Traffic information is critical for pavement design, management, and health monitoring. Numerous in-pavement sensors have been developed and installed to collect the traffic volume and loading amplitude. However, limited attention has been paid to the algorithm of vehicle speed estimation. This research focuses on the estimation of the vehicle speed based on a cross-correlation method. A novel wireless micro-electromechanical sensor (MEMS), Smartrock is used to capture the triaxial acceleration, rotation, and stress data. The cross-correlation algorithms, i.e., normalized cross-correlation (NCC) algorithm, the smoothed coherence transform (SCOT) algorithm, and the phase transform (PHAT) algorithm, are applied to estimate the loading speed of an accelerated pavement test (APT) and the traffic speed in the field. The signal-noise-ratio (SNR) and the mean relative error (MRE) are utilized to evaluate the stability and accuracy of the algorithms. The results show that both the correlated noise and independent noise have significant influence in the field data. The SCOT algorithm is recommended for speed estimation with reasonable accuracy and stability because of a large SNR value and the lowest MRE value among the algorithms. The loading speed investigated in this study was within 50 km/h and further verification is needed for higher speed estimation.

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 in-situ dynamic modulus property of asphalt mixtures is critical to aiding the decision-making of pavement maintenance and rehabilitation. With the recent advancement in data science and sensing technologies, embedded sensors have been applied to collect the in-situ signal induced by traffic loading and estimate the traffic information. However, very limited studies have focused on evaluating the mechanical properties of pavement materials and structures using embedded sensors. This paper aims to present a practical approach to performing the in-situ dynamic modulus test and develop an in-situ dynamic modulus predictive model using Artificial Neural Network (ANN) based on real-time sensing data. Particle-size wireless sensors were implemented in several pavement sections to collect data under vehicular loading. An empirical mode decomposition (EMD) method was introduced to calculate the intrinsic modes of the collected data as the ANN model inputs. Laboratory dynamic modulus tests using the same material as the paving projects were also performed with embedded wireless sensors. Those laboratory data, combined with the field sensing data, were used as the training and testing dataset for developing the ANN model. The results show that the developed ANN model, when adequately trained with particle-level sensing data, is feasible and robust for predicting the in-situ dynamic modulus of asphalt pavement.