## 3 Machine Learning Methods
### Overview of Machine Learning Algorithms
#### 3.1.3 ANN Approach  
ANN approaches simulate the microstructure (neurons) of a biological nervous system [67]. ANNs are computational processing information networks emulating the biological connection between neurons. In artificial neural networks, the models perform a major role in performing neural activities [68]. The motivation in developing artificial neural networks (ANNs) originated from emulation of the human brain to implement neural intelligent tasks analogous to that of the human brain [69]. Neurons are the human brain's basic unit, which underpins the capacity to memorize and think based on experience. These cells are present in the form of neuron networks. Generally, these cells receive input from othersources, perform non-linear neural activities and generate finalized output. In a similar way to other fields, ANNs are also utilized in engineering applications [70]. Recently, these neural activities have been found to be successful in civil engineering structural detection applications, identification behavior of the structural system, observation of groundwater, and prediction of mechanical properties. ANNs are comprised of a large number of dense parallel linkages. These cells receive data in the form of weighted inputs and then employ an activation function to communicate the weighted output to other neurons, resulting in the activation of a cascade of neurons. These neural activities are made up of one or more layers of activity. The multilayer perceptron network is commonly employed in brain processes such as memory and learning. These input parameters, which have been established based on previous experience, are processed using neural layer processing systems. The perception reaction is generated based on the number of input variables, the number of input layers, and the result. The input, hidden, and output layers are the three layers of a network with the addition of three layers. The hidden layer can be located between the input layer and the output layer, and may be part of a larger network of hidden layers than the input layer. A single hidden layer can be used to solve some of the problems with perceptrons; nevertheless, it is more important and more successful to use more than one hidden layer [71]. Figure 5 is a representation of a neural network architecture, which includes one input layer, two hidden layers, and one output layer, among other elements. Every neuron in the layers, except the input layer, begins by analyzing a linear combination in conjunction with a bias. The neurons then compute nonlinear functions in the buried layers of their input, which they then feedback to the computer. It is necessary to point out that a sigmoid function is a non-linear function [72]. ANNs mainly consists of five parts, including input, activation functions, sum function, weights, and output. ANNs can train their model by adapting its weight factor from outside the network or by adjusting within the layer in response to input [74]. The external source is used to provide input parameter information while the input's outcome set can be described by the weight of the values. The submission of inputs and weights with bias is characterized as a sum function. The mathematical expression of the weighted sum function is calculated using Equation (1). \[\left(net\right)_{j}=\sum_{i=1}^{n}w_{ij}x_{i}+b \tag{1}\]  
The activation function processes the net input which is found after submission from the sum function and determines the output of the cell. Normally sigmoid, ramp and Gaussian functions are the most popular activation functions. A schematic diagram of a multilayer perceptron, showing input, sum function with sigmoid activation, and output,  
Figure 5: Execution process of ANN model [73]. is shown in Figure 6[75]. The output of j neuron based on input variables is computed by employing sigmoid functions as follows. The sigmoid function is a non-linear activation function, except the input layer activates in every layer. The overall net out-based output of j neuron is obtained using Equation (2).