Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 -- 1442 2023-06-01 17:21:55 |
2 layout Meta information modification 1442 2023-06-02 04:29:29 |

Video Upload Options

Do you have a full video?


Are you sure to Delete?
If you have any further questions, please contact Encyclopedia Editorial Office.
Shtayat, A.; Moridpour, S. Assessing Pavement Condition System in Epidemic. Encyclopedia. Available online: (accessed on 22 June 2024).
Shtayat A, Moridpour S. Assessing Pavement Condition System in Epidemic. Encyclopedia. Available at: Accessed June 22, 2024.
Shtayat, Amir, Sara Moridpour. "Assessing Pavement Condition System in Epidemic" Encyclopedia, (accessed June 22, 2024).
Shtayat, A., & Moridpour, S. (2023, June 01). Assessing Pavement Condition System in Epidemic. In Encyclopedia.
Shtayat, Amir and Sara Moridpour. "Assessing Pavement Condition System in Epidemic." Encyclopedia. Web. 01 June, 2023.
Assessing Pavement Condition System in Epidemic

Maintaining the efficiency of road pavement is essential to achieving the highest road performance and comfort for road users. Pavement monitoring plays a significant role in maintaining the sustainability of road networks. Additionally, assessments have been performed using different equipment and devices or through visual inspections to determine the type and severity of pavement degradation.

pavement condition vibration prediction

1. Introduction

The infrastructure and transportation sectors are the backbone of urban cities and significantly contribute to a city’s economic income. Those sectors have faced many obstacles over the last few decades, including wars, natural disasters, and health pandemics, which have reduced their quality and usage while increasing their rehabilitation and reconstruction costs. Recently, the COVID-19 pandemic had a significant effect on business, decreasing the GDP percentage in most countries worldwide; additionally, many setbacks and disturbances were caused in those countries’ institutions and work systems during and after the pandemic. Due to the COVID-19 pandemic, the construction and management of transportation systems have been changed and affected by economic shut-downs and imposed social restrictions. Road networks have also suffered from neglect and lack of monitoring and maintenance due to government lockdowns, in addition to strict regulations that limit movement on roads and any form of construction, monitoring, inspection, and evaluation to improve road pavement condition. Therefore, the need for an accurate pavement monitoring system that satisfies the needs of the COVID-19 pandemic restrictions, in terms of keeping a safe social distance and minimizing contact with other people, is a top priority, allowing for the inspection, evaluation, and prediction of road pavement conditions in order to ensure the continuation of appropriate maintenance and treatment processes.
Currently, governments focus on improving road network sustainability and pavement quality to provide a high level of service for roadway users. Road networks need to be inspected and evaluated frequently by transport agencies to maintain the condition of road pavement. The technologies and techniques used for road pavement assessment vary according to several factors, including the road classification, traffic condition, monitoring device, level of deterioration, and environmental condition [1].
Sustainability is maintained for any road pavement during any pandemic by applying different planning, monitoring, assessment, prediction, and maintenance strategies after considering all the applied restrictions. In the COVID-19 pandemic, the same strategies have to be applied to keep the pavement in perfect condition. Pavement monitoring is the first step in pavement management and evaluation systems, and the data from monitoring reflect the condition and health status of road pavement surfaces [2][3]. However, outdoor procedures such as monitoring and inspections need to be fast, accurate, and committed to the imposed restrictions, such as social distancing [4].
On the other hand, predicting the pavement condition has a significant role in identifying the pavement health status and detecting and classifying the distress type, severity, and quantity [5]. The accuracy of pavement condition predictions depends mainly on selecting appropriate prediction models and data sizes and types [6]. This study used a support vector machine (SVM) model to detect and classify pavement defects on local roads based on vibration signals conducted by an accelerometer sensor. The accuracy of the prediction model is a significant factor for future treatment and maintenance actions for pavement surfaces. Preprocessing steps must be applied to prepare the data for building the prediction models, and include filtering, labelling, and feature extraction [7][8]. All preprocessing methods are necessary for noise-cancelling raw data. This study applied a high-pass filter to raw vibration signals to ensure the data were smoothed.

