Open AccessUse of Artificial Neural Network for Initial design of steel structures
Author(s) -
H. Mote,
Sanjeev Kumar
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/660/1/012064
Subject(s) - artificial neural network , computer science , process (computing) , set (abstract data type) , engineering design process , optimal design , design process , iterative and incremental development , mathematical optimization , artificial intelligence , machine learning , work in process , engineering , mathematics , mechanical engineering , operations management , software engineering , programming language , operating system
Design of steel structures is an iterative process that often turns complicated when requires initial guess. A good initial design guess that comes from past design experience can considerably reduce the number of subsequent analysis and design cycles. It is difficult to form declarative rules to express human intuitions and past experience. Another problem in design process is arriving at optimal solution, where optimization process in most cases is computationally expensive and time consuming. Artificial Neural Network (ANN) is a promising computational model that can perform cognitive tasks, such as learning and optimization. This paper focuses on application of ANN in design so as to make design process more efficient. The aim is to train ANN to arrive at optimal solution by considering design constraints along with practical. This is illustrated with an example of design of compression member which primarily requires initial guess. The economy in design is tried to obtain by providing training set of optimal solutions to a multilayer neural network. The design process followed to generate training set is in accordance with IS: 800-2007. The solutions given by ANN are approximate but have reasonable agreement with the expected solutions.