Abstract

Abstract Maintenance agencies are continually challenged to provide a high level of service and improve safety and mobility of winter roads in a cost-effective manner while minimizing corrosion and other adverse effects to the environment. This study investigated the baseline performance data of typical chloride deicers used on highways, by conducting the Modified SHRP (Strategic Highway Research Program) Ice Melting Test of select solid chemicals and liquid deicers at − 1 °C (30 °F), − 9 °C (15 °F), and − 18 °C (0 °F), respectively. The thermal properties and ice melting performance of solid chemicals and liquid deicers were also tested in the laboratory, and the effect of blending two chloride deicer solutions with or without an agro-based product was explored. Furthermore, this work aims to demonstrate the feasibility of using an electrochemical corrosion test as a supplement to the gravimetric corrosion test. It also aims to shed light on the correlations between the composition and the corrosivity and performance of deicers respectively. To this end, artificial neural networks (ANNs) were used to establish predictive models and to quantify such cause-and-effect relationships. One ANN model was established to correlate the electrochemical corrosion data (along with solution conductivity) with those from the gravimetric test method. Two additional ANN models were established to achieve better understanding of the correlation between the deicer composition (type, chloride and inhibitor concentrations, pH, and electrical conductivity) and their corrosivity and performance respectively. According to the modeling, there are strong correlations inherent in the deicer samples, whereas the trends differ as a function of the deicer type and the solution conductivity. The established ANN models were then used for numerical investigations on the parameters affecting the deicer properties and for quality assurance of deicers or enhancing deicer design.

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