Structural Health Monitoring of Partially Replaced Carbon Fabric-Reinforced Concrete Beam

Author:

Malathy Ramalingam1ORCID,James Jenifar Monica1,Prabakaran Mayakrishnan23ORCID,Kim Ick Soo4ORCID

Affiliation:

1. Department of Civil Engineering, Sona College of Technology, Salem 636005, India

2. Institute for Fiber Engineering and Science (IFES), Interdisciplinary Cluster for Cutting Edge Research (ICCER), National University Corporation Shinshu University, Nagano 386-8567, Japan

3. Department of Biomaterials, Saveetha Dental College and Hospitals, SIMATS, Saveetha University, Chennai 600077, India

4. Nano Fusion Technology Research Lab, Interdisciplinary Cluster for Cutting Edge Research (ICCER), Division of Frontier Fibers, Institute for Fiber Engineering (IFES), Shinshu University, Nagano 386-8567, Japan

Abstract

Textile-reinforced concrete (TRC) is a composite concrete material that utilizes textile reinforcement in place of steel reinforcement. In this paper, the efficacy of the partial replacement of steel reinforcement with textile reinforcement as a technique to boost the flexural strength of reinforced concrete (RC) beams was experimentally investigated. To increase the tensile strength of concrete, epoxy-coated carbon textile fabric was used as a reinforcing material alongside steel reinforcement. Beams were cast by partially replacing the steel reinforcement with carbon fabric. Partially replaced carbon fabric-reinforced concrete beams of size 1000 × 100 × 150 mm3 were cast by placing the fabrics in different layers. A four-point bending test was used to test cast beams as simply supported until failure. Then, 120 ohm strain gauges were used to study the stress–strain behavior of the control and TRC beams. Based on this experimental study, it was observed that 50% and 25% of the steel replaced with carbon fabric beams performed better than the conventional beam. ABAQUS software was used for numerical investigation. For the load deflection characteristics, a good agreement was found between the experimental and numerical results. Based on the experimental analysis carried out, a prediction model to determine the ultimate load-carrying capacity of TRC beams was created using an Artificial Neural Network (ANN).

Publisher

MDPI AG

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