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The application of cement asphalt mortar (CAM) in modern high-speed railways has been gaining attention due to its combined merits between asphalt and cement hydration product characteristics. To promote sustainable development, it is promising to utilize by-products in the making of new CAM instead of using only cement. In this research, the cement content was partly replaced by fly ash or ground-granulated blast furnace (GGBS) slag to achieve this objective. Then, laboratory experiments were conducted to determine the effect of these admixtures on the fresh and hardened characteristics of CAM. The test results revealed that the CAM mixture with slag received better fresh properties compared to the controlled mixture. However, the poor pozzolanic property of these by-product materials may lead to the low strength development. Meanwhile, although the mixture with fly ash suffered from slow strength establishment compared to the control mix at an early age, the strength of this condition increases dramatically after 28 days. Based on the findings, the application of appropriate fly ash content in the CAM mixture will not only provide ideal workable time and mixing stability but also ensure the required strength for the design target. This combination also serves as a cost-effective and environmental solution.

Black ice is a thin coating of ice on the road surface, which strongly reduces friction at the tire-road surface, resulting in dangerous driving when it happens. An appropriate diagnostic of black ice could prevent traffic accidents as well as provide timely notice to drivers. Therefore, this study aims at developing a black ice prediction model to diagnose the probability of black ice formation. Several combinations that can form road ice have been considered, including freezing rain, hoar frost, freezing of wet roads. In addition, black ice risky index (BRI) has been computed to reflect the probability of black ice formation. To acquire a fast prediction and high accuracy, the existing Geographical Information System (GIS) database and meteorological data have been utilized. GIS database includes road geometry and location of automatic weather stations, while the meteoritical data consists of air temperature, wind speed, humidity, cloud cover. The model has been developed based on the Python programming language. A 5-km road condition was observed from 1 December to 31 December 2021 to determine the model accuracy. Based on the results from the prediction model, black ice formation has been verified when the BRI is higher than 0.8. The model may be useful to develop black ice diagnostic program.