Perspective of Life-Cycle Cost Analysis and Risk Assessment for Airport Pavement in Delaying Preventive Maintenance

Abstract

Airport pavements deteriorate with age due to the impacts of environmental factors and air traffic. There is a dearth of studies on the scheduling of airport pavement maintenance and the importance of assessing the different scheduling strategies using Life-Cycle Cost Analysis (LCCA). An analysis of the cost–benefit study of a delay in maintenance alternatives should be done to determine whether a one-year delay is beneficial. This study concerns airport pavement management systems and the significance of a delay in several maintenance and preservation strategies depending on the analysis of inputs and outputs based on data from the Rocky Mountain Metropolitan airport in Jefferson County, United States. Herein, four preventive maintenance strategies were reviewed: three of the strategies involve crack treatment, namely crack sealing, patching, and a slurry seal, while the review of surface treatment only looks at the overlay method, which is done based on two Pavement Condition Indices (PCI 90 and PCI 80), and the resulting improvement in service life. The novel integrated LCCA + LCA program, namely PAVECO, is introduced in this research to compare alternatives from perspectives that are not purely economic, by considering direct costs, indirect costs, and salvage values. Results show that a one-year delay in preventive maintenance increases the deterministic life cycle cost by 16%. Based on the sensitivity analysis of the discount rate, the total cost shows more than a 10% decrease as the discount rate increased by 1%. Additionally, the uncertainty in PCI 80 is higher than that in PCI 90. The probabilistic model provides stakeholders with the opportunity to consider the alternative which is most suited to the allocated budget. It can be concluded that, based on the simulation of the effects of maintenance activities during the pavement life-cycle, preventive maintenance should start when the roads are in good condition to prompt managers and stakeholders to analyze the costs during the life-cycle. Postponing the preventive maintenance of airport pavement could raise the cost by 50%. The maintenance of airport pavements in developing countries is still one of the main challenges of pavement management due to the maintenance only being done when it is necessary.

1. Introduction

1.1. Research Background

The construction and maintenance of airport pavements require a huge amount of public funds. In both industrialized and developing countries, airport pavements are the lifeline of fundamental economic activities through which goods and people are transported [1]. The increase in air traffic with time is the main reason for the increase in the load imposed on pavements, and this affects pavement structures that have either reached or depleted their service life. For this reason, airport management authorities have shifted their focus from constructing new pavements to Maintenance and Rehabilitation (M&R) activities. Planning the future maintenance of fatigued pavement is becoming an increasingly difficult task due to the complex behavior of aging pavement [2,3]. At the same time, the increasingly limited budget, which is currently being experienced by most agencies, has resulted in reduced maintenance activities.

Most research tend to focus on the advancement in Airport Pavement Management Systems (APMS) made by several agencies in the last couple of decades. The main role of APMS is to aid decision-makers in determining the M&R techniques for the maintenance of airport pavements that are in usable condition over a predefined period of time in the most cost-effective manner [4]. Different APMS might adopt a different framework based on the preferences of agency requirements, although there are several similar functions in all APMS that are crucial in ensuring performance; for example, the prediction of pavement performance, network inventory, assessment of pavement condition, and planning strategies. Airport pavement management simplifies the LCCA for various alternatives and helps managers to determine the timing of the framework and choose the most effective alternative. It provides a consistent, systematic method for determining the required maintenance and identifies the preferred and the best maintenance schedule by predicting the future condition of pavements [5,6].

Currently, decisions on airport maintenance in developing countries are contingent on the historical experience of airport pavement experts and the judgment of management authorities without considering LCCA and other management practices. Given the current situation of financial prudence, pavement infrastructure requires a more deliberate and systematic approach in determining maintenance requirements and preferences [7]. Decision-making agencies responsible for pavement maintenance often consider urgent requirements or experience instead of adopting long-term management and documentation of data. This method does not allow the managing organizations to assess the cost-effectiveness of various alternative maintenance strategies, which, consequently, results in imprudent spending. The most recent development in airport pavement management provides a way of managing pavement economically and on time [8]. This paper aims to determine the most cost-effective maintenance schedule method that would produce a minimum life cycle treatment cost (Economic Benefit) by showing the effect of just a one-year delay in preventive maintenance and the risk assessment of this one-year delay. This would empower airport management companies to determine pavement maintenance requirements through the prudent spending of available funds.

