Maximizing Pavement Service Life: A Comprehensive Process Model Based on Structural Life Extension, Serviceability Deterioration Processes, and Asset Value

Abstract

This research aimed to develop a comprehensive decision-making model for road rehabilitation, with the goals of extending pavement service life, minimizing major repairs, and improving the efficiency of investment and resource planning. The proposed methodology integrates structural condition, functional performance, and total economic value across the pavement lifecycle. It enables engineers and road managers to make informed decisions based on structural capacity, functional performance, asset value, and optimized rehabilitation strategies. The model was validated through case studies using data from Central European roads and accelerated pavement testing. It compared conventional and high-modulus asphalt overlays of equal thickness, demonstrating that a 3000 MPa increase in modulus extended residual life by over 30% and raised structural value by EUR 5.8/m². This approach enhances planning and prioritization of rehabilitation activities, supports the use of higher-quality materials, reduces lifecycle costs and CO₂ emissions, and facilitates integration with asset management systems. By linking pavement design, performance prediction, and asset management, the model supports strategic decision-making under performance and budget constraints.

1. Introduction

Processing the service life of the pavement at the required level is crucial, considering that the pavement materials embedded within the pavement structure gradually lose their original properties due to traffic loads and climatic conditions. The resulting loss of structural integrity and pavement serviceability requires a sustainable approach through technically and economically efficient rehabilitation methods.

To maximize pavement service life at minimal cost and make informed decisions regarding the use of reinforcing overlays or complete reconstruction using new or recycled materials, a comprehensive decision process is indispensable. This process prioritizes various factors, including the condition of the pavement and its subsystems, such as the structural pavement condition, pavement serviceability, calculation of pavement asset value, economic assessment, and optimization of pavement rehabilitation.

Structural pavement condition refers to the pavement’s ability to withstand traffic loads and represents its current state relative to its pristine condition. It is represented by the residual structural life as a proportion of the design life. The demand for a precise expression of the structural pavement condition led to the development of a new methodology, which determines the pavement’s residual structural life and its ratio to the design life using analytical–experimental measurements and calculations. The method for calculating residual structural life is based on a mathematical model of a layered elastic half-space and fatigue experimental tests. To apply this equation in determining residual structural life, it is necessary to ascertain the actual modulus of elasticity and strength, particularly in the base course of the pavement. These values are estimated during the evaluation of pavement load-bearing capacity using a falling weight deflectometer [1,2]. The falling weight deflectometer imparts an impact on the pavement, eliciting a flexible response in the form of a deflection bowl. The configuration of this bowl reveals the deflection measurements from individual sensors affixed to the pavement at specific distances from the impact point. Through a process referred to as back-calculation [3,4], the actual modulus of elasticity for each pavement layer is computed within a layered elastic half-space model [5,6,7]. Subsequently, based on the modulus of elasticity, the stresses in the pavement layers are determined [8,9,10,11]. Following the calculation of the stresses and strains in the pavement structure in its actual condition, it is then possible to calculate the residual life based on experimentally obtained fatigue parameters of asphalt concrete pavement materials, as defined by the European standard [12,13].

Pavement serviceability refers to the pavement’s ability to provide a safe, economical, and comfortable ride and traffic quality. Pavement serviceability is expressed in terms of roughness and surfacing damage. Roughness and surfacing damage may not always be related to the bearing capacity of the pavement and can often be repaired with thin overlays. However, roughness is generally linked to the overall pavement structure and its bearing capacity. Therefore, it is necessary to assess pavement condition based on the level of bearing capacity. The evaluation includes longitudinal and transverse unevenness and their impact on vehicle operating costs and travel time costs. To derive reliable mathematical deterioration functions, accurately measured real pavement degradation data must be obtained either through analytical and experimental tests conducted in an advanced accelerated pavement testing facility or by analyzing long-term pavement performance data collected through rigorous diagnostic procedures and stored in a road asset inventory. The progression of these adverse changes can be mathematically expressed through degradation functions known as pavement performance models [14,15,16,17,18,19,20].

The degradation function for unevenness can be defined based on experimental tests or long-term diagnostic measurements carried out by the road administrator throughout the pavement lifecycle (20+ years). The pavement performance model is influenced by the type of asphalt binder used—such as polymer-modified, unmodified, or recycled binders—as each exhibits distinct aging, stiffness, and fatigue characteristics under traffic and environmental loads. Similarly, the choice of aggregates, including their size, shape, mineral composition, and resistance to fragmentation and polishing, plays a crucial role in the rate and nature of surface degradation. These material-specific factors directly affect the pavement’s ability to resist rutting, cracking, and settlement, thereby shaping the overall progression of unevenness over time [21,22,23,24].

