Sensor-Based Structural Health Monitoring of Asphalt Pavements with Semi-Rigid Bases Combining Accelerated Pavement Testing and a Falling Weight Deflectometer Test

Abstract

The Structural Health Monitoring (SHM) of pavement infrastructures holds paramount significance in the assessment and prognostication of the remaining service life of roadways. In response to this imperative, a methodology for surveilling the surface and internal mechanical responses of pavements was devised through the amalgamation of Accelerated Pavement Testing (APT) and Falling Weight Deflectometer (FWD) examinations. An experimental road segment, characterized by a conventional asphalt pavement structure with semi-rigid bases, was meticulously established in Jiangsu, China. Considering nine distinct influencing factors, including loading speed, loading weight, and temperature, innovative buried and layout configurations for Resistive Sensors and Fiber-optic Bragg Grating (FBG) sensors were devised. These configurations facilitated the comprehensive assessment of stress and strain within the road structure across diverse APT conditions. The methodology encompassed the formulation of response baselines, the conversion of electrical signals to stress and strain signals, and the proposition of a signal processing approach involving partial filtering and noise reduction. In experimental findings, the asphalt bottom layer was observed to undergo alternate tensile strains under dynamic loads (the peak strain was ten με). Simultaneously, the horizontal transverse sensor exhibited compressive strains peaking at 66.5 με. The horizontal longitudinal strain within the base and subbase ranged between 3 and 5 με, with the base registering a higher strain value than the subbase. When subjected to FWD, the sensor indicated a diminishing peak pulse signal, with the most pronounced peak response occurring when the load plate was situated atop the sensor. In summary, a comprehensive suite of monitoring schemes for road structures has been formulated, delineating guidelines for the deployment of road sensors and facilitating sustained performance observation over extended durations.

Air Quality Class Prediction Using Machine Learning Methods Based on Monitoring Data and Secondary Modeling

“…Data normalization scales values from various ranges to [0, 1], eliminating the impact of data with different orders of magnitude and avoiding the problem of large values dominating small values [35,36]. Moreover, normalization can increase the solution speed and improve the convergence efficiency.…”

Analysis of Road Surface Texture for Asphalt Pavement Adhesion Assessment Using 3D Laser Technology

“…Exemplars include using the volumetric "sand patch" test, the circular track meter, or a high-speed profiler to detect the Mean Texture Depth (MTD) and Mean Profile Depth (MPD) [15][16][17]. The operation of most of these devices is manual, which is time-consuming, spot-based, and labor-intensive [18,19]. Nevertheless, these methods can measure adhesion only periodically, it hardly meets the requirements of the real-time inspection for pavement adhesion while the adhesion varies.…”