Holistic Approach to Decision Making in the Formulation and Selection of Anti-Icing Products

Abstract

To effectively fight snow storms in the challenging funding environment, many maintenance agencies in North America have started to produce their own anti-icing liquids, instead of procuring commercial anti-icers. This work demonstrates a systematic approach to collaborative, data-driven, and multicriteria decision making by conducting a set of laboratory tests to assess twenty blended chloride-based anti-icing formulations. The laboratory data were then used to establish predictive models correlating the multiple design parameters with the anti-icer performance and effects or with an anti-icer composite index. The authors used artificial neural networks for modeling and examined anti-icer performance (characteristic temperature and ice-melting capacity at 30 and 15 degrees F (-1.1 and -9.4 degrees C), respectively) and effects (splitting tensile strength of concrete after ten freeze-thaw cycles and corrosivity to mild steel) as a function of the formulation design. The anti-icer composite index was calculated for four different user priority scenarios (cost-first, performance-first, impacts-first, or a balanced approach), each of which placed a different set of decision weights on various target attributes. Three-dimensional response surfaces were then constructed to illustrate such predicted correlations and to guide the direction for formulation improvements. DOI: 10.1061/(ASCE)CR.1943-5495.0000039. (C) 2012 American Society of Civil Engineers.