Rigorous data collection policies, along with effective data management and evaluation practices, are essential for the development of pavement performance models and road asset management systems [25,26,27].

Experimental measurements of actual pavements using accelerated pavement testing facilities can serve to supplement, or ideally substitute, the long-term pavement performance monitoring, particularly because pavement deterioration models are dependent on external boundary conditions such as temperature, humidity, and loading frequency. This critical dependence has been extensively studied not only by the authors in prior work [28,29,30] but also broadly within the pavement research community, as documented in numerous studies on environmental and mechanical degradation mechanisms [31,32].

Given the inherent variability in how boundary conditions affect pavement behavior, it is essential that performance models use locally calibrated deterioration functions to improve prediction accuracy.

The initial definitions and implementation of pavement asset value in road network management, based on objective data, information systems, and decision-making processes, were presented in [33,34,35,36]. The importance of pavement and road assets in relation to service standards and societal utility has been documented in [37,38,39]. Pavement asset value can also be defined as an integrated system-based approach focused on achieving optimal asset condition and utilization, including risk assessment, using a holistic perspective. Pavement asset value must be assessed within a framework using objective and measurable indicators that reflect the value and performance of assets across the full range of their benefits and technical condition [40,41,42,43,44,45]. With the use of reliable and precisely defined deterioration trends in pavement serviceability parameters, it is possible to predict changes in asset value over time using pavement rehabilitation programs [46,47,48,49,50,51,52,53]. Consequently, road network funding policies can be evaluated based on the net increase or decrease in the value of the road network. Investments in development, rehabilitation, and improvement projects can be assessed and optimized through cost–benefit analyses (CBA) [54,55,56,57,58]. The allocation and optimal use of funds can then be achieved through optimization-based decision-making methods, such as the cross-asset allocation method [59,60,61,62,63,64].

2. Materials and Methods

Decision Process Model

The proposed pavement service life decision process algorithm, shown in Figure 1, is designed to include the necessary subsystems to maintain the required pavement service life. It is based on calculations of pavement structural condition, derived from residual structural life, and the progression of pavement serviceability, based on preventing unnecessary deterioration. These elements are integrated into the pavement asset value calculation. Since pavement is part of the road and the broader road network, the road asset value should also be included in the process. Finally, the algorithm incorporates economic assessment and optimization of rehabilitation. The outputs are pavement and road rehabilitation programs, constrained by limited financial resources and funding eligibility requirements.

While Big Data is shown in the flowchart as a potential input, the presented methodology primarily relies on conventional data sources available in road inventory databases maintained by road authorities. The mention of Big Data reflects anticipated future developments rather than current implementation.

As evident from the pavement asset value calculations, it is essential to know the rate of pavement deterioration, which is determined using the structural pavement condition. This condition is expressed by calculating the structural life expectancy and its ratio to the design life. From the perspective of pavement design, the residual service life is a crucial parameter.

To calculate the structural life expectancy, the crack initiation criterion is used, in accordance with the design methods for flexible and semi-rigid pavements. This criterion ensures that the radial stress at the bottom edge of the base course remains below its critical material strength. This condition can be expressed as an inequality, shown in Equation (1) [64,65]:

$${\sum }_{i=1}^n{Q}_i\times {\displaystyle \frac{{\delta }_{n,i}}{S_N\ast R_{i,I}}}\le 1$$

(1)

where σ_r,I denotes radial stress at the bottom of the base course during period “i” when subjected to the design axle load in MPa; R_i,I is the computed flexural tensile strength value of the base course material being assessed under the conditions in period “i” in MPa, and S_N is the fatigue coefficient.

When designing a new pavement, Equation (1) is applied as standard, as the material characteristics are defined by standard parameters—namely, the modulus of elasticity and Poisson’s ratio. The challenge arises when calculating stresses for older, in-service pavements. In such cases, the back-calculation method must be used, based on falling weight deflectometer (FWD) measurements and a numerical elastic deformation model in a layered elastic half-space. The output of the back-calculation method is the actual elastic moduli of the surfacing course, base course, subgrade, and subsoil. When these values are substituted into Equation (2) [64,65], the real current radial stress data are obtained.

The actual calculation of structural life expectancy is based on fatigue coefficients, denoted as coefficients ‘a’ and ‘b’ in the following equation. To derive these coefficients, it is essential to experimentally determine the parameters that define fatigue characteristics, representing the pavement’s resilience to repetitive loading. In this test, a sample of the pavement surfacing layer is subjected to repeated bending. The results are presented in a Wöhler diagram, as shown in Figure 2 and Figure 3. Radial stress and strength resiliency values in ACM layers based on DAL are shown in

Table 1. Radial stress and strength resiliency values in ACM layers based on DAL.

