https://orcid.org/0000-0002-0066-1306
![Sample our Engineering & Technology journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5933/TOC-Subject-Sample-2021-Engineering-Tech.jpg"})
ABSTRACT
Roughness is an important indicator of road deterioration and has a significant impact on road serviceability. Conventional instruments for roughness measurement, such as laser profilers, are expensive and require a complex set-up, which limits the surveying frequency and coverage. As an alternative, embedded sensors in smartphones mounted in vehicles have been leveraged to measure roughness indirectly, and multiple smartphone-based roughness index estimation (sRIE) systems have become available recently. However, there lacks a framework to evaluate the performance of sRIE systems in a systematic and repeatable manner. This research proposed an evaluation framework to assess the performance of sRIE systems in practical settings. The framework consists of statistical measures that evaluate the consistency and accuracy of sRIE systems under various mountings, vehicle types, and survey speeds. Three popular sRIE systems were assessed using the framework to validate their validity and practicality. By standardising the performance metrics, this framework allows for performance benchmarking between sRIE systems and conventional instruments.
Acknowledgement
This research work is part of a research project (Project No. 2.7) sponsored by the SPARC Hub (https://sparchub.org.au) at the Department of Civil Engineering, Monash University funded by the Australian Research Council (ARC) Industrial Transformation Research Hub (ITRH) Scheme (Grant number: IH180100010). The financial and in-kind support from ARRB and Monash University is gratefully acknowledged. Also, the financial support from ARC is highly acknowledged.
Disclosure statement
No potential conflict of interest was reported by the author(s).