Feasibility of Artificial Neural Network in Civil Engineering

Abstract

An Artificial neural network (ANN) is an information processing hypothesis that is stimulated by the way natural nervous system, such as brain, process information. The using of artificial neural network in civil engineering is getting more and more credit all over the world in last decades. This soft computing method has been shown to be very effective in the analysis and solution of civil engineering problems. It is defined as a body which works out the more and more complex problem through sequential algorithms. It is designed on the basis of artificial intelligence which is proficient of storing more and more information's. In this work, we have investigated the various architectures of ANN and their learning process. The artificial neural network-based method was widely applied to the civil engineering because of the strong non-linear relationship between known and unknown of the problems. They come with good modelling in areas where conventional approaches (finite elements, finite differences etc.) require large computing resources or time to solve problems. These includes to study the behaviour of building materials, structural identification and control problems, in geo-technical engineering like earthquake induced liquefaction potential, in heat transfer problems in civil engineering to improve air quality, in transportation engineering like identification of traffic problems to improve its flexibility , in construction technology and management to estimate the cost of buildings and in building services issues like analyzing the water distribution network etc. Researches reveals that the method is realistic and it will be fascinated for more civil engineering applications.

Analysis of Laminated Composite Plates: A Comprehensive Bibliometric Review

“…Artificial neural networks are widely used in engineering due to their efficacy in handling strong non-linear relationships [243]. Especially valuable when conventional methods demand significant computational resources or time, these networks find applications in diverse areas, including building material studies, structural identification (e.g., analysis of laminated composite structures), geotechnical engineering (e.g., earthquake-induced liquefaction potential), civil engineering heat transfer problems, transportation engineering (e.g., traffic problem identification), construction technology and management (e.g., estimating building costs), and building services (e.g., analyzing water distribution networks) [244]. Rjoub et al [245] used an ANN model to forecast the frequency of porous FGM plates with a side crack.…”

Results of Application of Artificial Neural Networks in Predicting Geo-Mechanical Properties of Stabilised Clays—A Review

“…The input value of a given neuron is simply obtained by computing the weighted sum of the inputs from connected neurons with the addition of a bias [15,16]. This output of the weighted summation then becomes the input for the activation function-a linear or non-linear function [17,18].…”

Mine subsidence prediction using gene expression programming based on multivariable symbolic regression

“…Developments in machine learning fields have created several new computer-aided data mining and hybrid approaches applicable for prediction problems. Artificial Neural Networks (ANN) have extensively been used to develop the nonlinear relationships between input parameters in mining and other geotechnical engineering systems [18][19][20][21]. A genetic algorithm (GA) is a robust stochastic approach for predicting various civil and mining problems.…”