Smart Pavement Data with Decision Support Systems

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1		Manuel Parente	+ 1493 word(s)	1493	2021-11-30 09:15:46	\|
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This entry is adapted from the peer-reviewed paper 10.3390/buildings11120579

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.

smart pavement decision support systems pavement management systems smartphone unmanned aerial vehicle (UAV) self-powered sensors image processing artificial intelligence

1. Introduction

The maintenance and rehabilitation of in-service pavements are pivotal activities of transportation engineering. The importance of maintaining functioning, comfortable and safe transportation networks reside in the fact that the economic and social growth of a country depends heavily on the health of its transportation network. Maintenance and rehabilitation (M&R) activities are usually planned based on the quality of the pavements, both at functional and structural levels.

Many countries and infrastructure agencies have their own pavement management system (PMS) to support decisions on when to intervene to maintain or rehabilitate the pavement of a segment of the infrastructure network. However, the state of the pavement quality of a network on which this PMS is based is still monitored by processes that have been established for a long time, and strongly depend on acquired experience.

The application of new smart technologies, such as internet of things (IoT), sensorization, and artificial intelligence (AI), has the potential to transform transportation engineering into a smarter and more efficient field, while simultaneously contributing to more sustainable development, meeting the goals established by the United Nations ^[1]^[2]. The 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 and processes that will bring significant societal and economic benefits.

Smart pavement is a concept that is increasingly referenced in academic publications, though its definition is slightly sparse among researchers. Even though smart pavement is a broad concept, an advantage of these kinds of new, digital era pavements is their capacity to monitor their structural and functional health in real time by means of sensorization.

Even though in practice, infrastructure agencies are still dependent on conventional methods that are either costly or based on lengthy processes, the literature reveals that many researchers are exploring new approaches to detect and predict the state of a pavement by means of low-cost and easy-to-implement technologies. Thus, the aim of this paper is to carry out a comprehensive review of the approaches and methodologies which promote the development of smart pavement systems, with the capability of generating and analyzing data so as to provide the foundation for effective decision support systems (DSSs).

2. The Role of Smart Technologies in Current Pavement Management Systems

PMSs can be considered decision support systems that aid infrastructure agencies in the planning of M&R actions to maintain a functional and safe network. The most important output of a PMS is the generation of an M&R action plan, which is achieved by applying decision support tools that usually take into consideration the minimization of costs of M&R actions to maintain an acceptable network condition. These decision support tools receive as input the present quality of the pavement and the financial resources available and output the M&R plan. However, the effectiveness and efficiency of these decisions made by the PMS highly depend on the quality of the input data that the decisions are made upon. 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].

Currently, the PMS in use by infrastructure agencies still depends on empirical methods of pavement condition evaluation and manual techniques of data collection. These agencies are held down by data collection methods that are either subjected to an engineer’s experience-based evaluation or slow-moving automated processes that are only scheduled sporadically, due to the inconveniences and high cost normally associated with these methods. The most used methods of data collection, namely for surface distress evaluation, are visual inspections, either on foot (walking surveys) or by driving along the shoulder at lower speeds (riding surveys also known as windshield inspection). The ensuing data are translated into reports based on subjective evaluations, photos or videos and manual measurements taken during these inspections. For structural and roughness evaluations, the agencies usually own dedicated vehicles equipped with special sensors, such as inertial profilers, deflectometers, ground-penetrating radar, infrared thermal imaging, and laser technology. Although automatic, these methods take a significant amount of time, depend on costly equipment, and sometimes disturb the normal traffic flow. In general, issues arise not because of the lower efficiency of these methods to monitor the pavement condition, but because of the long periods between evaluations, reducing the efficiency of the whole monitoring system and potentially compromising the decision-making process. Thus, ample room for improvement exists in what concerns both data collection and analysis methods that comprise DSS for pavement management.

