Recycled aggregate concrete (RAC) is a promising solution to address the challenges raised by concrete production. However, the current lack of pertinent design rules has led to a hesitance to accept structural members made with RAC. It would entail even more difficulties when facing application scenarios where brittle failure is possible (e.g., beam in shear). In this paper, existing major shear design formulae established primarily for conventional concrete beams were assessed for RAC beams. Results showed that when applied to the shear test database compiled for RAC beams, those formulae provided only inaccurate estimations with surprisingly large scatter. To cope with this bias, machine learning (ML) techniques deemed as potential alternative predictors were resorted to. First, a Grey Relational Analysis (GRA) was carried out to rank the importance of the parameters that would affect the shear capacity of RAC beams. Then, two contemporary ML approaches, namely, the artificial neural network (ANN) and the random forest (RF), were leveraged to simulate the beams’ shear strength. It was found that both models produced even better predictions than the evaluated formulae. With this superiority, a parametric study was undertaken to observe the trends of how the parameters played roles in influencing the shear resistance of RAC beams. The findings indicated that, though less influential than the structural parameters such as shear span ratio, the effect of the replacement ratio of recycled aggregate (RA) was still significant. Nevertheless, the value of v_c/(f_c)^1/2 (i.e., the shear contribution from RAC normalized with respect to the square root of its strength) predicted by the ML-based approaches appeared to be insignificantly affected by the replacement level. Given the existing inevitable large experimental scatter, more shear tests are certainly needed and, for safe application of RAC, using partial factors calibrated to consider the uncertainty is feasible when designing the shear strength of RAC beams. Some suggestions for future works are also given at the end of this paper.

The shear transfer mechanism of steel fiber reinforced concrete (SFRC) beams without stirrups is still not well understood. This is demonstrated herein by examining the accuracy of typical empirical formulas for 488 SFRC beam test records compiled from the literature. To steer clear of these cognitive limitations, this study turned to artificial intelligence (AI) models. A gray relational analysis (GRA) was first conducted to evaluate the importance of different parameters for the problem at hand. The outcomes indicate that the shear capacity depends heavily on the material properties of concrete, the amount of longitudinal reinforcement, the attributes of steel fibers, and the geometrical and loading characteristics of SFRC beams. After this, AI models, including back-propagation artificial neural network, random forest and multi-gene genetic programming, were developed to capture the shear strength of SFRC beams without stirrups. The findings unequivocally show that the AI models predict the shear strength more accurately than do the empirical formulas. A parametric analysis was performed using the established AI model to investigate the effects of the main influential factors (determined by GRA) on the shear capacity. Overall, this paper provides an accurate, instantaneous and meaningful approach for evaluating the shear capacity of SFRC beams containing no stirrups.

Recycled concrete aggregates (RCAs) generated from construction and demolition activities have been recognized as a feasible alternative to natural aggregates (NAs). Naturally, the columns fabricated with reinforced recycled concrete (RRC) have been proposed and investigated to promote the structural use of recycled aggregate concrete (RAC). There is still, however, very limited modeling research available to reproduce, accurately and efficiently, the seismic response of RRC columns under lateral cyclic loading; proper evaluations are also lacking on addressing the columns’ seismic behaviors. To fill some of those research gaps, a fiber-based numerical model is developed in this study and then validated with the experimental results published in the literature. Subsequently, the numerical model justified is applied to carry out a comprehensive parametric study to examine the effects of a range of variables on the hysteretic characteristics of RRC columns. Furthermore, a grey relational analysis is conducted to establish quantifiable evidence of key variable sensitivities. The evaluation results imply that the use of the additional water method (AWM) for manufacturing RAC is likely to reduce the lateral load-carrying capacity of the RRC columns (up to 10%), whereas the opposite would occur if a conventional mixing procedure is adopted. Moreover, compared with other factors such as steel area ratio, the content of RCA replacement has a less remarkable effect on the seismic performance of the RRC columns. In general, the RRC columns possess acceptable seismic-resistant properties, and they can be used in earthquake-prone regions with confidence.

The addition of recycled aggregate (RA) in self-compacting concrete (SCC) offers compelling opportunities for green and high-end applications of concrete. However, research on the constitutive relationship of the resulting concrete (termed RASCC herein) is sparse. This motivates the current study to look at the compressive stress–strain behavior of RASCC and its variability. A total of 120 cylinders were prepared with waste concrete from different sources. It was found that the workability of the concrete did decrease with the use of RA, especially when recycled fine aggregate was incorporated. The addition of RA had a less pronounced impact on the ascending branch of the averaged stress–strain curve, but increased the steepness of the descending branch markedly. It was also observed that the RA incorporation reduced the modulus of elasticity, peak stress, strain at peak stress, and toughness of the finished concrete. Most notably and importantly, the variability of those four properties was much more sensitive to the incorporation of recycled fine aggregate than to recycled coarse aggregate. Nevertheless, controlling the replacement ratio and using high-strength RA can allow the products to achieve compressive behavior comparable to that of conventional self-compacting concrete. A stochastic stress–strain model for RASCC is developed and experimentally validated.

Alkali-activated concrete (AAC), produced from industrial by-products like fly ash and slag, offers a promising alternative to traditional Portland cement concrete by significantly reducing carbon emissions. Yet, the inherent variability in AAC formulations presents a challenge for accurately predicting its compressive strength using conventional approaches. To address this, we leverage machine learning (ML) techniques, which enable more precise strength predictions based on a combination of material properties and cement mix design parameters. In this study, we curated an extensive dataset comprising 1756 unique AAC mixtures to support robust ML-based modeling. Four distinct input variable schemes were devised to identify the optimal predictor set, and a comparative analysis was performed to evaluate their effectiveness. After this, we investigated the performance of several popular ML algorithms, including random forest (RF), adaptive boosting (AdaBoost), gradient boosting regression trees (GBRTs), and extreme gradient boosting (XGBoost). Among these, the XGBoost model consistently outperformed its counterparts. To further enhance the predictive accuracy of the XGBoost model, we applied four state-of-the-art optimization techniques: the Gray Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), beetle antennae search (BAS), and Bayesian optimization (BO). The optimized XGBoost model delivered superior performance, achieving a remarkable coefficient of determination (R²) of 0.99 on the training set and 0.94 across the entire dataset. Finally, we employed SHapely Additive exPlanations (SHAP) to imbue the optimized model with interpretability, enabling deeper insights into the complex relationships governing AAC formulations. Through the lens of ML, we highlight the benefits of the multi-faceted synergistic approach for AAC strength prediction, which combines careful input parameter selection, optimal hyperparameter tuning, and enhanced model interpretability. This integrated strategy improves both the robustness and scalability of the model, offering a clear and reliable prediction of AAC performance.