Application of Vehicle-Based Sensing Technology in Monitoring Vibration Response of Pavement Conditions

Ho, Chun Hsing; Snyder, Matthew; Zhang, Dada

doi:10.1061/jpeodx.0000205

Cited by 22 publications

(17 citation statements)

References 24 publications

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“…Recent studies have suggested that using vertical accelerations from a single device or sensor to assess pavement conditions is not sufficient to facilitate pavement distress classification [7,8,23,24]. Thus, the use of multiple devices or sensors in vibration data collections have been used in several studies.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Additionally, the bagged trees classification and robust regression analysis were applied in pavement monitoring through three-dimensional rotations and the results of detection exhibited accuracy of more than 90% [25]. Ho et al [7] and Zhang et al [8] used magnitude values, which was combining all three accelerations in the three directions to monitor pavement conditions and their results were effectively validated with IRI segments.…”

Section: Literature Reviewmentioning

confidence: 99%

“…x, y, and z), GPS coordinates, time, driving speed, and magnitude values from all five sensors. The details about the data collection process are explained in references [7,8]. In the pavement condition assessment, the driving speed was maintained from 50 to 55 miles per hour except for the traffic congestion that occurred.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…Referring to the authors' previous work [7,8], the threshold values that classify the pavement condition were obtained from distribution fitting and percentile analysis. Figure 4 illustrates the process of obtaining threshold values from eastbound lane 1 in road Sect.…”

Section: The Effect Of Thresholds In Pavement Condition Assessmentmentioning

confidence: 99%

“…Ho et al [7] and Zhang et al [8] conducted a year-long pavement condition survey using vehicle based pavement sensing techniques on the I-10 corridors in the Phoenix region. Their findings are strongly in support of vehicle mounted sensors for use in pavement condition assessment.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the impact of factors in vehicle based pavement sensing implementation: sensor placement, pavement temperature, speed, and threshold

Zhang

2023

J Infrastruct Preserv Resil

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The purpose of the paper is to improve the efficiency of vehicle based sensing technology in highway pavement condition assessment by evaluating the effect of four factors (sensor placement, pavement temperature, drive speed, and threshold for pavement distress classification) and providing suggestions to better improve the accuracy of pavement condition detection and minimize the interruption of pavement sensing operation. Two I-10 corridors in the Phoenix region were selected for vibration data collection and data analysis. A series of statistical analyses were performed to determine if each one of the factors has a significant impact on the pavement distress detection. The results of Analysis of Variance (ANOVA) tests and Analysis of Covariance (ANCOVA) tests show that the placement of sensors have a significant effect in the pavement condition assessments. The significant differences occurred in the group of sensors that were placed on the same side of the vehicle, as well as, in either front wheels or rear wheels of the vehicle. The effect of pavement temperature on the vehicle based sensing implementation is significant while the mean drive speed is not seen as a significant factor in the pavement condition survey. The two thresholds were determined to select points of interest (POI; cracks, potholes) for the pavement distress classification and these POIs are in good agreement with international roughness index (IRI) data in an ArcGIS map. The findings of the paper can be used to better improve the computing algorithms of vehicle based sensing techniques.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Data Acquisitionmentioning

confidence: 99%

Section: The Effect Of Thresholds In Pavement Condition Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating the impact of factors in vehicle based pavement sensing implementation: sensor placement, pavement temperature, speed, and threshold

Zhang

2023

J Infrastruct Preserv Resil

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Road safety analysis framework based on vehicle vibrations and sounds using deep learning techniques

Pakkala,

Akhila Thejaswi,

Rai

et al. 2023

Int J Syst Assur Eng Manag

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Road accidents in India occur due to potholes. These potholes are not repaired because the authorities will not be aware of it unless the public raises an issue. Lack of adequate techniques to identify potholes has caused huge trouble to the public. The primary goal of this study is to build a deep learning model that would analyze the patterns in the sound recording of the vehicles and label the Road Anomaly Events (RAEs). Deep learning techniques like Convolutional Neural Network (CNN) and BLSTM are used to classify the sound signature and then are labelled accordingly. The idea can be implemented in areas where there is regular movement of vehicles to identify the exact locations of the pothole and inform the concerned authorities so that the public can experience smoother roads. From the analysis, it is found that the model has an accuracy of 83% with ADAM Optimizer while RMSProp produces 54-60% accuracy.

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Road Roughness Detection Based on Discrete Kalman Filter Model with Driving Vibration Data Input

Wang

Miao

et al. 2023

Int. J. Pavement Res. Technol.

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Application of Vehicle-Based Sensing Technology in Monitoring Vibration Response of Pavement Conditions

Cited by 22 publications

References 24 publications

Evaluating the impact of factors in vehicle based pavement sensing implementation: sensor placement, pavement temperature, speed, and threshold

Evaluating the impact of factors in vehicle based pavement sensing implementation: sensor placement, pavement temperature, speed, and threshold

Road safety analysis framework based on vehicle vibrations and sounds using deep learning techniques

Road Roughness Detection Based on Discrete Kalman Filter Model with Driving Vibration Data Input

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