In the new industrial revolution—Industry 4.0—marked by the emergence of new technologies capable of automating and optimizing processes, the “smart” concept has gained increasing attention. In both industry and academic worlds, the concepts of smart city and smart home are examples of well-established and advertised terms. However, the concept of smart road or smart pavement is not as well defined or disseminated. Some authors considered that smart road includes four main features ^[5]^[6]. According to Pompigna and Mauro ^[6], smart roads should be capable of the following: learn with and assess itself (by monitoring its conditions in real time); adapt to the circumstances of the road (for instance, cracking or icing); interact with other intelligent devices (such as vehicles) and connect to databases of maintenance systems through a communication system; and feed themselves by supplying green energy. Throughout this paper, the term smart pavement was adopted to drive the focus to smart technologies applied to the pavement in view of automating its monitoring, as opposed to other smart road components and features, such as those related to energy and communication for instance ^[5].

Ultimately, the decisions made by these systems are not just affected by the chosen methods of data collection, but by the data analysis methods as well. Many researchers have focused on the analysis of smart data collection methods applied to pavement monitoring, such as unmanned aerial vehicles (UAV) ^[7], probe vehicles (basically vehicles equipped with sensors) ^[8], smartphones ^[9], and embedded sensors in the pavement ^[10]^[11]^[12]. Many other state-of-the-art works that explore data analysis methods as well have mainly focused on just one type of functional distress (such as cracking or surface irregularity measurement) ^[7]^[8]^[13] or in structural evaluation ^[11]. Yet not many surveys make the bridge between smart data collection methods and data analysis methods also based on new intelligent techniques, which are suited to be a part of DSS for smart pavements.

With the possibility of gathering a significant amount of varied data (including real-time data) it becomes advantageous to use AI techniques, such as machine learning (ML), to extract knowledge from this information, allowing for the detection and prediction of pavement conditions. 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 state-of-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.

Therefore, future DSSs for pavement management would benefit from integrating smart data collection and data analysis techniques for both functional and structural evaluations. This integrated approach could result in a decrease in both cost and resource usage associated with the maintenance and monitoring plans of infrastructure agencies without significant reductions in efficiency when compared with current systems and techniques in use. Figure 1 depicts a DSS as such, including the currently available smart data collection methods and the levels of analysis that were studied for both functional and structural pavement characteristics.

Figure 1. Decision support system for smart pavements.

References

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Picado-Santos, L.; Ferreira, A.; Antunes, A.; Carvalheira, C.; Santos, B.; Bicho, M.; Quadrado, I.; Silvestre, S. Pavement management system for Lisbon. Proc. Inst. Civ. Eng. Munic. Eng. 2004, 157, 157–165.
Peraka, N.S.P.; Biligiri, K.P. Pavement asset management systems and technologies: A review. Autom. Constr. 2020, 119, 103336.
Zhao, H.; Wu, D. Definition, Function, and Framework Construction of a Smart Road. In Proceedings of the 2015 International Symposium on Frontiers of Road and Airport Engineering, Shanghai, China, 26–28 October 2015.
Pompigna, A.; Mauro, R. Smart roads: A state of the art of highways innovations in the Smart Age. Eng. Sci. Technol. Int. J. 2021, in press.
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Nguyen, T.; Lechner, B.; Wong, Y.D. Response-based methods to measure road surface irregularity: A state-of-the-art review. Eur. Transp. Res. Rev. 2019, 11, 43.
Alavi, A.H.; Buttlar, W.G. An overview of smartphone technology for citizen-centered, real-time and scalable civil infrastructure monitoring. Futur. Gener. Comput. Syst. 2019, 93, 651–672.
Shtayat, A.; Moridpour, S.; Best, B.; Shroff, A.; Raol, D. A review of monitoring systems of pavement condition in paved and unpaved roads. J. Traffic Transp. Eng. 2020, 7, 629–638.
Graziano, A.D.; Marchetta, V.; Cafiso, S. Structural health monitoring of asphalt pavements using smart sensor networks: A comprehensive review. J. Traffic Transp. Eng. 2020, 7, 639–651.
Schnebele, E.; Tanyu, B.F.; Cervone, G.; Waters, N. Review of remote sensing methodologies for pavement management and assessment. Eur. Transp. Res. Rev. 2015, 7.
Zakeri, H.; Nejad, F.M.; Fahimifar, A. Image Based Techniques for Crack Detection, Classification and Quantification in Asphalt Pavement: A Review. Arch. Comput. Methods Eng. 2017, 24, 935–977.

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Manuel Parente

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1. Introduction

2. The Role of Smart Technologies in Current Pavement Management Systems

References