Prediction of Punching Shear Strength using Metaheuristic Approach of Optimization

Paper Title

Prediction of Punching Shear Strength using Metaheuristic Approach of Optimization

Authors

Gaurav Sarkar, Suhail Ahmad Magray and Tanushree Sarkar, Dsce, Karnataka, India, GGV, Bilaspur, Chhattisgarh, India

Abstract

The relationship between technology and application of civil engineering is not a new concept. Over the years civil engineering has encountered a slew of issues, most of them have been solved with the aid of technology. Prediction of punching shear strength is one such problem statement which could be solved using a metaheuristic approach of optimization. Numerous experiments on the punching shear resistance of reinforced concrete slabs have been conducted by researchers, with positive findings. The actual service life will be shortened due to steel bars’ propensity for corrosion. The main goals of all organizations are to make civil engineering applications more valuable intrinsically so that people can use them to construct faster so that resources are used more effectively, and to ultimately improve people’s lives. Using evolutionary artificial neural networks, internal flat slabs of reinforced concrete can be predicted for their punching shear strength. It is a hybrid model of an artificial neural network (ANN) and a Genetic algorithm, a metaheuristic based on natural selection that is a subset of the larger category of evolutionary algorithms (EA). The experimental findings from 519 flat slabs tested by various authors starting in 1938 were used in this research. The model tries to predict the dependent feature, Punching shear resistance, using independent features such as Shape of the column cross section, Column side or smaller side, Larger side of the column, Average effective depth in X and Y directions, Average reinforcement ratio in X and Y directions, Column effective width, Effective width / Effective depth, Concrete compressive strength, and Steel yield strength. Sometimes, signals are altered at the receiving synapses, and the processing element adds the weighted inputs. Input from one neuron is sent to another (or output is sent to the outside world) if it reaches the threshold, and the cycle continues. The algorithm builds the subsequent population at each stage using members of the current generation. By using Selection, Crossover and Mutation, we can obtain a set of optimal parameters that aid in producing effective results. We also contrasted the accuracy attained using GA with other popularly employed optimizer types like SGD, ADAM and RMSProp. We have also made use of the benefits of the GA algorithm, such as its adaptability, understanding ability, and lack of computational complexity.

Keywords

Artificial Neural Network, Reinforced Concrete Flat Slab, Punching Shear Strength, Genetic Algorithm & Metaheuristic.
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