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.

The traffic safety of mountain highway has always been one of the taking point. This study aims to collect road design data in large-scale research and analyzes the accident risk of highway geometric alignment. Accordingly, a method based on satellite maps and clustering algorithms is proposed to calculate the geometric alignment of the highway plane and its longitudinal section. The reliability of the method was verified on Nanfu highway in Chongqing, China. The planar and longitudinal sectional geometries of the four highways in Chongqing were obtained by the above method, and the corresponding 36,439 traffic accidents which occurred from 2010 to 2016 were used as the research objects. The accident risk of the highway geometry was analyzed based on the SHAP and MLP theories. The results show that the fitting and prediction abilities of the MLP model are better than those of the negative binomial model, and its correlation coefficient is improved by 33.2%. In addition, compared with the negative binomial model, the MLP model can estimate more accurately and flexibly the complex nonlinear relationship between the independent and the dependent variables.

Coarse aggregate shape plays a critical role in determining surface performance and durability in pavement systems. Traditional manual shape inspection is laborious and subjective, especially for bulk aggregates in overlapped state. In this work, we propose an automated digital image-based evaluation method for stacked coarse aggregates, combining preprocessing (grayscale conversion, histogram equalization, Gaussian filtering), segmentation, and contour reconstruction via the Graham scan convex hull algorithm. Morphological parameters such as equivalent ellipse major/minor axes, area, and perimeter are then extracted to compute individual particle shape factors. To assess batch-level quality, shape factor standard deviations (σ) and mean shape factors were computed from 50 aggregate images. Comparison with manual measurement results shows mean relative errors below 15%. Our analysis reveals a strong correlation between σ and overall shape quality: lower σ indicates more uniform geometry, while higher σ suggests greater irregularity. Based on experimental data, we define three σ-based categories: excellent (σ ≤ 0.32), good (0.32 < σ ≤ 0.42), and poor (σ > 0.42). This σ-driven evaluation framework enables rapid, quantitative, and objective assessment of aggregate morphology in practical aggregate production and pavement quality control.

This study aims to enhance the hydration performance and mechanical strength of steel slag-based cementitious materials via the synergistic activation of Na₂SiO₃ and triethanolamine (TEA), solving the early-age hydration and low reactivity of steel slag. The mix is 32% steel slag (SS), 43% blast furnace slag (BFS), 12% desulfurized gypsum (DG), and 13% ordinary Portland cement (OPC). The full factorial design uses Na₂SiO₃ (4–6%) and TEA (0.03–0.08%) as composite activators. Mortar specimens were tested for compressive and flexural strengths at 3d, 7d, 10d, and 28d. XRD, SEM, FTIR, and TG revealed the hydration mechanism and microstructure evolution. The results show an optimal dosage of 5% Na₂SiO₃ and 0.05% TEA increasing compressive strengths at 3d and 28d by 43.10% and 22.09%, respectively, compared with the control group. This synergy improves matrix compactness, supporting the high-value utilization of steel slag and development of steel slag-based cementitious materials.

Pavement structure bearing capacity is an important evaluation parameter in pavement design, construction, maintenance management, and reconstruction, and is generally expressed by the pavement deflection value. Some of the current road bearing capacity detection equipment have high detection accuracy, but the detection speed is slow, they cannot achieve real-time continuous detection; and some detection speeds are fast, but the measurement accuracy is easily affected by the pavement roughness and vehicle vibration. Moreover, the detection result is the pavement displacement, which cannot directly reflect the comprehensive modulus of the pavement structure. In this paper, firstly, a two-stage jump mechanical model of “machine-pavement” system is established in order to develop a device that can simulate the real driving load and continuously test the bearing capacity of pavement structure, and the main factors affecting the acceleration response of vibrating drums were determined through analysis. Then, a finite element model of the “machine-pavement” system was established to overcome the difficulty in obtaining the parameters such as vibrating weight, equivalent stiffness, and equivalent damping of pavement structure in numerical solution of dynamic model. Next, the mean value A of the maximum acceleration signal of the vibrating drum and the coefficient of variation a_cv of the maximum acceleration signal were selected as evaluation indicators to analyze the change trend of the maximum acceleration of the vibrating drum with the excitation frequency and excitation force under different composite modulus of pavement structures. Finally, the relationship between the composite modulus E of the pavement structure and the maximum acceleration A of the vibrating drum was obtained by simulating the pavement structure with the composite modulus ranging from 100 MPa to 2900 MPa, and the accuracy of this relationship was verified by field tests. The research showed that the acceleration signal of the vibrating drum had a good fitting relationship with the bearing capacity of the pavement structure when the testing device with the vibrating drum mass of 100 kg, the exciting frequency of 60 Hz, and the exciting force of 650 N jumped on the pavement structure, and the error was about 20% after comparing with the results of Benkelman beam testing, which basically met the engineering requirements. Therefore, the device can be used to continuously detect the bearing capacity of pavement structures.