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Road surface monitoring and maintenance are essential for driving comfort, transport safety and preserving infrastructure integrity. Traditional road condition monitoring is regularly conducted by specially designed instrumented vehicles, which requires time and money and is only able to cover a limited proportion of the road network. In light of the ubiquitous use of smartphones, this paper proposes an automatic pothole detection system utilizing the built-in vibration sensors and global positioning system receivers in smartphones. We collected road condition data in a city using dedicated vehicles and smartphones with a purpose-built mobile application designed for this study. A series of processing methods were applied to the collected data, and features from different frequency domains were extracted, along with various machine-learning classifiers. The results indicated that features from the time and frequency domains outperformed other features for identifying potholes. Among the classifiers tested, the Random Forest method exhibited the best classification performance for potholes, with a precision of 88.5% and recall of 75%. Finally, we validated the proposed method using datasets generated from different road types and examined its universality and robustness.

The research presented in this paper focuses on issues associated with the development of an experimental technique to estimate the dynamic characteristics of wheeled vehicles (namely, the frequency response function) using only in‐service response data. To validate the approach and eliminate complexities associated with multi‐wheel vehicles, a single‐wheeled prototype vehicle was designed and commissioned. The vertical vibration acceleration of the prototype vehicle's sprung mass was measured during normal operation. The power spectral density function was computed and used to estimate the frequency response function of the vehicle. A number of experiments using various configurations of the single‐wheeled prototype vehicle were undertaken, along with a series of vibration table experiments to provide a comparison with the estimated frequency response functions. The results show that the best estimate of the frequency response function using the vehicle response data provides reasonable agreement with the measured laboratory experiments when the value of the slope of the spectral function is not set to the value suggested by the International Organisation for Standardisation. Another technique was further developed to estimate the value of the pavement spectral slope using only in‐service response data; however, this technique does not yield consistent and accurate estimates. Interestingly, the main resonance of the vehicle is in agreement between the vibration table and response data around the sprung mass of all three vehicle configurations when inspected using linear scales (regardless of the variation in the spectral shape of the excitation), although the additional modes (including the unsprung mass) differs for all vehicles. Copyright © 2014 John Wiley & Sons, Ltd.

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