A vibratory-based method for road damage classification

Gunawan, Fergyanto E.; Yanfi, Yanfi; Soewito, Benfano

doi:10.1109/isitia.2015.7219943

Cited by 19 publications

(16 citation statements)

References 6 publications

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“…Gunawan et al [8] performed similar experiment that utilized a smart-phone which was enriched with a 3D accelerometer sensor and geo-location sensor. The smart-phone installed in a vehicle.…”

Section: Research Methods 21 Relevant Workmentioning

confidence: 99%

“…Eriksson et al [5] and Gunawan et al [8] used vehicle acceleration data as the main source. Smart-phone which is enriched with a 3D accelerometer sensor and geo-location sensor is installed into the vehicle.…”

Section: Vibration-based Methodsmentioning

confidence: 99%

“…The previous studies had demonstrated that vibration technique has potential to detect damaged road [7,8]. However, those studies were only able to differentiate damaged roads from un-damaged one, without ability to distinguish the types of the road anomaly.…”

Section: Introductionmentioning

confidence: 99%

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Vibration-Based Damaged Road Classification Using Artificial Neural Network

Purnama

Gunawan

2018

TELKOMNIKA

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It is necessary to develop an automated method to detect damaged road because manually monitoring the road condition is not practical. Many previous studies had demonstrated that the vibration-based technique has potential to detect damages on roads. This research explores the potential use of Artificial Neural Network (ANN) for detecting road anomalies based on vehicle accelerometer data. The vehicle is equipped with a smart-phone that has a 3D accelerometer and geo-location sensors. Then, the vehicle is used to scan road network having several road anomalies, such as, potholes, speedbump, and expansion joints. An ANN model consisting of three layers is developed to classify the road anomalies. The first layer is the input layer containing six neurons. The numbers of neurons in the hidden layer is varied between one and ten neurons, and its optimal number is sought numerically. The prediction accuracy of 84.9% is obtained by using three neurons in conjunction with the maximum acceleration data in x, y, and z-axis. The accuracy increases slightly to 86.5%, 85.2%, and 85.9% when the dominant frequencies in x, y, and z-axis, respectively, are taken into account beside the previous data.

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Section: Research Methods 21 Relevant Workmentioning

confidence: 99%

Section: Vibration-based Methodsmentioning

confidence: 99%

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Vibration-Based Damaged Road Classification Using Artificial Neural Network

Purnama

Gunawan

2018

TELKOMNIKA

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“…Jamakhandi and Srinivasa [30] detected humps, pits and abrupt elevation. Bello-Salau et al [10], Gunawan and Soewito [26], Ghadge et al [23], Kumar et al [36], Li and Goldberg [39], Pooja and Hariharan [53] and Rishiwal and Khan [55] identified bumps and potholes, with [23] validating the threshold with a K-means clustering and random forest. Gawad et al [22] trained a perceptron to generate the threshold, used to identify the presence of road anomalies.…”

Section: Threshold-based Approachesmentioning

confidence: 98%

Vehicular Perception Based on Inertial Sensing: A Structured Mapping of Approaches and Methods

Menegazzo

Wangenheim

2020

SN COMPUT. SCI.

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In this paper, we present a structured literature mapping of the state-of-the-art of vehicular perception methods and approaches using inertial sensors. An in-depth investigation and classification were performed employing the results of a systematic literature review. The analysis focused on identifying methods that capture signals provided by inertial sensors such as accelerometers and gyroscopes to recognize transient or persistent events associated with the vehicle's movement. We classified these events into vehicular exteroception, associated with potholes, cracks, speed bumps, pavement type, conservation state; and vehicular proprioception, associated with lane change, braking, skidding, aquaplaning, turning right or left. Through the comprehensive study of publications in a 7-year time window, in addition to the methods, we have also identified their dependency factors, hardware platforms and applications.

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“…The abovementioned Z-THRESH is similar but simpler than Zpeak in Pothole Patrol [102], Nericell [103] and Traffic-Sense [104], which used specific algorithms to filter and to cluster the collected data. Based on Pothole Patrol, further analysis to differentiate pothole and bump-road cases in [105], or to develop the PRISM platform for remote sensing [106]. For the same purpose, a supervised learning approach based on temporal classification was undertaken in [107].…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Response-based methods to measure road surface irregularity: a state-of-the-art review

Nguyen

Lechner

Wong

2019

Eur. Transp. Res. Rev.

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Purpose: With the development of smart technologies, Internet of Things and inexpensive onboard sensors, many response-based methods to evaluate road surface conditions have emerged in the recent decade. Various techniques and systems have been developed to measure road profiles and detect road anomalies for multiple purposes such as expedient maintenance of pavements and adaptive control of vehicle dynamics to improve ride comfort and ride handling. A holistic review of studies into modern response-based techniques for road pavement applications is found to be lacking. Herein, the focus of this article is threefold: to provide an overview of the stateof-the-art response-based methods, to highlight key differences between methods and thereby to propose key focus areas for future research. Methods: Available articles regarding response-based methods to measure road surface condition were collected mainly from "Scopus" database and partially from "Google Scholar". The search period is limited to the recent 15 years. Among the 130 reviewed documents, 37% are for road profile reconstruction, 39% for pothole detection and the remaining 24% for roughness index estimation. Results: The results show that machine-learning techniques/data-driven methods have been used intensively with promising results but the disadvantages on data dependence have limited its application in some instances as compared to analytical/data processing methods. Recent algorithms to reconstruct/estimate road profiles are based mainly on passive suspension and quarter-vehicle-model, utilise fewer key parameters, being independent on speed variation and less computation for real-time/online applications. On the other hand, algorithms for pothole detection and road roughness index estimation are increasingly focusing on GPS accuracy, data aggregation and crowdsourcing platform for large-scale application. However, a novel and comprehensive system that is comparable to existing International Roughness Index and conventional Pavement Management System is still lacking.

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A vibratory-based method for road damage classification

Cited by 19 publications

References 6 publications

Vibration-Based Damaged Road Classification Using Artificial Neural Network

Vibration-Based Damaged Road Classification Using Artificial Neural Network

Vehicular Perception Based on Inertial Sensing: A Structured Mapping of Approaches and Methods

Response-based methods to measure road surface irregularity: a state-of-the-art review

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