Modelling of expansion ratio and half-life of foamed bitumen using gene expression programming

ABSTRACT

A typical foamed bituminous mixture design process involves production and evaluation of foamed bitumen over a range of foaming temperatures and water contents. Such a process of elaborate testing (over a range of temperatures and water contents) is a time- and resource-consuming exercise. This work presents a novel gene expression programming-based approach to predict the expansion ratio and half-life of foamed bitumen. To develop these predictive models, a database that consists of foamed bitumen characteristics (i.e. expansion ratio and half-life) and physico-chemical properties of bitumen was developed using six distinct binders (of different viscosity grades). Initially, all binders were tested for their physical properties and chemical composition. Furthermore, foamed bitumen characteristics of these binders were measured over the range of test temperatures (110°C to 200°C) and water contents (2% to 11%). The final database consisted of 166 observations (arising out of different combinations of 6 binder types, 6 water contents, and 8 test temperatures and an average of 3 replicates for each condition). Expressions to predict foamed bitumen characteristics have been developed based on temperature, water content, viscosity, and chemical composition using this database through the gene expression programming technique. The numerical values of goodness of fit parameters indicated that models based on physical properties are able to predict expansion ratio and half-life with reasonable accuracy. Furthermore, the use of information regarding chemical composition along with physical properties improved the accuracy of predictive models significantly. These expressions can be used to identify the feasible range of test conditions for the production of foamed bitumen before actual testing is commenced.

KEYWORDS:

• Cold mix technology
• foamed bitumen
• expansion ratio
• half-life
• gene expression programming

Acknowledgements

The authors thank IIT Delhi HPC facility for computational resources.

Disclosure statement

No potential conflict of interest was reported by the authors.