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Experimental Investigation on the Mechanical Properties of Natural Fiber Reinforced Concrete
Recently, addition of various natural fibers to high strength concrete has aroused great interest in the field of building materials. This is because natural fibers are much cheaper and locally available, as compare to synthetic fibers. Keeping in view, this current research conducted mainly focuses on the static properties of hybridized (sisal/coir), sisal and coir fiber-reinforced concrete. Two types of natural fibers sisal and coir were used in the experiment with different lengths of 10, 20 and 30 mm and various natural fiber concentrations of 0.5%, 1.0%, and 1.5% by mass of cement, to investigate the static properties of sisal fiber reinforced concrete (SFRC), coir fiber reinforced concrete (CFRC) and hybrid fiber reinforced concrete (HFRC). The results indicate that HFRC has increased the compressive strength up to 35.98% with the length of 20 mm and with 0.5% concentration, while the CFRC and SFRC with the length of 10 mm and with 1% concentration have increased the compressive strength up to 33.94% and 24.86%, respectively. On another hand, the split tensile strength was increased by HFRC with the length of 20 mm and with 1% concentration, CFRC with the length of 10 mm and with 1.5% concentration, and SFRC with the length of 30 mm and with 1% concentration have increased up to 25.48%, 24.56% and 11.80%, respectively, while the HFRC with the length of 20 mm and with 0.5% concentration has increased the compressive strength of concrete but has decreased the split tensile strength up to 2.28% compared to PC. Overall, using the HFRC with the length of 20 mm and with 1% concentration provide the maximum output in terms of split tensile strength. Graphical Abstract Experimental Investigation on the Mechanical Properties of Natural Fiber Reinforced Concrete
Journal Article
Fibre-Reinforced Polymer Reinforced Concrete Members under Elevated Temperatures: A Review on Structural Performance
Several experimental and numerical studies have been conducted to address the structural performance of FRP-reinforced/strengthened concrete structures under and after exposure to elevated temperatures. The present paper reviews over 100 research studies focused on the structural responses of different FRP-reinforced/strengthened concrete structures after exposure to elevated temperatures, ranging from ambient temperatures to flame. Different structural systems were considered, including FRP laminate bonded to concrete, FRP-reinforced concrete, FRP-wrapped concrete, and concrete-filled FRP tubes. According to the reported data, it is generally accepted that, in the case of insignificant resin in the post curing process, as the temperature increases, the ultimate strength, bond strength, and structure stiffness reduce, especially when the glass transition temperature Tg of the resin is approached and exceeded. However, in the case of post curing, resin appears to preserve its mechanical properties at high temperatures, which results in the appropriate structural performance of FRP-reinforced/strengthened members at high temperatures that are below the resin decomposition temperature Td. Given the research gaps, recommendations for future studies have been presented. The discussions, findings, and comparisons presented in this review paper will help designers and researchers to better understand the performance of concrete structures that are reinforced/strengthened with FRPs under elevated temperatures and consider appropriate approaches when designing such structures.
Journal Article
Multivariable Regression Strength Model for Steel Fiber-Reinforced Concrete Beams under Torsion
Torsional behavior and analysis of steel fiber reinforced concrete (SFRC) beams is investigated in this paper. The purpose of this study is twofold; to examine the torsion strength models for SFRC beams available in the literature and to address properly verified design formulations for SFRC beams under torsion. A total of 210 SFRC beams tested under torsion from 16 different experimental investigations around the world are compiled. The few strength models available from the literature are adapted herein and used to calculate the torsional strength of the beams. The predicted strength is compared with the experimental values measured by the performed torsional tests and these comparisons showed a room for improvement. First, a proposed model is based on optimizing the constants of the existing formulations using multi-linear regression. Further, a second model is proposed, which is based on modifying the American Concrete Institute (ACI) design code for reinforced concrete (RC) members to include the effect of steel fibers on the torsional capacity of SFRC beams. Applications of the proposed models showed better compliance and consistency with the experimental results compared to the available design models providing safe and verified predictions. Further, the second model implements the ACI code for RC using a simple and easy-to-apply formulation.
Journal Article
Systematic Review on the Creep of Fiber-Reinforced Concrete
Fiber-reinforced concrete (FRC) is increasingly used in structural applications owing to its benefits in terms of toughness, durability, ductility, construction cost and time. However, research on the creep behavior of FRC has not kept pace with other areas such as short-term properties. Therefore, this study aims to present a comprehensive and critical review of literature on the creep properties and behavior of FRC with recommendations for future research. A transparent literature search and filtering methodology were used to identify studies regarding creep on the single fiber level, FRC material level, and level of structural behavior of FRC members. Both experimental and theoretical research are analyzed. The results of the review show that, at the single fiber level, pull-out creep should be considered for steel fiber-reinforced concrete, whereas fiber creep can be a governing design parameter in the case of polymeric fiber reinforced concrete subjected to permanent tensile stresses incompatible with the mechanical time-dependent performance of the fiber. On the material level of FRC, a wide variety of test parameters still hinders the formulation of comprehensive constitutive models that allow proper consideration of the creep in the design of FRC elements. Although significant research remains to be carried out, the experience gained so far confirms that both steel and polymeric fibers can be used as concrete reinforcement provided certain limitations in terms of structural applications are imposed. Finally, by providing recommendations for future research, this study aims to contribute to code development and industry uptake of structural FRC applications.
Journal Article
Interpretable Machine Learning Algorithms to Predict the Axial Capacity of FRP-Reinforced Concrete Columns
Fiber-reinforced polymer (FRP) rebars are increasingly being used as an alternative to steel rebars in reinforced concrete (RC) members due to their excellent corrosion resistance capability and enhanced mechanical properties. Extensive research works have been performed in the last two decades to develop predictive models, codes, and guidelines to estimate the axial load-carrying capacity of FRP-RC columns. This study utilizes the power of artificial intelligence and develops an alternative approach to predict the axial capacity of FRP-RC columns more accurately using data-driven machine learning (ML) algorithms. A database of 117 tests of axially loaded FRP-RC columns is collected from the literature. The geometric and material properties, column shape and slenderness ratio, reinforcement details, and FRP types are used as the input variables, while the load-carrying capacity is used as the output response to develop the ML models. Furthermore, the input-output relationship of the ML model is explained through feature importance analysis and the SHapely Additive exPlanations (SHAP) approach. Eight ML models, namely, Kernel Ridge Regression, Lasso Regression, Support Vector Machine, Gradient Boosting Machine, Adaptive Boosting, Random Forest, Categorical Gradient Boosting, and Extreme Gradient Boosting, are used in this study for capacity prediction, and their relative performances are compared to identify the best-performing ML model. Finally, predictive equations are proposed using the harmony search optimization and the model interpretations obtained through the SHAP algorithm.
Journal Article