2. An Alternative System for Assessing Pavement Condition in the Event of an Epidemic

Levels of road service and pavement efficiency are among the most significant elements of transportation sustainability. Therefore, regular inspections and frequent monitoring are necessary to maintain high-quality road pavement to provide more comfort and safety for roadway users [9] while also reducing fuel consumption and vehicle maintenance costs [10]. However, some conditions may arise and affect the sustainability of road monitoring and, subsequently, periodic maintenance. Consequently, some deterioration and distress appear on the surface of pavements, such as cracking, patching, rutting, etc. Usually, the neglect of road maintenance and periodic monitoring causes damage to appear on the pavement surface [11]. Some unplanned cases of neglect, such as the COVID-19 pandemic, may cause postponements or delays to the monitoring and periodic maintenance of road pavements; thus, some types of deformations such as corrugation, cracking, and potholes may appear despite a decrease in road-use levels [12]. This happened at different road spots after around two years of lockdowns in most countries because of the COVID-19 pandemic [12].
During the COVID-19 pandemic, one of the obstacles that prevented fieldwork was the requirement of providing safe social distancing between the monitors themselves and other community members [13]. Hence, some field inspection requests faced rejection from governments or health organizations under the requirements of the pandemic restrictions in order to protect workers and prevent the widespread of coronavirus among the monitors [14]. Researchers have revealed that any epidemic could change the sustainability of the transportation system by increasing travel and operation costs, changing travel needs, and decreasing revenues [14].
In the past, precisely before the coronavirus pandemic, a significant revolution was produced in pavement monitoring systems by using high-quality devices to measure the condition of pavement surfaces [1][15]. To clarify, for the walk-and-look inspection method, at least two expert inspectors need to complete the rating of any local road surface, and the same monitor numbers are required when evaluating the pavement condition using a car or van [16][17]. Thus, working as a team is required to effectively complete the monitoring work.
Unfortunately, this method cannot be utilized in the presence of a severe epidemic that requires mandatory social distancing, such as COVID-19. Therefore, a safe pavement monitoring system is needed to be developed that could satisfy the required social distancing requirements in accordance with the restrictions imposed by the World Health Organization [18].
The proposed method for monitoring local roads using electric bicycles provides an opportunity for transport agencies and governments to conduct periodic monitoring of road pavements and determine the type and severity of their deterioration [2]. An e-bike was used as a pavement-monitoring vehicle to evaluate the pavement condition while moving over a road section (Katto et al. [19], Shtayat et al. [20][21], and Cafiso et al. [9]). They used an accelerometer sensor or smartphones fixed on the handlebar or rear basket to measure the vibrations of the vehicle chassis during movement over the pavement. The measured vibration signals revealed the condition of the pavement’s deterioration in terms of severity and location. The vibration signals’ fluctuations indicate potential distress on the road pavement [22]. Shtayat et al. [20] revealed that the level of the fluctuations changed according to the severity of distress. Moreover, a camera or mobile camera was used during the vehicle’s movement to match the vibration signals and record video regarding distress type, severity, quantity, and location. In this research, an accelerometer sensor and line-scan camera are used to identify the level of deterioration on a local road by measuring the vibration signals from an e-bike chassis. Additionally, a matching was made to confirm the conducted data with the observation results.
Predicting pavement performance is another way of measuring the efficiency of using the vibration-based method a pavement monitoring technique. To clarify, the accuracy of the prediction models depends mainly on the quality of the conducted data. Moreover, distress detection and classification are the main items in forecasting the pavement’s health status [23]. Many previous studies developed different prediction models to correctly and accurately analyse the condition of road pavement, including support vector machines (SVMs) [23], linear regression (LR) [24], a decision tree (DT) [25], a random forest (RF) [26], and neural networks (NNs) [27]. Prediction models provide a clear vision of the road pavement’s current and future health status. Moreover, by the prediction models, the researchers can detect distress and classify it according to its type and severity [28]. In this study, detection and classification processes were applied to identify the type, severity, and location of distress using the support vector machine model (SVM). SVM is a supervised machine learning algorithm that uses wide-range and dimensional data space for classification and regression analysis. It has been widely used for detecting, classifying, and forecasting the performance of vibrations [5]. Two main preprocessing steps must be applied to prepare the vibration data for building the prediction model, including data labelling and feature extraction [26]. Data labelling is a process focused on identifying the start and end points of the fluctuated signals that may represent potential distress [29]. The process aims to identify the possible anomalies on the pavement from signals and classify them into windows that include the entire length of distress. At the same time, feature extraction is a process that focuses on extracting the targeted spikes from labelled windows.


