Krill herd-based optimal neural network for analysing safety and quality performance at construction site. (27th June 2019)

• Record Type: Journal Article
• Authors: Balamurugan, Sivasubramanian; Sundar, Mallaian Lenin
• Is Part Of: International journal of rapid manufacturing. Volume 8:Number 4(2019)
• Page Start: 345
• Page End: 363
• Publication Date: 2019-06-27
• Subjects: construction -- safety -- neural network -- optimisation; Manufacturing processes -- Periodicals; 670.4205
• Languages: English
• ISSNs: 1757-8817

Abstract

In construction exertion, an organisation's capacity to convey a quality item in a protected way is the way to business achievement. In order to better comprehend what adds to productive quality and safety programs in construction. The vast majority of the researchers have focused on examining optimisation models to create optimal construction site format offered inspired algorithms. This paper analysed the safety measures in a construction site for high-quality. Here, two distinctive soft computing methodologies are artificial neural network (ANN) and optimisation model. This expectation investigation considers two distinct parameters like reworkers and defects in a construction site. For improving the performance enhance hidden layer and neurons in ANN structure utilising krill herd optimisation strategy so the proposed model as krill heard neural network (KHNN). From this examination, get least mean square error (MSE) and maximum accuracy as 88, 95.26% compared with our existing techniques.