Strength prediction of hybrid fiber reinforced self-compacting concrete using optimal recurrent neural network

ABSTRACT Self-Compacting Concrete (SCC) is a special type of concrete which does not require external mechanical compaction and it compacts by its own self weight. Due to its high flow ability and high resistance property, it is being used in congested reinforcement areas and high rise structures. Inclusion of fiber in SCC is an effective solution to manage the heavy load conditions. SCC with fiber addition results in enhancing tensile and flexural strength properties. This present research aims to predict the strength behaviour of SCC using hybrid fibers. Furthermore, the automatic validation is achieved by Optimal Recurrent Neural Network (ORNN) technique which has been developed to analyze the compressive strength, tensile strength and flexural strength. The ORNN is a combination of Recurrent Neural Network (RNN) and Dingo Optimizer (DO). Moreover, in the aspect of durability behaviour SCC with hybrid fiber concrete is made and heated at a temperature of 210°C, 320°C, 530°C and 790°C after their curing period as per ISO 834 guidelines. Test results proved that when the concrete is subjected to high temperature, there was decrease in compressive strength properties. Moreover, the present research findings are implemented in MATLAB for computing the strength properties and the results are discussed elaborately. In order to validate the proposed method, 80% of data is used for training and remaining 20% of data is utilized for testing. This research study is highly valuable towards Civil and Structural Engineering, particularly in the design and analysis of fire-resistant infrastructure, high-performance concrete elements, and intelligent construction materials.