1.2. Literature Review

The first airport pavement management system was introduced in the 1970s as a project implemented by the Construction Engineering Research Laboratory (CERL) for the US Air Force, and has subsequently led to the establishment of the PAVER and Micro PAVER systems [9]. In 1995, Harrison and McNerney assessed different APMS and concluded that the Micro PAVER system, which only looks at surface distress, is a time-consuming process that does not take into consideration the costs borne by airlines and airports, such as cost of user delay due to the closing of runways and expanded roughness, which causes fatigue to the airplane [10]. Broten and Wade (2004) performed a principled evaluation and assessment of airport pavement management activities by administering a survey on all aviation agencies in every state in the US [11]. Transportation agencies from several states, such as Michigan (MDOT, 1999), Minnesota (MNDOT, 2001), Ohio (ODOT, 2001), and California (CalTrans, 2008), prepared complete pavement maintenance manuals in their respective areas of authority [12,13,14,15]. The Unified Facilities Criteria (UFC), published by the U.S. Department of Defense, contains related reports and technical guidelines on the management of pavement maintenance (USDOD, 2004) [16]. In addition to these studies, which are appropriate for both rigid and flexible airport pavements, other APMS that are specific to other types of pavement were also conducted, such as the AirPACS, which was published to answer various questions regarding the maintenance of jointed plain concrete pavement (JPCP) at airports [17]. A comprehensive economic impact on the pavement network, which takes into consideration flight delay and operating costs, was addressed in the recently published Enhanced APMS (EAMPS) [18].

APMS can serve as an important tool that alerts managers of the trigger condition of pavements in order to determine the appropriate Preventive Maintenance (PM) in a life cycle. The LCCA for a particular segment or project analysis for possible M&R are taken into consideration when determining the available alternatives for each pavement section and the most cost-effective alternative for the life cycle of pavements. The best progressive strategy to better allocate the budget for several transportation agencies is a multi-year prioritization that utilizes an incremental cost-effectiveness analysis. This method, however, is very rarely performed for airport pavement networks [19]. The reason for the slow implementation includes shorter airport pavement networks, other, more significant performance issues at airports, and a lack of airport pavement management programs [9]. A review of the current studies on APMS shows that only a limited determination was made based on available data and information when preparing a review of regular airport pavement maintenance activities and their current application.

The principal components in all APMS are comprised of a network inventory, various methods for assessing and monitoring pavement conditions, the framework used to predict the assessment of pavement conditions, and management strategies. Condition reports are presented in the form of quality indices, such as PCI in relation to a single or a combination of various pavement characteristics, and represent the deteriorating state of pavement during its service life [20,21]. The PCI is a compilation of a wide range of data regarding various distresses required for the calculation of the total index. The disadvantage of using the PCI method is the questionable repeatability of visual surveys due to the subjective nature and distinction between the severity of distress, which could be managed through broad and careful inspection rules or through the use of automated data gathering devices [22]. Additionally, the functional assessment design and framework process of airport pavements depends on structural considerations such as constraining stresses, strains, and deflections [23].

Figure 1 shows that maintenance costs could decrease significantly if maintenance was done during the early stages of deterioration. Preventive maintenance should be carried out before pavements reach their threshold performance level (base level of serviceability) and require reconstruction. The trigger value or threshold performance level is the base adequate execution level, below which pavements are deemed unsuitable for its serviceable plan. This is frequently based on a particular agency’s implementation requirements or a users’ point of view. Agencies usually use threshold values that are based on performance plans rather than on time planning [24].

The effectiveness of short-term prevention, which considers pavement performance jump, is crucial in ensuring pavement performance and measures the viability of alternative maintenance. The effectiveness of long-term prevention, which incorporates service-life extension, average pavement performance, and the area jumped by the performance curve, is suitable for preservation “strategies” or “schedules” [26]. In APMS, several performance-predicting methods play important roles in managing crucial decision-making processes. The framework of the pavement management system is illustrated in Figure 2.

