ORCID Profile
0000-0002-7372-5163
Current Organisations
Imperial College London
,
University of Cambridge
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Publisher: MDPI AG
Date: 28-09-2020
DOI: 10.3390/S20195564
Abstract: 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.
Publisher: Elsevier BV
Date: 03-2018
DOI: 10.1016/J.SCITOTENV.2017.10.291
Abstract: Man-made sources of ground vibration must be carefully monitored in urban areas in order to ensure that structural damage and discomfort to residents is prevented or minimised. The research presented in this paper provides a comparative evaluation of various methods used to analyse a series of tri-axial ground vibration measurements generated by rail, road, and explosive blasting. The first part of the study is focused on comparing various techniques to estimate the dominant frequency, including time-frequency analysis. The comparative evaluation of the various methods to estimate the dominant frequency revealed that, depending on the method used, there can be significant variation in the estimates obtained. A new and improved analysis approach using the continuous wavelet transform was also presented, using the time-frequency distribution to estimate the localised dominant frequency and peak particle velocity. The technique can be used to accurately identify the level and frequency content of a ground vibration signal as it varies with time, and identify the number of times the threshold limits of damage are exceeded.
Publisher: Elsevier BV
Date: 09-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 16-10-2023
Publisher: Informa UK Limited
Date: 17-03-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 16-10-2023
Publisher: Elsevier BV
Date: 03-2023
Publisher: Wiley
Date: 26-02-2021
DOI: 10.1002/PTS.2563
Publisher: Wiley
Date: 12-09-2014
DOI: 10.1002/PTS.2101
Publisher: CRC Press
Date: 18-11-2011
DOI: 10.1201/B10571-138
Publisher: Wiley
Date: 20-02-2015
DOI: 10.1002/PTS.2124
Publisher: CRC Press
Date: 18-11-2011
DOI: 10.1201/B10571-139
Publisher: Wiley
Date: 06-02-2017
DOI: 10.1002/PTS.2287
Publisher: Springer Science and Business Media LLC
Date: 26-09-2017
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Daniel Ainalis.