Multi-objective optimisation of mild steel embossed plate shear connector using artificial neural network-integrated genetic algorithm

Abstract

A hybrid artificial neural network (ANN), integrated with a genetic algorithm (GA), was used for the multi-objective optimisation of mild steel embossed plate shear connector and the optimisation responses were examined by the push-out tests in accordance with BS 5400-5:2005 code. This innovative shear connection enables a greater frictional interlocking with the composite girder, allowing it to sustain a higher transverse load and gain a better bearing capacity at the same time retaining ductility. To obtain the ultimate load and relative slip data, the Taguchi experimental design was used, which took into account the three most influencing parameters: length, height, and thickness of the shear connector, which are the input elements for optimisation. The ANN-integrated GA approach is used for multi-objective optimisation, in which optimisation begins with modelling of the ANN and its output is optimised using GA. Five multi-layered (three) perceptron models that have different number of neurons in the hidden layer are developed using a feed-forward backpropagation learning algorithm. Logarithmic sigmoid (Logsig) and linear (Purelin) transfer functions from the Levenberg–Marquardt approach are adopted in this model. Using the best ANN model response values (3−11−2), multi-objective optimisation with GA was performed. The following optimum process parameters are established in this study: length, 40 mm; height, 124.97 mm; thickness of the connector, 12 mm for the maximum ultimate load of 431.991 kN and minimised relative slip of 2.056 mm, both with more than 95 % confidence level.