Journal of Cold Regions Engineering

In this paper, the key heat-transfer parameters, thermal conductivity, and thermal diffusivity of biocover material made of compost subjected to freeze-thaw cycles (FTCs) for a wide range of water contents is investigated by means of the transient method. The samples were kept at $-12°\mathrm{C}$ to freeze and at room temperature (23°C) to thaw. The results obtained show that FTCs have an impact on the thermal properties of compost biocovers. However, more than approximately 70% of the total thermal conductivity changes occurred in the first two FTCs. Moreover, it was found that a linear relation between thermal conductivity and degree of saturation is valid even under FTCs. Furthermore, variations in the thermal properties are due to the combined effect of three factors: volume change, grain-size distribution, and crack formation. Finally, an empirical equation has been developed to predict thermal conductivity changes versus FTCs.

This paper investigates the effect of different dosages of fly ash (FA) and silica fume (SF) in self-compacting mortars (SCMs) on the freeze-thaw (F-T) resistance as well as fresh and hardened properties. Nine mortar mixtures were prepared at $640\mathrm{kg}/{\mathrm{cm}}^3$ of unit weight and water/binder (w/b) ratio between 0.43 and 0.50. All SCMs contained either FA or SF, but the control mixture contained only portland cement. The fresh properties of SCMs were determined using viscosity, slump flow, and V-funnel flow time tests. The compressive strength of SCMs containing SF was generally found to be higher than that of SCMs with FA and the control mixture. At 28 and 91 days, the control mixture had the highest flexural tensile strength in all blends except SF20 at 91 days. In addition, with increasing replacement rates of FA and SF in SCMs, the sorptivity of SCMs with FA resulted in an increase, whereas a reduction in the sorptivity of SCMs with SF was observed. Finally, as replacement ratio of FA and SF increased, the relative dynamic elastic modulus of SCMs showed a reduction, and the decreases in the elastic modulus of SCMs containing SF occurred sharply compared with SCMs incorporating FA because of the deleterious effects of SF on F-T cycles. Moreover, as SF and FA content increased, the dynamic elastic modulus of SCMs decreased at 28 days.

Forecasting of ice jams and their breakup is crucial to prevent or reduce flooding risk in cold regions. A back propagation (BP) neural network model improved by the Levenberg-Marquardt clustering method has been developed with air temperatures and precipitation as inputs and applied for ice-jam forecasting in a given year in the upper reaches of the Heilongjiang River (Amur River), where ice flooding occurs frequently during spring. The accuracy rate achieved was 85%, higher than that obtained using the conventional statistical method (62% accuracy), for ice-jam breakup forecasting. The BP model has a forecast period of 10 days with a maximum error of two days and a qualified rate of 100% for national standards breakup date forecasting. The forecast on the ice-jam breakup, which was released 24 days ahead, provided accurate results for the breakup date and the occurrence of ice jams in the spring of 2017.