Steel slag is increasingly applied during road construction and maintenance processes due to its superior surface and mechanical properties such as hardness, rough surface, and wear resistance. In this paper, the effects of steel slag on mechanical performances in varied skeleton structures of asphalt mixtures are evaluated. The Marshall mix design method is modified, and a concept of nominal asphalt-aggregate ratio is proposed to match the asphalt mix design that includes steel slag. The aforementioned methods are then applied to the asphalt concrete (AC) and stone matrix asphalt (SMA) gradations. The performance tests are carried out to further evaluate the influence of the modified method on the mechanical properties for both AC and SMA gradations. Results show that the performance indexes of the steel slag mixture can satisfy specification requirements by using the modified Marshall mix design method; then, its effectiveness is verified. In contrast to the mixtures without using steel slag, the asphalt mixtures with steel slag show better rutting resistance of SMA gradation due to the characteristics of strong internal friction resistance and higher strength; the low-temperature crack resistance can also be improved, especially for the SMA gradation. The steel slag mixture does not show strong volume expansion and has good water stability. The skid resistance performance of the steel slag mixture is improved at a small level for both skeleton structures. The overall results indicate that the performance improvement effects of steel slag for asphalt mixture are obvious, and the modified Marshall design method can be used to guide the mix design for the application of steel slag in the asphalt mixture, which can significantly reduce the engineering cost and environmental effect for producing the asphalt mixture, promoting the sustainability of asphalt pavement.

Remote and real-time displacement measurements are crucial for a successful bridge health monitoring program. Researchers have attempted to monitor the deformation of bridges using remote sensing techniques such as an accelerometer when a static reference frame is not available. However, errors accumulate throughout the double-integration process, significantly reducing the reliability and accuracy of the displacement measurements. To obtain accurate reference-free bridge displacement measurements, this paper aims to develop a real-time computing algorithm based on hybrid sensor data fusion and implement the algorithm via smart sensing technology. By combining the accelerometer and strain gauge measurements in real time, the proposed algorithm can overcome the limitations of the existing methods (such as integration errors, sensor drifts, and environmental disturbances) and provide real-time pseud-static and dynamic displacement measurements of bridges under loads. A wireless sensor, SmartRock, containing multiple sensing units (i.e., triaxial accelerometer and strain gauges) and a Micro Controlling Unit (MCU) were utilized for remote data acquisition and signal processing. A remote sensing system (with SmartRocks, an antenna, an industrial computer, a Wi-Fi hotspot, etc.) was deployed, and a laboratory truss bridge experiment was conducted to demonstrate the implementation of the algorithm. The results show that the proposed algorithm can estimate a bridge displacement with sufficient accuracy, and the remote system is capable of the real-time monitoring of bridge deformations compared to using only one type of sensor. This research represents a significant advancement in the field of bridge displacement monitoring, offering a reliable and reference-free approach for remote and real-time measurements.