Creating relation between bolt arrangements at bolted moment connections among cold-formed steel members using artificial neural network

In this paper, three artificial neural networks are presented using the experimental results from bolted moment connections among cold-formed steel members and the software MATLAB in order to predict the rotation at the connections. A common neural network which has a multilayer perceptron along with back propagation learning algorithm is applied in this research. Each of the networks consists of four layers including two hidden ones. The number of neurons in the first hidden layer is changed from 1 to 10 to achieve optimal results. The best results are obtained when the networks had 10, 10, and 9 neurons in the first hidden layer for column base and beam column connections (in positive and negative rotations), and they had the performance of 0.0001371, 0.00044, and 0.00047, respectively, after being trained in the software MATLAB. Thirty percent of the data from each test series were omitted randomly in order to verify the networks. The Mann–Whitney P value tests are 0.9933, 0.9393, and 0.9653 for column base and beam column connections (in positive and negative rotations), respectively.