Falling weight impact acceleration-time signals analysis for road modulus detection: Theoretical and experimental investigations
Year: 2024
Type: article
Abstract: Structural modulus detection is essential for construction and maintenance in road engineering. Falling weight impact loading method has significant strengths in large-scale, continuous, and rapid detection. To provide theoretical support for this, theoretical analysis and falling weight impact experiments were employed in this study to investigate the influence of road structure parameters and falling weight impact parameters on impact acceleration-time signals. First, the applicability of the Hertz model in road detection was analyzed, and theoretical formulas for peak acceleration amax and impact duration t2 were established, considering the collision between falling weight and half-space. Second, the properties of layered structures were studied through model box impact experiments. Third, field tests were conducted to study engineering applications. The results showed that the influence patterns were similar in both theoretical and experimental studies, with differences reflected in the time of compression and restitution phases due to the road materials’ viscosity and plasticity. Additionally, in indoor experiments, amax showed an excellent power function relationship with structural layer modulus, with R2 more than 0.95, and the contribution of each layer’s modulus to amax decreased from top to bottom. Finally, amax and resilient modulus of an old road also followed a good power function relationship, with R2 around 0.76. Therefore, amax is recommended for road surface modulus detection based on solid theoretical and experimental support.
Authors: Xinnan Xu, Mohan Zhao, Yu Liu, Chaofan Wu, Yuhao Pei, Chengmiao Zhang
Institutions: Hong Kong Polytechnic University, Chang'an University
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,110
Awards: G2456090016, G3926151158, G40448562
OpenAlex ID: W4403727618
