition, the findings also demonstrated that the replacement of FA by BGWNP was the most significant, while the effect of GBFS replacement by BGWNP was less significant. In particular, the highest improvement in compressive strength was recorded when FA, GBFS, and BGWNP were 61.6%, 30%, and 8.4%, respectively. Furthermore, the results of ANOVA (p values < 0.0001 and high F-values) as well as several statistical validation methods (R > 0.9, RAE < 0.1, RSE < 0.013, and RRSE < 0.116) confirmed that all the models were robust, reliable, and significant. Similarly, the data variation was found to be less than 5%, and the difference between the predicted R² and adj. R² was very small (<0.2), thus confirming that the proposed non-linear quadratic equations had the capability to predict for further observation. In conclusion, the use of BGWNP in AAM could act as a beneficial and sustainable strategy, not only to address environmental issues (e.g., landfill) but to also enhance strength properties.

Bentonite is one of the SiO₂-rich pozzolanic clay types that has been enormously employed as a cost-effective and eco-friendly supplementary cementitious material in ordinary Portland cement (OPC) concrete. However, the use of bentonite in geopolymer concrete (GPC) has not been explored very widely. Further, the research available on the effect of utilizing treated bentonite in GPC is limited. The practical application of GPC is also very limited due to its significant shrinkage and high brittleness compared to OPC concrete. There are several studies available that have highlighted the use of polypropylene fibers (PPF) in improving the mechanical properties of GPC by reducing drying shrinkage and enhancing ductility. However, the effect of PPF on the durability properties of GPC needs to be addressed. Further, the effect of the combined integration of bentonite and PPF on the mechanical and durability properties of GPC has not been reported yet. The aim of this study is, therefore, to investigate the individual and combined effect of bentonite and PPF on the workability, mechanical properties, and durability of fly ash (FA)-based GPC. Bentonite replaced 10% of FA, and PPF was added at varying proportions (0.5%, 0.75%, and 1%) for raw and treated bentonite. Slump test was used to assess workability, while compressive, tensile, and flexural tests were utilized to evaluate the mechanical properties. Water absorption, acid attack, and abrasion resistance tests were used to evaluate durability. The results showed that bentonite and PPF significantly enhance mechanical properties, especially when combined with treated bentonite, with the highest improvement observed for mixtures with 1% PPF. The compressive strength was improved by an extent of 10% and 18% for raw bentonite-GPC and treated bentonite-GPC, respectively, compared to the control mix without bentonite. The durability test results revealed that water absorption of raw and treated bentonite-GPC mixtures at the age of 90 days was decreased by 16% and 21%, respectively, compared to the control mix (without bentonite). The mass loss of raw and treated bentonite-GPC mixtures in sulphuric acid solution was 5% and 10% lower, respectively, than the control mix (without bentonite). The mass loss of raw and treated bentonite-GPC mixtures in abrasion resistance tests was 6% and 12% lower, respectively, than the control mix (without bentonite). For durability performance, mixtures with 0.5% PPF perform the best, while higher PPF contents negatively impact the GPC durability.

The growing application of Fiber-Reinforced Polymer (FRP) composites in rehabilitating deteriorating concrete infrastructure underscores the need for reliable, cost-effective, and automated nondestructive testing (NDT) methods. This review provides a comprehensive analysis of existing and emerging NDT techniques used to assess externally bonded FRP (EB-FRP) systems, emphasizing their accuracy, limitations, and practicality. Various NDT methods, including Ground-Penetrating Radar (GPR), Phased Array Ultrasonic Testing (PAUT), Infrared Thermography (IRT), Acoustic Emission (AE), and Impact–Echo (IE), are critically evaluated in terms of their effectiveness in detecting debonding, voids, delaminations, and other defects. Recent technological advancements, particularly the integration of artificial intelligence (AI) and machine learning (ML) in NDT applications, have significantly improved defect characterization, automated inspections, and real-time data analysis. This review highlights AI-driven NDT approaches such as automated crack detection, hybrid NDT frameworks, and drone-assisted thermographic inspections, which enhance accuracy and efficiency in large-scale infrastructure assessments. Additionally, economic considerations and cost–performance trade-offs are analyzed, addressing the feasibility of different NDT methods in real-world FRP-strengthened structures. Finally, the review identifies key research gaps, including the need for standardization in FRP-NDT applications, AI-enhanced defect quantification, and hybrid inspection techniques. By consolidating state-of-the-art research and emerging innovations, this paper serves as a valuable resource for engineers, researchers, and practitioners involved in the assessment, monitoring, and maintenance of FRP-strengthened concrete structures.

