Pavement asset management systems and technologies: A review

Peraka, Naga Siva Pavani; Biligiri, Krishna Prapoorna

doi:10.1016/j.autcon.2020.103336

Cited by 118 publications

(46 citation statements)

References 120 publications

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“…In turn, the quality of the data depends on the considered method of collection and data analysis, which means that, for instance, a data collection method based on subjective criteria may compromise decision making. Thus, the type of adopted data collection and analysis methods not only influences the quality of the data, but can also improve the time and cost of the process of data collection and treatment [3,4].…”

Section: The Role Of Smart Technologies In Current Pavement Management Systemsmentioning

confidence: 99%

“…Image processing is also increasingly adopted to detect various types of surface distresses. The work by Peraka and Biligiri [4] evidenced the significance of the link between both data collection and data analysis methods as decision support tools dedicated to pavement management. However, their analysis of the current stateof-the-art focuses solely on the monitoring of the pavement's functional condition (i.e., detection and prediction of surface distresses or estimation of roughness indicators), while disregarding the potential of an in-depth analysis of a broader DSS concept capable of taking into consideration the developed tools for both functional and structural monitoring of pavements.…”

Section: The Role Of Smart Technologies In Current Pavement Management Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Integration of Smart Pavement Data with Decision Support Systems: A Systematic Review

Amândio¹,

Parente²,

Neves

et al. 2021

Buildings

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Nowadays, pavement management systems (PMS) are mainly based on monitoring processes that have been established for a long time, and strongly depend on acquired experience. However, with the emergence of smart technologies, such as internet of things and artificial intelligence, PMS could be improved by applying these new smart technologies to their decision support systems, not just by updating their data collection methodologies, but also their data analysis tools. The application of these smart technologies to the field of pavement monitoring and condition evaluation will undoubtedly contribute to more efficient, less costly, safer, and environmentally friendly methodologies. Thus, the main drive of the present work is to provide insight for the development of future decision support systems for smart pavement management by conducting a systematic literature review of the developed works that apply smart technologies to this field. The conclusions drawn from the analysis allowed for the identification of a series of future direction recommendations for researchers. In fact, future PMS should tend to be capable of collecting and analyzing data at different levels, both externally at the surface or inside the pavement, as well as to detect and predict all types of functional and structural flaws and defects.

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Section: The Role Of Smart Technologies In Current Pavement Management Systemsmentioning

confidence: 99%

Section: The Role Of Smart Technologies In Current Pavement Management Systemsmentioning

confidence: 99%

Integration of Smart Pavement Data with Decision Support Systems: A Systematic Review

Amândio¹,

Parente²,

Neves

et al. 2021

Buildings

View full text Add to dashboard Cite

show abstract

“…Similarly, a review by (Cao et al, 2020), reviews an image processing method, machine learning method, and 3D imaging method. A vibration-based method is a technique used in an accelerometer on vehicle platform-based for data collection (Peraka & Biligiri, 2020). This is an advantage where it only required small storage, cost-effective, and amenable for automatic real-time data processing.…”

Section: Introductionmentioning

confidence: 99%

Low altitude multispectral mapping for road defect detection

Mukti¹,

Tahar

2021

GEOGRAFIA

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Pothole's defect is major damages indicated the road condition visually, and the structural defects due to some potential causes. Nowadays, new forms of remote sensing technique were widely used, but less studies in the application of low altitude multispectral mapping. The potential of multispectral images is its help better in resolution due to its spectral characteristic. Hence, it helps a lot in feature classification with proper training sample, classifier used, and spectral band composite. Thus, this study aims to extract the defective roads by using the multispectral image of Parrot Sequoia with low flight altitude. This study tries to detect a pothole's existence from band combination and supervised classification other than its common use which ultimately for agriculture purposes. The classifier used in this is Maximum Likelihood, Support vector machine (SVM) and Mahalanobis Distance. 15 different probability of band stacks of green, NIR, red edge, and red band were used as multispectral images. The comparison of the performance between the types of classifier and band combination was modeled and discussed in this study. Classifier algorithm maximum likelihood gives the lowest error of 0.108m² with a combination of NIR + red edge band. SVM gives the lowest error of 0.427m² with a combination of green + NIR + red edge + red band. While Mahalanobis distance gives the lowest error of -0.082m² with a combination of red edge + red band. Averagely, Mahalanobis distance gives the lowest error of 0.299m² of all bands used.

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“…Therefore, the presence of adequate pavement texture is actively monitored on highway networks by road agencies [6]. Unevenness, megatexture, and macrotexture are routinely monitored at traffic speed, using laser profile sensor techniques and cameras mounted on specialist vehicles [7]. Conversely, the frictional response to microtexture and macrotexture, whilst also surveyed at traffic speed using specialist vehicles, adopts techniques that make contact with a pavement in order to measure skid resistance.…”

Section: Introductionmentioning

confidence: 99%

Seasonal Signals Observed in Non-Contact Long-Term Road Texture Measurements

et al. 2021

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Texture is required on road pavements for safe vehicle braking and manoeuvres. This paper provides a unique analysis of long-term texture obtained using traffic speed condition survey (TRACS) data from 14 sites, located along a north to south transect spanning the longest highway in the UK. A total of 19 years of sensor measured texture depth (SMTD) data have been analyzed using spatial filtering techniques and compared with meteorological and traffic datasets. The results for hot rolled asphalt (HRA) surfaces reveal that changes to SMTD follow a linearly increasing trend with time. The “rate of change” is influenced by the order of magnitude of annual average daily traffic (AADT), when factored for the percentage of heavy goods vehicles. This linear trend is disrupted by environmental parameters, such as rainfall events and seasonal conditioning. In the summer, this signal is evident as a transient peak in the “rate of change” of texture greater than 0.04 mm, and in the winter as a reduction. The transient changes in texture corresponded to above average rainfall occurring in the week prior to SMTD measurement. The signal observed demonstrates an inverse pattern to the classically understood seasonal variation of skid resistance in the UK, where values are low in the summer and high in the winter. The findings demonstrate for the first time that texture measurements experience a seasonal signal, and provide compelling evidence pointing toward surface processes (such as polishing and the wetting and drying of surface contaminants) causing changes to texture that are affecting seasonal variation in skid resistance.

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Pavement asset management systems and technologies: A review

Cited by 118 publications

References 120 publications

Integration of Smart Pavement Data with Decision Support Systems: A Systematic Review

Integration of Smart Pavement Data with Decision Support Systems: A Systematic Review

Low altitude multispectral mapping for road defect detection

Seasonal Signals Observed in Non-Contact Long-Term Road Texture Measurements

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