Intervening Factors in Pavement Roughness Assessment with Smartphones: Quantifying the Effects and Proposing Mitigation

Bisconsini, Danilo Rinaldi; Pegorini, Vinicius; Casanova, Dalcimar; Oliveira, Rafael Albuquerque de; Farias, Bruno Alessandro; Júnior, José Leomar Fernandes

doi:10.1061/jpeodx.0000303

Cited by 5 publications

(13 citation statements)

References 8 publications

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“…The current trends are to use smartphone accelerometers for IRI estimation ( 32 – 39 ). Yu et al ( 33 ) presented an overview of the current literature on relationships between the IRI and acceleration measured by smartphones.…”

Section: Estimation Of the International Roughness Index From Vehicle...mentioning

confidence: 99%

“…Most authors used unweighted acceleration in the vertical direction or total vibration that was mostly measured on the dashboard ( 32,33 ). A two-parameter linear relationship with constant and linear terms was assumed in most cases ( 34 – 39 ). The results are typically presented for single or several distinct vehicle speed values.…”

Section: Estimation Of the International Roughness Index From Vehicle...mentioning

confidence: 99%

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Relation Between Seated Person Vibrations and the International Roughness Index

Múčka

2023

Transportation Research Record: Journal of the Transportation R

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Vertical vehicle feet/floor interface vibrations are typically not used to indirectly estimate road roughness. The relation for vehicle floor vibrations, the International Roughness Index (IRI), vehicle speed, and road roughness vertical displacement spectrum according to the ISO 8608: 2016 standard was previously derived only for the simulation of a reference quarter-car model. It was estimated using the in situ measurement of vibrations of a person sitting in a passenger car for different vehicle and road categories, vehicle speed, and road roughness. Vibrations at the feet/floor interface and seat surface of a seated passenger were evaluated with the frequency-weighted root-mean-square vertical acceleration according to the ISO 2631-1: 1997 standard. The results indicated that the simulated results for the quarter-car model may have substantially underestimated the ratio between the vehicle floor vibrations of a seated person and the IRI, but they were similar to seat surface vibrations.

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Section: Estimation Of the International Roughness Index From Vehicle...mentioning

confidence: 99%

Section: Estimation Of the International Roughness Index From Vehicle...mentioning

confidence: 99%

Relation Between Seated Person Vibrations and the International Roughness Index

Múčka

2023

Transportation Research Record: Journal of the Transportation R

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“…Accelerometers with a resolution of 10 mm/s 2 introduce double the inherent noise compared to that having a resolution of 1 mm/s 2 [56]. The effects of smartphone type on pavement roughness measurement have been widely studied, with multiple studies reporting a significant impact [56,85,90,[93][94][95]. For example, Zhang et al [92] reported a relative error of up to 11%, which they considered tolerable.…”

Section: Acceleration-based Response-type Approachesmentioning

confidence: 99%

“…Variations in the vehicles' dynamic systems lead to differences in the filtering of vibration caused by road roughness [95]. Most studies reported a significant impact of vehicle model variation on the measured pavement roughness [85,89,90,[94][95][96].…”

Section: Acceleration-based Response-type Approachesmentioning

confidence: 99%

Industry- and Academic-Based Trends in Pavement Roughness Inspection Technologies over the Past Five Decades: A Critical Review

Fares

Zayed

2023

Remote Sensing

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Roughness is widely used as a primary measure of pavement condition. It is also the key indicator of the riding quality and serviceability of roads. The high demand for roughness data has bolstered the evolution of roughness measurement techniques. This study systematically investigated the various trends in pavement roughness measurement techniques within the industry and research community in the past five decades. In this study, the Scopus and TRID databases were utilized. In industry, it was revealed that laser inertial profilers prevailed over response-type methods that were popular until the 1990s. Three-dimensional triangulation is increasingly used in the automated systems developed and used by major vendors in the USA, Canada, and Australia. Among the research community, a boom of research focusing on roughness measurement has been evident in the past few years. The increasing interest in exploring new measurement methods has been fueled by crowdsourcing, the effort to develop cheaper techniques, and the growing demand for collecting roughness data by new industries. The use of crowdsourcing tools, unmanned aerial vehicles (UAVs), and synthetic aperture radar (SAR) images is expected to receive increasing attention from the research community. However, the use of 3D systems is likely to continue gaining momentum in the industry.

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“…Due to the vast advancement in these fields, the potential of using smartphones for pavement condition monitoring was increased. The potential of using the smartphone as a cost-effective pavement roughness evaluation method was examined by many researchers such as Douangphachanh and Oneyama (2014), Bisconsini et al (2018), Aleadelat et al (2018), Islam et al (2014), Hanson et al (2014), Zang et al (2018) and Sandamal and Pasindu (2020). The common conclusion from these studies was that the pavement roughness evaluation by using smartphones showed a very good accuracy compared to high accurate devices and the ability to collect large roughness data from the road network.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of pavement roughness regression models based on smartphone measurements

Al‐Suleiman

Alatoom

2022

JEDT

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Purpose The purpose of this paper was to study the possibility of using smartphone roughness measurements for developing pavement roughness regression models as a function of pavement age, traffic loading and traffic volume variables. Also, the effects of patching and pavement distresses on pavement roughness were investigated. The work focused on establishing pavement roughness prediction models and applying these models to pavement management systems (PMS) to help decision-makers choose the best maintenance and rehabilitation (M&R) options by using cost-effective methods. Design/methodology/approach Signal processing techniques including filtering and processing techniques were used to obtain the International Roughness Index (IRI) from raw acceleration data collected from smartphone accelerometer sensors. The obtained IRI values were inputted as a dependent variable in analytical regression models as well as several independent variables with proper transformations. Findings According to the study results, several regression models were developed with a big variation in the coefficients of determination (R2). However, the best models included pavement age, accumulated traffic volume (∑TV) and construction quality factor (CQF) with R2 equal to 0.63. It was also found that the effects of pavement distresses and patching was significant at a-level < 0.05. The patching effect on pavement roughness was found higher than the effect of other pavement distresses. Practical implications The presented results and methods in this paper could be used in the future predictions of pavement roughness and help the decision-makers to estimate M&R needs. The work focused on establishing IRI prediction models and applying these models to the PMS to help decision-makers choose the best M & R options. Originality/value To develop sound pavement roughness models, it is essential to collect roughness data using automated procedures. However, applying these procedures in developing countries faces several difficulties such as the high price and operation costs of roughness equipment and lack of technical experience. The advantage of using IRI values taken from smartphones is that the roughness evaluation survey may be expanded to cover the full road network at a cheaper cost than with automated instruments. Therefore, if the roughness survey covers more roads, the prediction model’s accuracy will be improved.

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Intervening Factors in Pavement Roughness Assessment with Smartphones: Quantifying the Effects and Proposing Mitigation

Cited by 5 publications

References 8 publications

Relation Between Seated Person Vibrations and the International Roughness Index

Relation Between Seated Person Vibrations and the International Roughness Index

Industry- and Academic-Based Trends in Pavement Roughness Inspection Technologies over the Past Five Decades: A Critical Review

Development of pavement roughness regression models based on smartphone measurements

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