Application of Artificial Neural Networks in Civil Engineering

Artificial Neural Networks (ANN): Artificial neural networks, usually simply called neural networks, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Basic Structure of ANNs: The idea of ANNs is based on the belief that working of human brain by making the right connections, can be imitated using silicon and wires as living neurons and dendrites.

Artificial Neural Network (ANN) has emerged with advancement of Information and Communication technology and biological sciences during last decades. The aim is to utilize technology and construct machines that will work like brain of humans. The internal architectural requirements of such a machine is to have huge simultaneous memory and storage in consistent with intensive processing power to cater the ambiguous information and behave like human brain. ANN has broad range of applications in today's business and IT industry. This paper aims to investigate the working of ANN and its applications in real environment.

Indian Journal of Automation and Artificial Intelligence

Artificial Neural Network (ANN) has emerged with advancement of Information and Communication technology and biological sciences during last decades. The aim is to utilize technology and construct machines that will work like brain of humans. The internal architectural requirements of such a machine is to have huge simultaneous memory and storage in consistent with intensive processing power to cater the ambiguous information and behave like human brain. ANN has broad range of applications in today’s business and IT industry. This paper aims to investigate the working of ANN and its applications in real environment.

umerous advances have been made in developing intelligent systems, some inspired by biological neural networks. N Researchers from many scientific disciplines are designing artificial neural networks (A"s) to solve a variety of problems in pattern recognition, prediction, optimization, associative memory, and control (see the "Challenging problems" sidebar).

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well. This paper gives overview of Artificial Neural Network, working & training of ANN. It also explain the application and advantages of ANN.

A STUDY, 2018

First step towards AI is taken by Warren McCulloch a neurophysist and a mathematician Walter Pitts. They modelled a simple neural network with electrical circuits and got the results very accurate and derived a remarkable ability of neurons to perceive information from complicated and imprecise data. During the present study it was observed that trained neural network expert in analyzing the information has been provided with other advantages as Adaptive learning, Real Time operation, self-organization and Fault tolerance as well. Apart from convectional computing, neural networking use different processing units (Neurons) in parallel with each other. These need not to be programmed. They function just like human brain. We need to give it examples to solve different problems and these examples must be selected carefully so that it would not be waste of time.we use combination of neural networking and computational programming to achieve maximal efficiency right now but neural networking will eventually take over in future. We introduced artificial neural networking in which electronic models where used as neural structure of brain. Computers can store data as ledgers etc. but have difficulty in recognizing patterns but brain stores information as patterns. Further as artificial neural networking was introduced which has artificial neurons who act as real neurons and do functions as they do. They are used for speech, hearing, reorganization, storing information as patterns and many other functions which a human brain can do. These neural networks were combined and dynamically self-combined which is not true for any artificial networking. These neurons work as groups and sub divide the problem to resolve it. These are grouped in layers and it is art of engineering to make them solve real world problems. The most important thing is the connections between the neurons, it is glue to system as it is excitation inhibition process as the input remains constant one neuron excites while other inhibits as in subtraction addition process. Basically, all ANN have same network that is input, feedback or hidden and output.

An artificial neural network is a system based on the operation of biological neural networks, in other words, is an emulation of biological neural system.

IUPAC Standards Online, 2017

This research work presents new development in the field of natural science, where comparison is made theoretically on the efficiency of both classical regression models and that of artificial neural network models, with various transfer functions without data consideration. The results obtained based on variance estimation indicates that ANN is better which coincides with the results of Authors in the past on the efficiency of ANN over the traditional regression models. The certain conditions required for ANN efficiency over the conventional regression models were noted only that the optimal number of hidden layers and neurons needed to achieve minimum error is still open to further investigation.

The use of neural network architecture in deep learning models is called as Artificial Neural Network (ANN). It is one of the most powerful machine learning algorithms applied to tasks across many domains. (finance, humanities, science. research and academics etc.). An ANN is a form of computation inspired by the structure and function of brain. [ Padhy, 2005] In this paper, we concentrate on the fundamentals of human neurons and how they are applied to artificial neurons to understand the principles of ANNs.

—An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well. This paper gives overview of Artificial Neural Network, working & training of ANN. It also explain the application and advantages of ANN.