Research on a Lightweight Portable Dynamic Pavement Roughness Measurement System
Hong Zhang, Yuanshuai Dong, Yun Hou, Chang Liu, Zhenyu QianABSTRACT
Pavement roughness measurement constitutes a fundamental basis for evaluating road serviceability, ride comfort, and maintenance decision‐making. Existing vehicle‐mounted roughness measurement systems generally rely on dedicated survey vehicles, presenting limitations in mounting adaptability, installation complexity, and versatility, which hinder flexible deployment in grassroots maintenance scenarios, particularly for low‐volume and rural roads with growing maintenance demands. To address these issues, this study develops a lightweight pavement roughness measurement system suitable for ordinary vehicles, based on the combined sensing principle of laser displacement, acceleration, and rotary encoder. A portable mounting scheme is proposed, along with a data processing framework encompassing multi‐sensor synchronized acquisition, signal denoising, vibration displacement compensation, longitudinal profile reconstruction, resampling, and International Roughness Index (IRI) computation. The IRI is computed from the reconstructed longitudinal profile using the standardized quarter‐car simulation. Under the tested pavement conditions, experimental results indicate that both the 0.1 and 0.25 m sampling intervals adequately characterize the macroscopic longitudinal profile, while the finer 0.1 m interval is more favorable for capturing localized surface details; shorter computation intervals (10–20 m) exhibit higher sensitivity to localized anomalies than longer ones. Within the speed range of 30–50 km/h, the coefficient of variation of segmental IRI values remains below 5%, demonstrating satisfactory repeatability. Compared with a commercial comprehensive inspection vehicle, the CICS Road Condition Rapid Detection System (CICS), operated at the same speeds, the system yields generally consistent results, with optimal overall performance at 40 km/h, where the correlation coefficient reaches R = 0.989, and the root‐mean‐square error is 0.098 m/km.