Analysis, Behavior, Strengthening and repairing of Reinforced Concrete Corbels: Comprehensive Review

In this review, an extensive survey on the theoretical models and approaches that were proposed in literature to study the behavior of RC corbels has been presented. Such approaches included the shear friction approach, strut and tie model, finite element and Neural networks. Moreover, the review has been extended to consider the studies conducted experimentally by researchers and scholars to investigate the response of the RC corbels. Furthermore, various proposals that were suggested regarding strengthening and repairing of RC corbels have been discussed. Different materials have been used to improve the performance of RC corbels, such as steel fibers, FRP composites, NSM steel bars, NSM CFRP bars and composite sections have been considered. The most important findings reported in the relevant literature have been summarized. In addition, several recommendations to extend the studies concerning the RC corbel to improve the knowledge about the behavior of this significant structural member have been presented

Analysis, Behavior, Strengthening and repairing of Reinforced Concrete Corbels: Comprehensive Review

In this review, an extensive survey on the theoretical models and approaches that were proposed in literature to study the behavior of RC corbels has been presented. Such approaches included the shear friction approach, strut and tie model, finite element and Neural networks. Moreover, the review has been extended to consider the studies conducted experimentally by researchers and scholars to investigate the response of the RC corbels. Furthermore, various proposals that were suggested regarding strengthening and repairing of RC corbels have been discussed. Different materials have been used to improve the performance of RC corbels, such as steel fibers, FRP composites, NSM steel bars, NSM CFRP bars and composite sections have been considered. The most important findings reported in the relevant literature have been summarized. In addition, several recommendations to extend the studies concerning the RC corbel to improve the knowledge about the behavior of this significant structural member have been presented

Analysis, Behavior, Strengthening and repairing of Reinforced Concrete Corbels: Comprehensive Review

In this review, an extensive survey on the theoretical models and approaches that were proposed in literature to study the behavior of RC corbels has been presented. Such approaches included the shear friction approach, strut and tie model, finite element and Neural networks. Moreover, the review has been extended to consider the studies conducted experimentally by researchers and scholars to investigate the response of the RC corbels. Furthermore, various proposals that were suggested regarding strengthening and repairing of RC corbels have been discussed. Different materials have been used to improve the performance of RC corbels, such as steel fibers, FRP composites, NSM steel bars, NSM CFRP bars and composite sections have been considered. The most important findings reported in the relevant literature have been summarized. In addition, several recommendations to extend the studies concerning the RC corbel to improve the knowledge about the behavior of this significant structural member have been presented

Support vector machines in structural engineering: a review

Recent development in data processing systems had directed study and research of engineering towards the creation of intelligent systems to evolve models for a wide range of engineering problems. In this respect, several modeling techniques have been created to simulate various civil engineering systems. This study aims to review the studies on support vector machines (SVM) in structural engineering and investigate the usability of this machine learning based approach by providing three case studies focusing on structural engineering problems. Firstly, the concept of SVM is explained and then, the recent studies on the application of SVM in structural engineering are summarized and discussed. Next, we performed three case studies using the experimental studies provided. Applicability of SVM in structural engineering is confirmed by these case studies. The results showed that SVM is superior to various other learning techniques considering the generalization capability of produced model

Primary and secondary reinforcements in reinforced concrete corbels

The study is concerned with normal-strength concrete corbels. 30 such corbels were studied by finite element modelling and the variables considered include ratios of primary and secondary reinforcement, type of applied loading, vertical or horizontal. Finite element modelling with a software package LUSAS was used to analyse four series of corbels namely PV series (primary reinforcement with vertical loading), SV series (secondary reinforcement with vertical loading), PH series (primary reinforcement with horizontal loading) and SH series (secondary reinforcement with horizontal loading). The results indicate that corbels with neither primary reinforcement nor secondary reinforcement fail suddenly. In the case of PV series and SV series, corbels increase in ratio of primary and secondary reinforcement generally resulted in enhancement of strength and ductility when subjected to only vertical loading. This increase is significant up to 0.4% in the case of primary reinforcement and 0.3% in the case of secondary reinforcements. No noticeable change in ultimate load or ductility was observed for corbels in PH series and SH series. First published online: 24 Oct 201

Machine Learning Prediction of Shear Capacity of Steel Fiber Reinforced Concrete

The use of steel fibers for concrete reinforcement has been growing in recent years owing to the improved shear strength and post-cracking toughness imparted by fiber inclusion. Yet, there is still lack of design provisions for steel fiber-reinforced concrete (SFRC) in building codes. This is mainly due to the complex shear transfer mechanism in SFRC. Existing empirical equations for SFRC shear strength have been developed with relatively limited data examples, making their accuracy restricted to specific ranges. To overcome this drawback, the present study suggests novel machine learning models based on artificial neural network (ANN) and genetic programming (GP) to predict the shear strength of SFRC beams with great accuracy. Different statistical metrics were employed to assess the reliability of the proposed models. The suggested models have been benchmarked against various soft-computing models and existing empirical equations. Sensitivity analysis has also been conducted to identify the most influential parameters to the SFRC shear strength