Pavement performance serves as a tool in the planning of future M&R activities and can be predicted using the deterministic and probabilistic methods [6,27,28]. PCI prediction models are functional performance models that are generally produced for the PAVER framework, but are also used in other APMS such as Integrated APMS and AirPAVE [29]. The methods created for performance prediction relate future PCI quality to a progression of informative or predictive variables, such as the age of pavement, deflection measurement, time since the last overlay, and traffic. To determine the number of cycles of load applied before failure occurs, structural performance models relate the characteristics of pavement material structure to the load applied to it. These types of prediction models are widely used by pavement managers and have been extended to pavements by different agencies, such as the Asphalt Institute, U.S Army Corps of Engineers, Portland Cement Association (PCA), and Shell International Petroleum Company [30,31].

1.3. Research Motivation

Over the last couple of decades, several studies have gained significant ground in changing pavement LCCA from an artistic expression to knowledge-based expression [5,6,7,19]. Furthermore, agencies in the US have reported preparing LCCA and comparing PC programs to upgrade their procedure. Even though there is a way to go in order to reach comprehensive LCCA methodology in the airport field, the highway agencies have connected LCCA more reliably, and at an elevated level, compared to the airport pavement community. Furthermore, the increasing costs associated with airport pavement in delayed preventive maintenance and rehabilitation, coupled with shortfalls in airport revenue, has led airport agencies to seek accurate decision-making tools that utilize economic and operations research techniques to arrive at long-term and cost-effective investments [4].

1.4. Research Objective

The main objective of this study, which is to compare the maintenance schedule that would produce a minimum life cycle cost, is achieved through the probabilistic life-cycle cost analysis and risk assessment by using a special tool named PAVECO. This program is comprehensive in considering LCCA and LCA as sustainable airport pavement management systems in Universiti Kebangsaan Malaysia (UKM) based on an analysis of the inputs and outputs. Section 2 describes the methodology employed for the integrated LCCA + LCA program, namely the PAVECO program for maintenance strategies for PCI 90 and PCI 80. This section also presents data used in this research. Section 3 describes the key results and outputs with the probabilistic method and risk assessment, as well as the deterministic method and sensitivity analysis. Section 4 presents the results and discussion. Finally, conclusions are presented in Section 5.

2. Methodology

The surface distress of airport pavements is usually evaluated using PCI. The value of PCI ranges from 0 to 100 and can be rated as shown in Table 1. PCI distress data are obtained through a visual survey carried out by expert pavement inspectors. PCI depends on the assessment of the type, quantity, and severity of distress [3,6,9].

In this research, the functional unit is defined as a 1-km runway pavement with a width of 50 m, making the pavement area 50,000 m², with two critical PCI being considered. The upper PCI limit (PCI 90) means that the pavement is in a good condition (minor distress), while the lower limit of critical PCI (PCI 80) means that the pavement is in a satisfactory condition (medium/low distress) for all alternatives of airport pavement management system models. The preventive maintenance schedule for PCI 90 is adopted from the Rocky Mountain Metropolitan Airport and the preventive maintenance schedule for PCI 80 is simulated based on the actual PCI 90. In 2012, Rahman and Tarefder indicated that, in airports with high and moderate traffic, these two critical PCI points could be reached within less than one year from one another [32]. For a particular treatment alternative in this study, maintenance work was performed in the base year of 2016, and PCI 90 was reached in 2017, while PCI 80 will occur in 2018, which means a one-year lapse within the same preventive maintenance activity. The following alternatives were considered for implementation during the 25-year pavement life cycle in the two different PCI ranges for the current study. Crack treatments include crack sealing and spray patching, while surface treatments include slurry seals and thin overlays.

Crack sealing is a maintenance activity that seals cracks with rubberized bituminous material [33]. It includes the routing of cracks, cleaning the routed surface, and applying the sealant on them. Crack filling is similar to crack sealing, but without the routing. Crack filling is easily damaged by snow-plows and, hence, is not cost-effective. Spray patching is a preventive maintenance measure that involves the application of a bituminous compound that is subsequently covered with a layer of aggregate. It could be done manually or by using special mechanical equipment to spray the emulsion and apply the cover aggregate, followed by preparation for basic compaction, all in a single pass. If spray patching is applied on the entire width of pavement, it can be categorized as surface treatment. A slurry seal, in itself, is a surface treatment that uses an unheated mixture of asphalt emulsion, graded fine aggregate, mineral filler, water, and other additives; these materials are mixed and spread over pavement surface as a slurry. A slurry seal is applied to provide a protective layer of bitumen-rich mortar. Hot-mix overlay of asphalt concrete pavement involves setting a layer of hot mix over existing pavement. Traditional asphalt concrete overlays are typically constructed with a base thickness of 2.5 inches (63.5 mm). Overlays less than 2.5 inches are called thin overlays [34].

The assessment of various scheduling strategies depends on the LCCA. LCCA is an engineering economic analysis technique used to differentiate the relative monetary benefits of different developments or rehabilitation design alternatives for a project. LCCA aids in the selection of the cheapest method which fulfills the objectives of a project. It is used to determine the benefits achieved when all choices that are being considered are equal. The LCCA procedure starts with the development of alternatives to achieve the targeted performance of a project. Initial and future activities required for executing each project design alternatives are then planned, and the expenses for these exercises are assessed. In this study, only direct agency expenditures (maintenance activities) are considered while ignoring user costs that result from agency work zone operations. By utilizing a monetary technique known as discounting, these costs are converted into present value and are then added to each option. Two statistical analysis approaches, namely deterministic and probabilistic approaches, can be used in LCCA. The methods differ in the way they handle the variability associated with the LCCA input. Each LCCA input variable is assumed to have a fixed, discrete value in the deterministic method, whereas in the probabilistic method, the LCCA inputs are specified by the probabilistic functions that convey both the range of likely inputs and the likelihood of them happening [35]. The triangular distribution is taken to signify the variability of the discount rate as suggested by Walls and Smith (1998) in the Interim Technical Bulletin [35]. The discount rate can consider a triangular or normal distribution, where the benefit of triangular is reducing the number of iterations to achieve the results faster [18]. A discount rate of 4% is used in this study; for both alternatives, 3%, 4%, and 5% are chosen as the least likely, most likely, and maximum likelihood values of the discount rate, respectively (1% standard deviation). The discount rate is the interest rate by which future costs (in dollars) are converted to present value. In other words, the discount rate is generally the distinction of interest and inflation rates, which show the real value of money over time, as shown in Equation (1). The real discount rate typically ranges from 3 to 5% [18].

(1)$i_{dis}=i_{int}-i_{inf} \left(\mathrm{Decimal}\right)$

where, $i_{inf}$ is the annual inflation rate and decimal and $i_{int}$ are the annual interest rate.

Maintenance cost is assumed to have a normal distribution. This article adopts the assumptions made by the ARA in 2011 [18]. The mean values of unit cost for crack sealing, patching, slurry seal, and overlay are assumed to be 1.80, 5.00, 3.50, and 8.90 $/m², respectively. Present Worth (PW) is calculated using Equations (2) and (3). Net Present Value (NPV) discounts all Future Costs (FC) to the present value by using a discount factor:

(2)$\mathrm{PW}=\mathrm{C}\times {\left[\frac{\left(1+i_{inf}\right)}{\left(1+i_{int}\right)}\right]}^n$

where,

$\begin{array}{c}\mathrm{PW}= \mathit{Present-worth} \mathit{cost}, \left(\$\right);\hfill \\ \mathrm{FC}= \mathit{Future} \mathit{cost} \mathit{in} \mathit{present-day} \mathit{terms}, \left(\$\right);\hfill \\ n= \mathit{Time} \mathit{until} \mathit{cost} \mathrm{C} \mathit{is} \mathit{incurred}, \mathit{years}. \hfill \end{array}$

(3)$\mathrm{PW}=\sum \mathrm{future} \mathrm{cost} (\frac{1}{{\left(1+i_{dis}\right)}^n})$

where, $i_{dis}$ = the annual discount rate.

The novel integrated LCCA + LCA program, namely the PAVECO program, is introduced in this study to compare alternatives not only from the economic perspective by considering direct costs, indirect costs, and salvage values, but also from the environmental perspective of the various modules, such as material, transportation, construction, usage, maintenance and rehabilitation, and end-of-life, which covers every aspect of the pavement’s life-cycle. This tool can be applied to both flexible and rigid airport pavements to focus on all areas of analysis. The results obtained from this tool are useful to engineers and airport management as it provides a fair, unbiased, and defensible statistical method that evaluates the alternatives throughout the pavement life-cycle. This study attempts to determine the optimum pavement maintenance strategy. All benefits and costs were converted into monetary value. Information regarding pavements and their respective maintenance and unit costs were obtained from the Airport Cooperative Research Program Synthesis and is shown in Table 2 [9]. PCI increase data was obtained from a previous study [36]. The unit cost of crack sealing is assumed to have a typical crack density of 0.25 m/m² [37]. In the calculation of total cost, all treatments were applied over the entire area as a surface treatment, except for spray patching. The assumption is made that 30% of the area should be patched when patching is required. A 10% standard deviation is considered for all maintenance activities.

In 2004, Smadi mentioned using a guideline to quantify the benefits of pavement management in monetary terms [38]. The undiscounted expenditure timetable for minimum acceptable PCI 90 and 80 is shown in Figure 3. When PCI = 90, the pavement is in good condition and, therefore, the maximum life extension is considered for each activity. On the other hand, when PCI = 80, the pavement is in normal condition (satisfactory) and, due to medium distress, the mean of life extension is selected.

As illustrated in Figure 4, none of the strategies have priority over any other one and both maintenance activities would keep the pavement in similar condition, resulting in the same PCI 55–70 (fair condition) at end-of-life. However, the first strategy (starts when PCI 90) consists of six maintenance activities while the second strategy (starts when PCI 80) considers seven maintenance activities during 25 years of analysis. As mentioned previously, PCI 80 is simulated based on the data for PCI 90, which is the real data from the Rocky Mountain Metropolitan Airport, by considering the timetable activity framework (Figure 3).

3. Results and Outputs

3.1. Results for the Deterministic Method and Sensitivity Analysis

In deterministic LCCA, a single value is selected for each input parameter (usually the value considered most likely to occur based on historical evidence or professional experience), and the group of selected values are then used to compute a single projected life-cycle cost. Since each input parameter is represented by only one value, the uncertainties and variations known to exist for these variables in the real world are not fully accounted for. The deterministic results are presented in Table 3, and Figure 5 shows a detailed comparison of all activities to illustrate the difference between the two alternatives. A discount rate of 4% is used in this deterministic study.

The sensitivity analysis method is used to determine the variables that influence the result at the highest level. By utilizing the sensitivity analysis, the variables of the model were identified and a ranking of the considered options can be rearranged by determining the breakeven points. If an adjustment in the variables of a model is the same as the discount rate, it could influence the ranking of the achievable design options but will not allow for the development of dominant alternative design options [6]. In addition, the impact of a single-variable model on the results of the analysis can be judged through sensitivity analysis; however, it is not practical for managers to accomplish a simultaneous and consolidated impact of the several-variables model on LCC outcomes and rankings. Finally, no tendency of specific values was detected as a probability distribution was not assigned to the variables. As shown in Table 4, the discount rate’s sensitivity analysis shows more than a 10% decrease in total cost when the discount rate is increased by 1%. The variation in decrease for the different discount rates are shown in Figure 6.

3.2. Results for the Probabilistic Method and Risk Assessment

Probabilistic LCCA simulates, and accounts for, the inherent variability of the input parameters. For a given pavement strategy, sample input values are randomly drawn from the defined frequency distributions, and the selected values are used to compute one forecast life-cycle cost value. The sampling process is repeated hundreds or even thousands of times, thereby generating many forecast life-cycle cost values for the pavement strategy. The resulting forecast costs can be analyzed and compared with the forecast results of different alternatives to identify the most economical strategy.

For airport pavement LCCA, it is recommended that the probabilistic computation approach be used when reliable historical data exist to model one or more of the input parameters [18]. Probabilistic LCCA was performed using PAVECO. The discount rate and unit cost are considered as normally distributed items with a 1% and 10% standard deviation, respectively, in this program. The users are allowed to change the standard deviation based on the latest available data in PAVECO. The results of the probabilistic approach are shown in Table 5.

Figure 7 shows the NPV histogram curve for the two PCI alternatives, namely PCI 90 and PCI 80, where the probability is the area below the curve. The whole range of possible results is displayed with the evaluated probability of every result actually happening. There is no assumption that a specific option is better. The fundamental advantage of the histogram is that it demonstrates variability of the mean. A wider distribution shows a larger variability. As can be seen in Figure 7, the result for Alternative II (PCI 80) is more uncertain than that of Alternative I.

Two important financial metrics to measure the potential downside (or upside) of an investment are the Value at Risk (VaR) and Value at Gain (VaG). VaR represents the threshold value of an investment cost for a given probability [39,40]. The concept of upside and downside risk is important in interpreting risk assessment. Upside risk for total cost reflects the opportunity for a lower cost than the mean of the cost (under-run), while the downside risk for the total cost reflects the probability of financial failure (over-run). The cumulative diagram defines the risk assessment for maintenance cost activities as given in Figure 8. In this case study, the probability of downside risk (over-run) for both options is about 50%.

If the amount of the budget is defined in the design phase, a risk assessment would help managers and stakeholders choose the best option that is close to the allocated budget. A comparison between Alt I (PCI 90) and Alt II (PCI 80) options indicate that the VaR for the $700,000 investment on Alt 1 option is around 10%, while the VaR is around 60% for investing on Alt II. This means that for the 5000 iterations processed, 90% of the computed values for NPV are less than $700,000 when maintenance activities are carried out one year earlier (PCI 90). A steeper cumulative slope means that the variability is lower, while a flatter slope indicates greater variability. Since the slope for Alt II (PCI 80) is steeper than that for Alt I (PCI 90), Alternative II has a higher variability (uncertainty) than Alternative I.

Concerning the consequence of making a decision based on risk assessment outcomes, decision-makers need to characterize the level of risk the agency could endure. Stakeholders who could afford lower risk prefer a small spread of inconceivable outcomes, with a greater probability associated with favorable results. Decision-makers who are risk-takers would accept a wider spread, or acceptable variation, in the distribution of the result [35].

4. Discussion

Results are typically stated as a single deterministic number, and no explanation of the variability in the data quality or the source is usually given [35]. The consequences from this study regarding data sources, data ranges, input variability, probabilistic analysis, and comparison of tools on a single project, as well as the comparison of a maximum of three different projects using the same data, indicate that users must be aware of the system boundary, data sources, and limitations while using LCCA and LCA tools to compare design, material, and parameter alternatives. In general, PAVECO appeared to be better at differentiating projects with different scenarios because it exhibits superior data quality, transparency, a more complete scope, and a more comprehensive system boundary. Furthermore, in developing countries, maintenance is done on a reactive (required to do) basis, while in developed countries, the most challenging task in pavement management is to maintain the performance of airport pavements through preventive maintenance. In this study, starting preventive maintenance activities at two different levels of PCI (90 and 80), which occur within one year of each other at international airports, indicates the following:

• As shown in Figure 4, preventive maintenance starts when the roads are in good condition and the result of the simulation shows that at the end-of-life module, both trends finish at the same PCI level (55 to 70-Fair level). This similarity would prompt managers and stakeholders to analyze the costs during the life-cycle;

• The only difference in preventive activities between PCI90 and PCI80 is one additional slurry seal and a different timeframe. These differences show an approximately 16% increase in total life-cycle cost when the maintenance is postponed for only one year (PCI80);

• Sensitivity analysis indicates a 10% decrease in total costs when the discount rate is increased by 1%. This is due to the NPV of the economic mode analysis chosen in this study. This sensitivity will change if the net future value (NFV), or another method, is used instead of the economy mode;

• The results of probabilistic methods show that postponing preventive activities for only one year (PCI80) will increase the cost, in addition to increasing uncertainties in comparison to commencing preventive maintenance on time (PCI90);

• Although the VaR is always higher in PCI80, and this represents the risk of investment exceeding the defined budget for the project, the upside risk (under-run) and downside risk (over-run) are almost equal for both alternatives;

• The findings exhibited that PAVECO’s outcomes are not exceptional but are on the same order of magnitude as those in the reviewed literature.

5. Conclusions

One of the most important assets of a country is its airport network, which should be managed and maintained as scheduled to ensure that the airports are in a satisfactory condition. The advanced pavement management framework provides a precise and systematic process for keeping track of the inventory of pavement infrastructure, monitoring pavement construction, determining the appropriate maintenance for each pavement alternative with a suitable schedule, planning and funding airport pavement preservation exercises, and assessing the cost-effectiveness of previous pavement preservation activities.

Delaying preventive maintenance for only one year results in an approximately 16% increase in the life-cycle cost of airport pavements. Sensitivity analysis of discount rate shows more than a 10% decrease in total cost when the discount rate is increased by 1%. In addition, postponing preventive maintenance activities could increase the cost by up to 50%. It should be noted, however, that preventive maintenance in developing countries still is being based on a reactive basis.

