The fact that bitumens behave as non-Newtonian fluids results in non-linear relationships between their near-infrared (NIR) spectra and the physico-chemical properties that define their consistency (viz. penetration and viscosity). Determining such properties using linear calibration techniques [e.g. partial least-squares regression (PLSR)] entails the previous transformation of the original variables by use of non-linear functions and employing the transformed variables to construct the models. Other properties of bitumens such as density and composition exhibit linear relationships with their NIR spectra. Artificial neural networks (ANNs) enable modelling of systems with a non-linear property-spectrum relationship; also, they allow one to determine several properties of a sample with a single model, so they are effective alternatives to linear calibration methods. In this work, the ability of ANNs simultaneously to determine both linear and non-linear parameters for bitumens without the need previously to transform the original variables was assessed. Based on the results, ANNs allow the simultaneous determination of several linear and non-linear physical properties typical of bitumens.
|Publication status||Published - 5 Apr 2001|