Alkali activated concretes have emerged as a prospective alternative to conventional concrete wherein diverse waste materials have been converted as valuable spin-offs. This paper presents a wide experimental study on the sustainability of employing waste sawdust as a fine/coarse aggregate replacement incorporating fly ash (FA) and granulated blast furnace slag (GBFS) to make high-performance cement-free lightweight concretes. Waste sawdust was replaced with aggregate at 0, 25, 50, 75, and 100 vol% incorporating alkali binder, including 70% FA and 30% GBFS. The blend was activated using a low sodium hydroxide concentration (2 M). The acoustic, thermal, and predicted engineering properties of concretes were evaluated, and the life cycle of various mixtures were calculated to investigate the sustainability of concrete. Besides this, by using the available experimental test database, an optimized Artificial Neural Network (ANN) was developed to estimate the mechanical properties of the designed alkali-activated mortar mixes depending on each sawdust volume percentage. Based on the findings, it was found that the sound absorption and reduction in thermal conductivity were enhanced with increasing sawdust contents. The compressive strengths of the specimens were found to be influenced by the sawdust content and the strength dropped from 65 to 48 MPa with the corresponding increase in the sawdust levels from 0% up to 100%. The results also showed that the emissions of carbon dioxide, energy utilization, and outlay tended to drop with an increase in the amount of sawdust and show more the lightweight concrete to be more sustainable for construction applications.
Fiber-reinforced polymers (FRP) are widely utilized to improve the efficiency and durability of concrete structures, either through external bonding or internal reinforcement. However, the response of FRP-strengthened reinforced concrete (RC) members, both in field applications and experimental settings, often deviates from the estimation based on existing code provisions. This discrepancy can be attributed to the limitations of code provisions in fully capturing the nature of FRP-strengthened RC members. Accordingly, machine learning methods, including gene expression programming (GEP) and multi-expression programming (MEP), were utilized in this study to predict the flexural capacity of the FRP-strengthened RC beam. To develop data-driven estimation models, an extensive collection of experimental data on FRP-strengthened RC beams was compiled from the experimental studies. For the assessment of the accuracy of developed models, various statistical indicators were utilized. The machine learning (ML) based models were compared with empirical and conventional linear regression models to substantiate their superiority, providing evidence of enhanced performance. The GEP model demonstrated outstanding predictive performance with a correlation coefficient (R) of 0.98 for both the training and validation phases, accompanied by minimal mean absolute errors (MAE) of 4.08 and 5.39, respectively. In contrast, the MEP model achieved a slightly lower accuracy, with an R of 0.96 in both the training and validation phases. Moreover, the ML-based models exhibited notably superior performances compared to the empirical models. Hence, the ML-based models presented in this study demonstrated promising prospects for practical implementation in engineering applications. Moreover, the SHapley Additive exPlanation (SHAP) method was used to interpret the feature's importance and influence on the flexural capacity. It was observed that beam width, section effective depth, and the tensile longitudinal bars reinforcement ratio significantly contribute to the prediction of the flexural capacity of the FRP-strengthened reinforced concrete beam.
Brittle shear failure of beam-column joints, especially during seismic events poses a significant threat to structural integrity. This study investigates the potential of steel fiber reinforced concrete (SFRC) in the joint core to enhance ductility and overcome construction challenges associated with traditional reinforcement. A non-linear finite element analysis (NLFEA) using ABAQUS software was conducted to simulate the behavior of SFRC beam-column joints subjected to cyclic loading. Ten simulated specimens were analyzed to discern the impact of varying steel fiber volume fraction and aspect ratio on joint performance. Key findings reveal that a 2% volume fraction of steel fibers in the joint core significantly improves post-cracking behavior by promoting ductile shear failure, thereby increasing joint toughness. While aspect ratio variations showed minimal impact on load capacity, long and thin steel fibers effectively bridge cracks, delaying their propagation. Furthermore, increasing steel fiber content resulted in higher peak-to-peak stiffness. This research suggests that strategically incorporating SFRC in the joint core can promote ductile shear failure, enhance joint toughness, and reduce construction complexities by eliminating the need for congested hoops. Overall, the developed NLFEA model proves to be a valuable tool for investigating design parameters in SFRC beam-column joints under cyclic loading.
The majority of experimental and analytical studies on fiber-reinforced polymer (FRP) confined concrete has largely concentrated on plain (unreinforced) small-scale concrete columns, on which the efficiency of strengthening is much higher compared with large-scale columns. Although reinforced concrete (RC) columns subjected to combined axial compression and flexural loads (i.e., eccentric compression) are the most common structural elements used in practice, research on eccentrically-loaded FRP-confined rectangular RC columns has been much more limited. More specifically, the limited research has generally been concerned with small-scale RC columns, and hence, the proposed eccentric-loading stress-strain models were mainly based on the existing concentric-loading models of FRP-confined concrete columns of small scale. In the light of such demand to date, this paper is aimed at developing a mathematical model to better predict the strength of FRP-confined rectangular RC columns. The strain distribution of FRP around the circumference of the rectangular sections was investigated to propose equations for the actual rupture strain of FRP wrapped in the horizontal and vertical directions. The model was accomplished using 230 results of 155 tested specimens compiled from 19 studies available in the technical literature. The test database covers an unconfined concrete strength ranging between 9.9 and 73.1 MPa, and section's dimension ranging from 100-300 mm and 125-435 mm for the short and long sides, respectively. Other test parameters, such as aspect ratio, corner radius, internal hoop steel reinforcement, FRP wrapping layout, and number of FRP wraps were all considered in the model. The performance of the model shows a very good correlation with the test results.
The development of self-compacting alkali-activated concrete (SCAAC) has become a hot topic in the scientific community; however, most of the existing literature focuses on the utilization of fly ash (FA), ground blast furnace slag (GBFS), silica fume (SF), and rice husk ash (RHA) as the binder. In this study, both the experimental and theoretical assessments using response surface methodology (RSM) were taken into account to optimize and predict the optimal content of ceramic waste powder (CWP) in GBFS-based self-compacting alkali-activated concrete, thus promoting the utilization of ceramic waste in construction engineering. Based on the suggested design array from the RSM model, experimental tests were first carried out to determine the optimum CWP content to achieve reasonable compressive, tensile, and flexural strengths in the SCAAC when exposed to ambient conditions, as well as to minimize its strength loss, weight loss, and UPVL upon exposure to acid attack. Based on the results, the optimum content of CWP that satisfied both the strength and durability aspects was 31%. In particular, a reasonable reduction in the compressive strength of 16% was recorded compared to that of the control specimen (without ceramic). Meanwhile, the compressive strength loss of SCAAC when exposed to acid attack minimized to 59.17%, which was lower than that of the control specimen (74.2%). Furthermore, the developed RSM models were found to be reliable and accurate, with minimum errors (RMSE < 1.337). In addition, a strong correlation (R > 0.99, R2 < 0.99, adj. R2 < 0.98) was observed between the predicted and actual data. Moreover, the significance of the models was also proven via ANOVA, in which p-values of less than 0.001 and high F-values were recorded for all equations.