The feasibility of strengthening deteriorated or under-capacity concrete structures with external carbon-fiber-reinforced polymer (CFRP) laminates has been widely validated in the literature. However, there is a lack of knowledge on the in situ long-term performance and age-related environmental degradation of the mechanical properties of the laminates. The current study involved the immersion of coupons from a common new CFRP laminate in heated water at 23, 45, and 60 °C for 224 days. The coupons were then tested for residual tensile properties, such as tensile capacity and elastic modulus, using ASTM D3039 (2017) specifications. The CFRP tensile capacity and elastic modulus decreased by a maximum of 33% and 26%, respectively, for 224 days of exposure. Based on the test data, an age-based long-term prediction model with excellent reliability for CFRP laminate tensile capacity was developed. The model was then calibrated with test results from old CFRP coupons collected from an existing CFRP laminate retrofitted concrete bridge. The calibrated model output was then compared with the environmental reduction factor from ACI 440.2R-17 and a few other common sources. It was found that the ACI specified a reduction factor of 0.75, which does not consider the CFRP age and overestimates the design tensile strength of the CFRP laminate by approximately 13%, which may compromise the structural safety of retrofitted bridges. The reduction factors from the other guidelines varied between 0.51 and 0.85.

The use of glass-fiber-reinforced polymer (GFRP) bars as an alternative to steel bars for reinforcing concrete (RC) structures has gained increasing attention in recent years. GFRP bars offer several advantages over steel bars, such as corrosion resistance, lightweight, high tensile strength, and non-magnetic properties. However, there are also some challenges and uncertainties associated with the behavior and performance of GFRP-reinforced concrete (GFRP-RC) structures, especially under compression and bonding behavior. Therefore, there is a need for comprehensive experimental investigations to validate the effectiveness of GFRP bars in concrete columns. This paper presents a study that aims to address these issues by conducting experimental tests on GFRP-RC columns. The experimental tests examine the mechanical properties of GFRP bars and their bond behavior with concrete, as well as the axial compressive behavior of GFRP-RC columns with different reinforcement configurations, tie spacing, and bar sizes. The findings reveal that GFRP bars demonstrate a comparable, if not superior, compressive capacity to traditional steel bars, significantly contributing to the load-bearing capacity of concrete columns. The study concludes with a set of recommendations for further exploration, underscoring the potential of GFRP bars in revolutionizing the construction industry.

Artificial intelligence and machine learning are increasingly applied in civil engineering to predict material performance and optimize mix design. This study compares three ensemble learning approaches, Boosting, Bagging, and Stacking, for predicting the slump and compressive strength (CS) of eco-friendly mortars produced in the laboratory by partially replacing cement with glass powder (GP) and reinforcing with flax fibers (FF) and polypropylene fibers (PPF). Seven input variables were considered: the amounts of cement, GP, water, water-to-binder ratio (W/B), FF, PPF, and the superplasticizer dosage. A database of 580 experimental mixtures was created and used to train six predictive models: XGBoost and LightGBM (Boosting), Random Forest and Extra Trees (Bagging), and two hybrid Stacking models. Model performance was assessed using statistical metrics (R², RMSE, MAE), and SHAP (Shapley Additive Explanations) analysis was conducted to enhance interpretability and assess the influence of each input variable. This research is among the first to model both fresh (spread) and hardened (CS) properties of sustainable mortars with dual reinforcement using natural and synthetic fibers. The study highlights the critical role of W/B and SP dosage in workability and demonstrates the beneficial effect of GP in densifying the matrix and improving strength. The addition of FF and PPF enhanced CS by improving crack control, although higher fiber contents led to reduced workability. Overall, the study delivers both an original dataset and innovative mortar formulations, while demonstrating that ensemble ML techniques-particularly Stacking-offer superior predictive capability and practical support for eco-mortar mix design. This study is limited to short-term properties and a specific dataset; future work will expand the database and include durability assessments for broader applicability.