This paper evaluates the induction healing performance of granite and steel slag asphalt mixtures modified with Steel Wool Fibers (SWF). A computerized tomography scan (CT-scan) was conducted to analyze microstructure and distribution of steel wool fibers in the mixture. Thermal conductivity was examined and heating rate was investigated with induction heating system. The healing performance was investigated by crack-heal cycles with three-point bending test. Dynamic modulus and repeated indirect tensile test were carried out to characterize the mechanical properties of asphalt mixtures modified with SWF. The results showed that the conductive properties and induction heating rate of asphalt mixture was enhanced by adding steel wool fibers and steel slag. The microanalysis results showed the agglomeration and uneven distribution of fibers in the mixtures when the mixed conductive additives exceed a certain dosage. It was found that heating rate of steel slag mixture was higher than that of granite mixture; however, its healing performance was slightly lower because of the cracking of weak steel slag. Finally, the mechanical test results demonstrated that the rutting resistance of asphalt mixtures was enhanced by adding steel wool fibers.

This paper investigates the effects of air-void and binder content on the cracking mechanism of asphalt concrete by using induction heating. Semi-circular bending test was used to evaluate the cracking resistance of induction asphalt mixture, including fracture energy index, crack velocity, crack resistance index, and flexibility index. Compaction energy index was computed to evaluate the influence of the additive agents on the workability of asphalt mixture. Even though the healing level of asphalt mixture recovered up to 80%, cracking resistance declined greatly after one damage-healing treatment. It was observed that air void plays a major role in both healing capacity and cracking resistance, while the impact of asphalt content is milder. Furthermore, analysis results from Matlab® image processing demonstrated that the crack shape of samples was retained after induction healing, the crack area expanded approximately 18% compared to the virgin sample. The analysis also indicates that aggregate crack failure not only causes low healing level but also deteriorates cracking resistance of induction healed sample.

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.

In developing countries, nitrogen in the traditional market wastewater is a critical environmental problem. In this study, the microalga Chlorella sp., which was isolated from wastewater, was used to remove the total nitrogen (TN) from conventional market wastewater in combination with audible sound (Vietnamese classical music). In addition, effects of sound exposure on removal efficiency at different initial cell densities were analyzed. Results revealed that music sound control demonstrates potential to improve the removal efficiency. TN removal efficiencies of 96%, 69.5%, and 4.3% were observed for treatments with Chlorella sp./audible sound, Chlorella sp., and without Chlorella sp., respectively. The significance of probability value (p-value) (<0.05) on the paired sample t-test confirmed the critical role of audible sound and Chlorella sp. density on the TN removal in screening experiments. The predicted optimal conditions for TN removal were as follows: a Chlorella sp. density of 4%, an audible sound of 52.5 dB, and a cultivation time of 4.6 days. Results based on statistical analysis revealed that the quadratic models for TN removal are significant at a low p-value (<0.05) and a high predicted coefficient of determination (R2 = 0.9452) value. The obtained statistical results also indicated that most of the variables are significant for the abatement of TN from market wastewater using Chlorella sp.

This study investigates two methods for detecting black ice, a hazardous thin layer of ice on road surfaces. The rain sensor and deep learning methods were explored, while considering the influence of environmental conditions, such as temperature, humidity, and air voids in asphalt concrete, on black ice formation. Results from the rain sensor method showed that the electrical resistance involved cooling time and reached a constant value when black ice was formed. Lower air voids in asphalt concrete led to a higher chance of black ice formation, while humidity had a negligible effect. A vehicle module for detecting black ice was successfully developed using deep learning with 90% accuracy. Furthermore, experiments using infrared heating confirmed that environmental temperature affects the melting time and melting area of black ice. These findings can improve safety for drivers and help with route planning when travelling on black ice roads.