The Number of DAL	0	1,500,000	3,000,000	4,500,000	6,000,000	7,500,000
AC 11 O E∗ (MPa)	10,800	6000	5760	5600	5500	5450
AC 16 P E∗ (MPa)	8320	4550	4400	4300	4220	4160
Stress (MPa)	0.978132	0.634328	0.613133	0.600401	0.591348	0.584278
Strength (MPa)	2.4	0.769521	0.689998	0.643549	0.610559	0.584981

.

$$log{\epsilon }_{0j}=a_j+b\ast logN$$

(2)

where ε_0j is the maximal amplitude of proportional deformation in the initial phase of measurement; a, b are fatigue parameters that define the stress lines coefficient over a range of “N”, and N is the number of load repetitions.

The maximum number of design axle load repetitions that the pavement can endure can be calculated via Equation (3) [66]:

$$DAL={\left({\displaystyle \frac{\gamma \ast {\epsilon }_6}{{\epsilon }_j}}\right)}^B$$

(3)

where DAL denotes design axle load repetitions; ε₆ is the average deformation derived from the fatigue curve after 1.0 mil. loading cycles in µm/m; ε_j is the relative deformation on the bottom of a base course calculated with actual elastic moduli values, $\gamma$ is the reliability factor of the fatigue test, and B is the falling gradient of the fatigue line.

If the residual life is calculated, it is already possible to calculate the necessary overlay thickness as a newly designed layer so that the stresses correspond to the values according to Equation (1).

3. Case Study Calculation

To achieve the necessary pavement asset value, it is preferable to employ environmentally friendly technologies that utilize recycled materials. As an illustration, the structural life expectancy calculation based on pavement design is shown in Figure 4, which features an asphalt concrete (AC) cover material incorporating recycled materials. Deformation characteristics for paving materials are listed in

Table 2. Deformation characteristics of pavement cross-section.

Layer	Complex Modulus	Strength	Poisson Number	Layer Thickness
Surface course AC 11	7578	3.247 MPa	0.33	40 mm
Base course AC 16	9966	2.432 MPa	0.33	80 mm
Mechanically bound aggregate, 31.5	566	0.125 MPa	0.30	180 mm
Gravel sub-base, 31.5	364	0.076 MPa	0.30	200 mm
Sub-grade	100	-	0.35	-

. The complex modulus of the surfacing material is measured at different frequencies, as shown in

Table 3. Complex modulus of AC11 measured at different frequencies.

Samples of Complex Modulus for Different Temperatures (MPa)
Frequency (Hz)	−10 °C	0 °C	10 °C	15 °C	27 °C
1 Hz	14,523	10,920	7059	4685	1599
5 Hz	16,450	13,259	9169	6719	2920
10 Hz	17,388	14,250	9998	7575	3565
15 Hz	17,519	14,659	10,428	8019	3939
20 Hz	17,751	15,121	10,800	8436	4343

.

The resulting parameters are expressed in the Wohler diagram shown in Figure 2 and Figure 3. Both diagrams were obtained using the two-point (2PT) bending test under controlled conditions of a temperature of 10 °C and a loading frequency of 25 Hz. All tests were conducted on the same equipment and followed identical standard procedures, in accordance with [12]. The ε₆ value, which represents the strain ratio of the samples after 10⁶ loading cycles, reveals a 23% difference between the two mixtures.

For the pavement presented in this case study, the fatigue parameters—determined using the methods described in this section—are shown in

Table 4. Fatigue parameters of different mixtures are shown in Figure 3 and Figure 4.

Parameter	AC16-L Recyclate	AC16-L
A0	−38.2918	−25.5616
A1	−10.9971	−8.0090
b	−0.0909	−0.1249
ε6	93.85	114.61
Structural Life Expectancy (DAL 106)	1.1	1.4

. Its structural life expectancy was computed to be 1.4 × 10⁶ design axle loads for the new mixture and 1.1 × 10⁶ design axle loads for the recycled mixture.

For a more accurate structural life expectancy calculation, specimens taken directly from the pavement and cut to the required test dimensions can be used to determine the fatigue characteristics.

3.1. Pavement Unevenness Deterioration

The progression of unevenness deterioration can be mathematically described using degradation models. These models serve as the cornerstone of asset valuation, as they can predict the technical condition of individual assets within a road network.