Author Contributions

Conceptualization, writing—original draft preparation, and methodology, P.B.; Validation and review, S.H.K.; Software, H.A.O.; Formal analysis, A.M.A.-S.; Investigation and resources, A.M.M.; data curation, A.M.; Visualization, M.I.K.; project administration, M.H.S.; Supervision and writing—review and editing, N.I.M.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors express their gratitude to the University Kebangsaan Malaysia (DIP-2020-003). The authors would like to acknowledge the support of Prince Sultan University for the expert support and for paying for the Article Processing Charges (APC) of this publication.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Tables

Figure 1. A performance curve of the general life cycle of pavements [25].

Figure 2. A framework of a general pavement management system.

Figure 3. Timetable maintenance strategies for PCI 90 and PCI 80.

Figure 4. Simulation of the effects of maintenance activities during the pavement life-cycle.

Figure 5. Deterministic results of different maintenance schedule activities.

Figure 6. Variation in decrease for different discount rates.

Figure 7. NPV histogram for PCI 90 and PCI 80.

Figure 8. Cumulative risk assessment diagram for PCI 90 and PCI 80.

Table 1

Airport pavement treatments based on PCI [9].

PCI Rating	Description	Preservation Treatment
100–86	Good—minor distress	Routine maintenance
85–71	Satisfactory—medium/low distress	Preventive maintenance
70–56	Fair—Severe distress	Corrective maintenance/Rehabilitation
55–41	Poor—low operational problem	Rehabilitation
40–26	Very poor—high operational problem	Rehabilitation/Reconstruction
25–11	Serious—operational restriction	Reconstruction
10–0	Fail	Reconstruction

Table 2

Unit cost and life extension of the different maintenance strategies [9,36].

Maintenance Activity	Life Extension (Years)	PCI Rise	Mean Unit Cost ($/m2)
Crack Treatment	2–4	5	1.80
Spray Patching	3–5	5	5.00
Slurry Seal	4–6	10	3.50
Thin Overlay	8–12	15–20	8.90

Table 3

NPV of different maintenance scheduling treatments.

PCI	MaintenanceActivity	No. ofApplication	Year (s)	$\frac{1}{{\left(1+{\mathit{i}}_{\mathit{d}\mathit{i}\mathit{s}}\right)}^{\mathit{n}}}$	PCIbefore	PCIafter	NPV(1000 $)
90	Crack Sealing	2	117	0.9620.513	9075	9580	86.5446.20
	Spray Patching	2	521	0.8220.439	8570	9075	61.6432.91
	Slurry Sealing	1	10	0.676	75	85	118.22
	Thin Overlay	1	16	0.534	65	85	237.50
	TOTAL COST						583.01
80	Crack Sealing	2	216	0.9250.534	8065	8570	83.2148.05
	Spray Patching	2	519	0.8220.475	>75>60	>80>65	61.6435.60
	Slurry Sealing	2	923	0.7030.406	7055	8065	122.9571.00
	Thin Overlay	1	14	0.577	65	85	256.98
	TOTAL COST						676.43

Table 4

Discount rate sensitivity analysis results.

MinimumPCI	Maintenance	NPV 3%(1000 $)	NPV 4%(1000 $)	NPV 5%(1000 $)
90	Crack Sealing	87.3854.45	86.5446.20	85.7139.27
	Spray Patching	64.7040.32	61.6432.91	58.7626.92
	Slurry Seal	130.22	118.22	107.43
	Thin Overlay	277.31	237.50	203.86
TOTAL COST		654.38	583.01	521.95
80	Crack Sealing	84.8356.09	83.2148.05	81.6341.23
	Spray Patching	64.7042.77	61.6435.60	58.7629.68
	Slurry Seal	134.1288.67	122.9571.00	112.8156.97
	Thin Overlay	294.20	256.98	224.76
TOTAL COST		765.38	676.43	605.84

Table 5

Alternatives probabilistic output of LCCA.

Statistics	Alternative I (PCI 90)	Alternative II (PCI 80)
Mean	621,868.25	725,150.90
Standard Deviation	95,026.97	114,582.50
Min	477,742.92	551,628.81
Max	803,567.58	944,682.94

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