  1. 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.
  2. Shtayat, A.; Moridpour, S.; Best, B.; Shroff, A.; Raol, D. Dynamic monitoring of asphalt pavement using mobile application. In Proceedings of the 26th World Road Congress World Road Association (PIARC), Abu Dhabi, United Arab Emirates, 6–10 October 2019.
  3. Soni, J.; Gujar, R.; Shah, D.; Parmar, P. A Review on Strategic Pavement Maintenance with Machine Learning Techniques. In Intelligent Infrastructure in Transportation and Management; Springer: Singapour, 2020; pp. 141–151.
  4. Haque, M.M.; Chin, H.C.; Debnath, A.K. Sustainable, safe, smart—Three key elements of Singapore’s evolving transport policies. Transp. Policy 2013, 27, 20–31.
  5. Shtayat, A.; Moridpour, S.; Best, B.; Rumi, S. An Overview of Pavement Degradation Prediction Models. J. Adv. Transp. 2022, 2022, 7783588.
  6. Marcillo, P.; Caraguay, L.V.; Hernández-Álvarez, M. A Systematic Literature Review of Learning-Based Traffic Accident Prediction Models Based on Heterogeneous Sources. Appl. Sci. 2022, 12, 4529.
  7. Zhang, L.; Lan, M.; Zhang, J.; Tao, D. Stagewise Unsupervised Domain Adaptation With Adversarial Self-Training for Road Segmentation of Remote-Sensing Images. IEEE Trans. Geosci. Remote Sens. 2021, 60, 5609413.
  8. Zhu, Z.; Dong, Z.; Liu, Y. Detecting corrupted labels without training a model to predict. In Proceedings of the International Conference on Machine Learning, Baltimore, MD, USA, 17–23 June 2022; pp. 27412–27427.
  9. Cafiso, S.; Di Graziano, A.; Marchetta, V.; Pappalardo, G. Urban road pavements monitoring and assessment using bike and e-scooter as probe vehicles. Case Stud. Constr. Mater. 2022, 16, e00889.
  10. Chen, L.; Fan, Z.; Liu, P.; Qian, Z. Optimization Model of Network-Level Pavement Maintenance Decision considering User Travel Time and Vehicle Fuel Consumption Costs. Adv. Civ. Eng. 2021, 2021, 4699838.
  11. Prakash, G.; Yuan, X.-X.; Hazra, B.; Mizutani, D. Toward a Big Data-Based Approach: A Review on Degradation Models for Prognosis of Critical Infrastructure. J. Nondestruct. Eval. Diagn. Progn. Eng. Syst. 2021, 4, 021005.
  12. Li, J.; Yin, G.; Wang, X.; Yan, W. Automated decision making in highway pavement preventive maintenance based on deep learning. Autom. Constr. 2022, 135, 104111.
  13. Miller-Rushing, A.J.; Athearn, N.; Blackford, T.; Brigham, C.; Cohen, L.; Cole-Will, R.; Edgar, T.; Ellwood, E.R.; Fisichelli, N.; Pritz, C.F.; et al. COVID-19 pandemic impacts on conservation research, management, and public engagement in US national parks. Biol. Conserv. 2021, 257, 109038.
  14. Mogaji, E.; Adekunle, I.; Aririguzoh, S.; Oginni, A. Dealing with impact of COVID-19 on transportation in a developing country: Insights and policy recommendations. Transp. Policy 2022, 116, 304–314.
  15. Cao, W.; Liu, Q.; He, Z. Review of Pavement Defect Detection Methods. IEEE Access 2022, 8, 14531–14544.
  16. Zhang, D.; Zou, Q.; Lin, H.; Xu, X.; He, L.; Gui, R.; Li, Q. Automatic pavement defect detection using 3D laser profiling technology. Autom. Constr. 2018, 96, 350–365.
  17. Ragnoli, A.; De Blasiis, M.R.; Di Benedetto, A. Pavement Distress Detection Methods: A Review. Infrastructures 2018, 3, 58.
  18. Sohrabi, C.; Alsafi, Z.; O’Neill, N.; Khan, M.; Kerwan, A.; Al-Jabir, A.; Iosifidis, C.; Agha, R. World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19). Int. J. Surg. 2020, 76, 71–76.
  19. Katto, J.; Takeuchi, M.; Kanai, K.; Sun, H. Road infrastructure monitoring system using e-bikes and its extensions for smart community. In Proceedings of the 1st ACM Workshop on Emerging Smart Technologies and Infrastructures for Smart Mobility and Sustainability, Los Cabos, Mexico, 21 October 2019; pp. 43–44.
  20. Shtayat, A.; Moridpour, S.; Best, B. Using e-bikes and private cars in dynamic road pavement monitoring. Int. J. Transp. Sci. Technol. 2021, 11, 132–143.
  21. Shtayat, A.; Abu Alfoul, M.; Moridpour, S.; Al-Hurr, N.; Magableh, K.; Harahsheh, I. Waiting Time of Public Transport Passengers in Jordan: Magnitude and Cost. Open Transp. J. 2019, 13, 227–235.
  22. Harikrishnan, P.M.; Gopi, V.P. Vehicle Vibration Signal Processing for Road Surface Monitoring. IEEE Sens. J. 2017, 17, 5192–5197.
  23. Roberts, R.; Giancontieri, G.; Inzerillo, L.; Di Mino, G. Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning. Appl. Sci. 2020, 10, 319.
  24. Karballaeezadeh, N.; Mohammadzadeh, S.D.; Shamshirband, S.; Hajikhodaverdikhan, P.; Mosavi, A.; Chau, K.W. Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan–Firuzkuh road). Eng. Appl. Comput. Fluid Mech. 2019, 13, 188–198.
  25. Piryonesi, S.M.; El-Diraby, T.E. Using Machine Learning to Examine Impact of Type of Performance Indicator on Flexible Pavement Deterioration Modeling. J. Infrastruct. Syst. 2021, 27, 04021005.
  26. Kaur, D.; Pulugurta, H. Comparative analysis of fuzzy decision tree and logistic regression methods for pavement treatment prediction. WSEAS Trans. Inf. Sci. Appl. 2008, 5, 979–990.
  27. Guo, X.; Hao, P. Using a Random Forest Model to Predict the Location of Potential Damage on Asphalt Pavement. Appl. Sci. 2021, 11, 10396.
  28. Dan, H.-C.; Zeng, H.-F.; Zhu, Z.-H.; Bai, G.-W.; Cao, W. Methodology for Interactive Labeling of Patched Asphalt Pavement Images Based on U-Net Convolutional Neural Network. Sustainability 2022, 14, 861.
  29. Wen, T.; Ding, S.; Lang, H.; Lu, J.J.; Yuan, Y.; Peng, Y.; Chen, J.; Wang, A. Automated pavement distress segmentation on asphalt surfaces using a deep learning network. Int. J. Pavement Eng. 2022, 1–14.
Subjects: Engineering, Civil
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to : ,
View Times: 185
Revisions: 2 times (View History)
Update Date: 02 Jun 2023
Video Production Service