Identifying Deficient Pavement Sections by Means of an Improved Acceleration-Based Metric

Zeng, Huanghui; Park, Hyung Jun; Fontaine, Michael D.; Smith, Brian L.; McGhee, Kevin K

doi:10.3141/2523-15

Cited by 20 publications

(6 citation statements)

References 5 publications

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“…Recentemente, com vistas à redução de custos e possibilidade de coleta de dados a partir de processos colaborativos (crowdsourcing), passaram a ser utilizados dispositivos móveis para a avaliação da irregularidade longitudinal de pavimentos, em termos de IRI (International Roughness Index). Alguns buscam o cálculo direto do índice a partir dos sinais de aceleração verticais fornecidos por esses aparelhos [4,7,8], enquanto outros analisam a correlação entre esse índice e os dados de aceleração [9,10,11,12,13,14,15,16].…”

Section: Resultsunclassified

Monitoramento de Pavimentos com Dispositivos Móveis

Bisconsini¹,

Júnior²

2018

Anais Do STPR

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Resumo: Diversas agências de transportes e universidades estão em busca de novas tecnologias para aprimorar a prática de levantamento de defeitos para a alimentação de Sistemas de Gerência de Pavimentos (SGP). Este estudo inclui a revisão, análise crítica e síntese de diversos estudos e desenvolvimentos que buscaram, a partir de diferentes métodos, avaliar a condição de pavimentos por meio de dados fornecidos por dispositivos móveis. O objetivo é apontar os principais métodos utilizados, vantagens e limitações dessas abordagens. Por meio do estudo realizado, conclui-se que os dispositivos móveis, com alguns cuidados e restrições, podem ser utilizados como ferramentas para a avaliação da condição de pavimentos em nível de rede, com potencial de ganho em confiabilidade dentro de alguns anos, em função do desenvolvimento de seus sensores e de seu uso em larga escala.Palavras-chave: Gerência de pavimentos, Smartphones, Monitoramento de pavimentos.Abstract: Several transportation agencies and universities are looking for new technologies for collecting data for Pavement Management Systems (PMS). This study includes the review, critical analysis and synthesis of several researches and developments that have sought, from different methods, to evaluate the condition of pavements through data provided by mobile devices. The objective is to indicate the main methods used with the advantages and limitations of these approaches. Through the study, it was concluded that mobile devices, with some cautions and restrictions, can be used as an assessment tool of pavement conditions at network-level, with the potential to gain reliability within some years, due to the development of its sensors and its large-scale use.

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Section: Resultsunclassified

Monitoramento de Pavimentos com Dispositivos Móveis

Bisconsini¹,

Júnior²

2018

Anais Do STPR

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“…A number of studies have demonstrated that the measured vertical acceleration is related to vehicle speed (Douangphachanh and Oneyama 2014;Zeng 2014;Du et al 2014). To better understand the influence of vehicle speed on simulated Grms, one type of vehicle was modeled [i.e., a D-class sedan (DSD)] travelling at speeds ranging from 30 to 100 km=h on four roads of very good (IRI ¼ 1) to poor road condition (IRI ¼ 7.3) (Table 4).…”

Section: Effect Of Vehicle Speedmentioning

confidence: 99%

Study of the Factors Affecting Road Roughness Measurement Using Smartphones

Wang

Burrow

Ghataora

2020

J. Infrastruct. Syst.

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The measurement of road roughness is important for the management of economic road maintenance. Not only is it an indicator of road condition and ride quality, but it also is used to determine road use costs, including travel time, fuel consumption, and vehicle maintenance. Because of the importance of roughness for road asset management decision-making, road agencies spend considerable resources trying to measure road roughness in a repeatable and reproducible manner. However, many road agencies with large road networks are unable to record the condition of the entire network on a sufficiently frequent basis to determine adequately road condition to make informed preventative maintenance decisions. To address this, research has been carried out to develop low cost smartphone based technologies fitted inside vehicles to measure road condition. The trial of these systems has met with varying degrees of success. This paper presents an in-depth parametric study carried out using state-of-the-art vehicle dynamics software, informed by a review of the literature, to appreciate how and to what degree various influencing variables might affect roughness measurements using a smartphone fitted to a moving vehicle. These variables included the type and position of the smartphone; the type, speed, mass, dynamic response, suspension system, and tire pressure of the vehicle in which the smartphone is fitted; and the longitudinal road profile. The results of the parametric analysis were used to build multivariate linear regression and machine learning algorithms which predict road roughness from a measure of a vehicle's vertical acceleration taking into account the predominant influencing variables. The multivariate linear regression equations can be used to predict road roughness with a similar degree of accuracy that is expected from a visual inspection. On the other hand, the machine learning algorithms, when suitably trained, were able to estimate reliably the road roughness on an integer-based rating scale at a level of detail which is suitable for strategic road asset management, provided that the vehicle type and speed and the type of smartphone are taken into account.

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“…The results showed that smartphones measured road roughness with an acceptable accuracy when compared with an inertial profiler [8]. Zeng et al (2015) determined that road roughness depends on a normalized acceleration index. The data were collected by mounting two tablets on a vehicle dashboard.…”

Section: Introductionmentioning

confidence: 98%

“…Data were captured on the acceleration in three dimensions, the GPS coordinates, and the speed. This revealed that the recommended index could define road surface deficiencies with a high precision (between 80 and 93 percent) [9]. Hanson et al (2014) made an effort to correlate road roughness between a smartphone and a conventional profiler.…”

Section: Introductionmentioning

confidence: 99%

An Application of Android Sensors and Google Earth in Pavement Maintenance Management Systems for Developing Countries

Al‐Mansour

Al-Qaili

2022

Applied Sciences

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Governments and road agencies face the challenging task of maintaining roads. One of the reasons this is challenging is that the maintenance process requires utilizing a substantial amount of road network condition data. There are many approaches for measuring road surface conditions which are very costly and time-consuming, as well as requiring skillful operators. Developing countries have limited budgets for planning and monitoring road maintenance. This research aims to establish a low-cost pavement maintenance management system for intermediate and small cities in developing countries. The system utilizes low-cost sensors embedded in smartphones that can be used to measure road surface conditions. Google Earth is then used to present maintenance data, select ‏a maintenance ‏strategy, and view the maintenance output information. Road Lab Pro, an android application, is used to collect the data and estimate the surface condition of roads by using accelerometers, gyroscopes‏, and a GPS. The road network is divided ‏into segments and the road surface conditions are estimated for each segment using the smartphone application and a suspension vehicle. The required maintenance activities for these segments are then established. A priority index is determined for each segment to decide which segments should be maintained with the available budget. This effort allows us to investigate the feasibility of assessing road surface roughness using a smartphone to determine the presence of road distresses and the overall road condition, which is taken into account when making maintenance decisions. The application of this system reveals that these new technologies can provide cost-effective, easy-handling, and efficient ways for a road agency to perform good maintenance planning.

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Identifying Deficient Pavement Sections by Means of an Improved Acceleration-Based Metric

Cited by 20 publications

References 5 publications

Monitoramento de Pavimentos com Dispositivos Móveis

Monitoramento de Pavimentos com Dispositivos Móveis

Study of the Factors Affecting Road Roughness Measurement Using Smartphones

An Application of Android Sensors and Google Earth in Pavement Maintenance Management Systems for Developing Countries

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