Modelling efficiency of industrial waste utilised for microsurfacing using artificial neural networks Online publication date: Thu, 31-Jan-2019
by Rajesh Gujar; Gautam Dadhich
International Journal of Environment and Waste Management (IJEWM), Vol. 23, No. 2, 2019
Abstract: There was a need develop a model to determine the optimum proportion of waste materials which ensure the quality of designed micro surfacing mix. Artificial neural network (ANN) has been used to create a model for prediction of the optimum proportion of mineral filler and additive due to non-linearity of data. In the present study, since there are five inputs (dimensions) and two outputs having nonlinear relationship, ANN modelling suits to be best for output prediction. The Bayesian regularisation algorithm was used to train the network. The micro surfacing characteristics are a function of five input performance parameters namely mixing time, cohesion (30 min), cohesion (60 min), setting time and wet track abrasion test. The two output parameters are filler proportion and control additive balance. The model tool developed shall ease in determining the mix design parameters, i.e. filler content and additive percentage to achieve the desired effect.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Environment and Waste Management (IJEWM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook