Factors Affecting Pavement Condition: Comparison
View Latest Version
Please note this is a comparison between Version 2 by Lindsay Dong and Version 1 by Abdullah I. Al-Mansour.

The pavement experiences deterioration due to traffic and environment, i.e., unsatisfactory riding quality and structural inadequacy, over time. Thus, predicting pavement performance over time is one of the key elements of any pavement maintenance management system (PMMS).

  • pavement maintenance management system
  • expert system
  • pavement performance
  • cost-effective maintenance alternatives

1. Introduction

PMMS is a program to maintain and preserve the pavements of the road network at an acceptable level of safety, quality, and performance and minimize their lifecycle costs as a major component of a comprehensive pavement management system (PMS) [2][1]. The basic purpose of a PMMS is to achieve the best value possible for the available public funds and to provide safe, comfortable, and long-lasting transportation infrastructure [3][2].
An essential element of any PMMS is a reliable and accurate way to predict pavement performance. A typical way to predict the performance of the pavement is by developing pavement performance models, which can be used to determine the future maintenance needs and the required maintenance budget and to set maintenance priorities based on the available budget. Pavement performance models are imperative for a PMMS and perform a function similar to that of a car engine. Based on these models, the future pavement condition can be forecasted and can be used as an input to the maintenance policy to predict the required maintenance and rehabilitation (M&R) activities. Therefore, the PMMS assists top management in finding optimum strategies for providing and maintaining pavement in a serviceable condition [1][3].
A pavement performance curve describes the relationship between pavement conditions with pavement age or traffic level. The performance of the pavements was assessed to determine whether they were good or poor [4,5][4][5]. Pavement conditions can be determined through performance indicators such as pavement distresses, riding quality, structural adequacy, and skid resistance. These performance indicators can be combined into one index, e.g., the pavement condition index (PCI) has been widely utilized to allocate pavement maintenance strategies [6]. As shown in Figure 1, PCI values are computed based on visual assessment and indicate the types of distress, magnitude, and quantity present on the pavement surface [5]. The PCI provides the evaluation of the pavement’s state, with scores ranging from 0 (failed) to 100 (excellent), as illustrated in Figure 2.
/media/item_content/202206/62a7f86de4cf6applsci-12-04802-g001.png
Figure 1.
The procedure of the pavement condition index (PCI).
/media/item_content/202206/62a7f87c4fc6bapplsci-12-04802-g002.png
Figure 2.
Pavement condition index (PCI) rating.
Maintenance activities are used to prevent pavement deterioration or reduce the rate at which the pavement deteriorates. The effect of M&R can be represented as the improvement in the pavement condition. This effect can be evaluated based on the change in the slope of the pavement condition curve or as the change in the level of pavement condition before and after performing a maintenance strategy. In some cases, the effect is measured by the frequency of the maintenance applied to extend the pavement’s service life. The concept of pavement maintenance’s effect on pavement condition is illustrated in Figure 3.
/media/item_content/202206/62a7f8892ec16applsci-12-04802-g003.png
Figure 3.
Expected effects of maintenance strategies on pavement condition.
One technique to evaluate the effects of different M&R strategies is by building performance models. The procedure of modeling the M&R effect starts by determining the factors that may influence the pavement condition. These factors may include the pavement construction details, maintenance age, traffic level, availability of drainage system, maintenance age, environmental factors, etc. Once all the possible factors that may influence pavement condition are determined, pavement sections can be grouped based on the type of maintenance they received. The maintenance models can then be developed to predict pavement conditions under each maintenance strategy.

2. Factors Affecting Pavement Condition

Pavement Age

Over time, the hardening of asphalt increases, thus making it more brittle and susceptible to cracking. In pavement performance prediction, pavement age is measured from the date of construction or from the date of the last major maintenance application.

Pavement Maintenance Types

The type and timing of maintenance have a direct impact on the condition of the pavement. In tThe present study, the tytypes of maintenance strategies to be evaluated were restricted to four only. These M&R strategies were no maintenance application, basic routine maintenance, overlay, and reconstruction.

Traffic Level

Pavement deterioration is highly affected by traffic volume and vehicle types. The traffic level is usually expressed as the average daily traffic (ADT). To evaluate the M&R performance, road sections were categorized based on traffic level into two classes, high and low. A section of low traffic is receiving traffic of fewer than 3000 vehicles per lane per day (v/d), while a section of traffic that exceeds 3000 v/d is classified as high traffic.
Predicting pavement performance is fundamental for appropriate resource allocation at the network level [7]. Reducing the prediction error of pavement deterioration aids transportation organizations to save money [8,9][8][9]. Several data-driven pavement performance models have been developed. A popular approach in pavement performance is the model of Markov prediction. To predict pavement performance, several statistical regression models are commonly utilized [10,11,12][10][11][12].
A knowledge-based expert system is one of the results of applications of artificial intelligence (AI) research to software programming. AI is a branch of computer science that studies ways of enabling computers to perform tasks that appear to require human intelligence. Expert systems are named for their essential characteristic: they provide advice for problem-solving that is derived from the knowledge of experts [13]. Expert systems typically use a set of rules and facts to make inferences that are reported as conclusions. The inference process relies heavily on theories of logical deduction. The objective of an expert system is to help the user choose among a limited set of options, within a specific context, from information that is more likely to be qualitative than quantitative.
Expert systems or machine learning have become popular recently and have been successfully applied in many fields. The framework for an expert system primarily consists of the knowledge base, inference engine or inference machine, context or working memory, explanation module, and user interface. The collection of facts, rules and computational procedures for manipulating the information in the knowledge base to reach conclusions is called the control mechanism, or the inference engine. The objective of the inference engine is to find one or more conclusions for a sub-goal or a main goal of the consultation. It searches the facts and rules in the knowledge base and identifies and stores conclusions to use in new facts for subsequent inference. The context or working memory contains all the information derived from the inference process. This information describes the problem being solved, the rules that have been “fixed,” and the conclusions derived from them. The explanation module contains explanations for every inference made or piece of advice given. The user interface provides for dialogue between humans and machines.
Knowledge acquisition and representation are the most difficult parts of building an expert system. They often require the knowledge engineer to interact intensely with one or more experts in the application domain. Not all problems are suitable for expert systems. For a successful application, there must be an expert or expert in the domain, and the problem should be specialized. The expert selected must be able to articulate the special knowledge needed to solve problems in the domain. If solving the problem involves the use of rules of thumb or symbolic reasoning, then an expert system might be appropriate and helpful [14].
There is a considerable turnover in the ranks of maintenance management; in addition, much-experienced personnel is now retiring. However, many maintenance decisions are made by the unit’s foremen, sub-district superintendents, or field supervisors, based on their judgment. If they do not have ample experience, expert systems can help fill the gap. To illustrate, maintenance needs estimation through visual inspection requires foremen not only to correctly assess the road condition but also to convert the road condition information into workloads. Although the foremen can be trained to assess the road condition, workload estimation demands considerable experience and know-how. There is inherent uncertainty in the unit foreman’s translation of distress information into the type and number of activities to be performed. A knowledge-based expert system could be applied to take care of the uncertainty in the estimation process. It provides a tool for estimating the activities in the absence of an expert. The knowledge base can be tested and altered over time to improve its performance. Since the computer stores the expert system, it can become a part of a larger cost estimation system for the entire road network [15]. The expert system involves base knowledge, facts gathered through time, and a set of rules for each situation shown in the program. Additions to the knowledge base can improve advanced expert systems. A ‘tool’ or a ‘shell’ is a type of developing software that may be used to create an expert system. As shown in Figure 4, a shell is a comprehensive development environment for creating and managing knowledge-based applications [16,17][16][17].
/media/item_content/202206/62a7f8b1165bfapplsci-12-04802-g004.png
Figure 4.
Expert system architecture.
Sharaf E. and Kotob A. [20][18] designed a pavement maintenance management expert system for developing countries. The system consists of two programs: the first is an algorithmic program and the second program is an expert system. EXPSYS shell was used to develop this program. The input data of this expert system included many variables, such as section code, branch code, sample unit number, and condition survey. A two-fold system has been developed to assist highway agencies that lack in-house experts in the evaluation of asphalt pavement and assessment of maintenance and rehabilitation needs. First, the evaluation subsystem is an interactive algorithmic computer program, the output of which was PCI calculated from distress data. Secondly, the M&R subsystem is an expert system that simulates a consultation between the engineer and an expert in the field. The system has been developed and verified using data from portions of the Egyptian road network, where comprehensive visual inspection data were available.

References

  1. Haas, R.; Hudson, W.R.; Zaniewski, J. Modern Pavement Management; Krieger Publishing Company: Malabar, FL, USA, 1994.
  2. Lee, K.W.; Bowen, G. Standardization in Pavement Management Implementation for Municipal-Maintained Roads in Rhode Island; Special Technical Publication (STP) No. 1121, a Chapter in Standardization in Pavement Management Implementation; American Society for Testing and Materials: Montgomery, PA, USA, 1991; pp. 211–227.
  3. Al-Mansour, A.; Al-Swailmi, S.; Al-Swailem, S. Development of Pavement Performance Models for Riyadh Street Network. Transp. Res. Rec. 1999, 1655, 25–30.
  4. Wu, K. Development of PCI-Based Pavement Performance Model for Management of Road Infrastructure System; Arizona State University: Tempe, AZ, USA, 2015.
  5. Miah, M.; Oh, E.; Chai, G.; Bell, P. An overview of the airport pavement management systems (APMS). Int. J. Pavement Res. Technol. 2020, 13, 581–590.
  6. Shah, Y.U.; Jain, S.S.; Tiwari, D.; Jain, M.K. Development of overall pavement condition index for urban road network. Procedia-Soc. Behav. Sci. 2013, 104, 332–341.
  7. Meegoda, J.N.; Gao, S. Roughness progression model for asphalt pavements using long-term pavement performance data. J. Transp. Eng. 2014, 140, 04014037.
  8. Madanat, S. Incorporating inspection decisions in pavement management. Transp. Res. Part B Methodol. 1993, 27, 425–438.
  9. Rahman, M.M.; Uddin, M.M.; Gassman, S.L. Pavement performance evaluation models for South Carolina. KSCE J. Civ. Eng. 2017, 21, 2695–2706.
  10. Abaza, K.A.; Ashur, S.A.; Al-Khatib, I.A. Integrated pavement management system with a Markovian prediction model. J. Transp. Eng. 2004, 130, 24–33.
  11. Pan, N.F.; Ko, C.H.; Yang, M.D.; Hsu, K.C. Pavement performance prediction through fuzzy regression. Expert Syst. Appl. 2011, 38, 10010–10017.
  12. Liu, L.; Gharaibeh, N.G. Bayesian model for predicting the performance of pavements treated with thin hot-mix asphalt overlays. Transp. Res. Rec. 2014, 2431, 33–41.
  13. Cohn, L.; Harris, R. Knowledge-Based Expert Systems in Transportation; Synthesis of Highway Practice 183; National Cooperative Highway Research Program (NCHRP): Washington, DC, USA, 1992.
  14. Ritchie, S.; Cohn, L.; Harris, R. Development of Expert Systems Technology in the California Department of Transportation; Transportation Research Record No. 1187; Transportation Research Board, National Research Council: Washington, DC, USA, 1988.
  15. Sienna, K.; Fwa, T.; Mouaket, I. New Tools and Techniques for Highway Maintenance; Transportation Research Record No. 1276; TRB: Washington, DC, USA, 1990.
  16. Shiue, W.; Li, S.-T.; Chen, K.-J. A frame knowledge system for managing financial decision knowledge. Expert Syst. Appl. 2008, 35, 1068–1079.
  17. Mohammed, A.A.; Ambak, K.; Mosa, A.M.; Syamsunur, D. Expert system in engineering transportation: A review. J. Eng. Sci. Technol. 2019, 14, 229–252.
  18. Sharaf, E.; Kotob, A. A Simple Application of Expert System in Pavement Maintenance Decision Support a Second Scientific Symposium on Maintenance Planning and Operations; King Saud University College of Engineering: Riyadh, Saudi Arabia, 1993; pp. 24–26.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
Video Production Service
Feedback