Mathematically derived degradation functions are employed to predict pavement serviceability throughout its service life. These functions express changes in surface characteristics over time or, preferably, in relation to load cycles. The general shape of the degradation function illustrates the relationship between the relative value of a surface characteristic and either time or the number of load repetitions. Degradation models can be derived through experimental testing on an accelerated pavement testing (APT) facility shown in Figure 5, which simulates traffic loads and climatic parameters at a 1:1 ratio on a test pavement section. Alternatively, they can be developed based on long-term measurements of existing pavements using data archived in road inventories collected over the past 20 years. In our research, we applied both methods, so the degradation functions presented in this study were fitted using data obtained from APT experiments and long-term pavement monitoring, with model accuracy assessed through standard regression diagnostics such as R² values to ensure adequate representation of observed deterioration trends.

Measurements of transverse and longitudinal unevenness, skid resistance, and macro-texture were taken every 100,000 load cycles. Figure 6 illustrates the evaluation of transverse unevenness (RUT).

Unevenness is measured using profile levelling combined with a Bibus VX element scanner, which creates a highly accurate 3D point cloud scan (with precision up to 0.1 mm). Before each scanner measurement, contrast powder paint is applied to the pavement surface to enhance imaging, and positioning targets are placed to enable the handheld scanner to determine the point cloud’s position. The scanner outputs both transverse and longitudinal unevenness measurements, as shown in Figure 6 and Figure 7.

The mathematical expression of the derived transverse unevenness is given in Figure 8.

3.2. Longitudinal Unevenness IRI

Longitudinal unevenness analysis is based on long-term measurements conducted through the road inventory. Long-term pavement performance monitoring has been carried out since 1998 across a road network exceeding 18,000 km in length. These data were used to derive polynomial degradation functions for semi-rigid pavements on arterial roads, specifically validated for Central European climate conditions (

Table 5. Climate zone in which the presented deterioration functions apply.

Moisture Classification	Semi-Arid
Thornthwaite moisture index	−36
Duration of dry season (months)	7.8
Mean monthly precipitation (mm)	64.1
Temperature classification
Temperature Classification	Temperate—cool
Mean temperature (°C)	8.8
Avg. temperature range (°C)	20
Days T > 32 °C (days)	12.1
Freeze index (C-days)	332

).

Measurements were obtained using the PROFILOGRAPH GE system, whose outputs enable mathematical derivation of both longitudinal and transverse roughness functions. Longitudinal roughness is quantified through the international roughness index (IRI), calculated according to Equation (4) [64]:

$$\mathrm{I}\mathrm{R}\mathrm{I}={\displaystyle \frac{1}{N-1}}\ast \sum _{i=2}^N{T}_i$$

(4)

where Ti is the arithmetical average value of Ti ordinates, and N is the number of measurements.

The degradation function derived from long-term IRI measurements, showing the relationship between IRI values and design axle loads (DALs), is presented in Equation (5):

$$\begin{array}{c}IRI=-1.140 \ast 3+1.497 \ast 3-0.833\mathrm{x}+0.448\\ R^2=0.955\end{array}$$

(5)

3.3. Road Asset Value

In addition to pavement condition, asset value is also related to width, direction, and elevation characteristics, which are expressed through road category classification. Therefore, to ensure comprehensiveness, the calculation of road asset value should incorporate these factors. The road asset value calculation comprises road asset performance and pavement asset condition.

For road asset performance, the value depends on the level of service associated with the road category and the value of community benefits provided. For pavement asset condition, the value depends on structural pavement condition, pavement serviceability, and road user costs.

Road asset performance evaluation compares existing road parameters with required (designed or improved) parameters, considering both the level of service and societal benefits in terms of network performance for all road asset states. Pavement asset condition assessment utilizes calculations of structural pavement condition and pavement serviceability deterioration. The current asset value is determined by adjusting the present value using the ratio of remaining residual life to the pavement’s design life.

3.4. Road Asset Performance Value

Road asset performance is determined by the road category level of service and community benefits. The desired road category is defined; subsequently, the value of road asset performance is calculated as the ratio between the acquisition costs of the road asset in its current category and the acquisition cost of a road asset in the desired road category (Equation (6)):

$$RCLS={\displaystyle \frac{{RC}_{cc}}{{AC}_{dc}}}\ast 100$$

(6)

where RCLS is the road category level of service index in %, RC_cc denotes the acquisition costs of the road asset in its current category in EUR, and AC_dc denotes the acquisition cost of road assets in the projected road category in EUR.

For community benefits, the value of the current category relative to the desired category can be quantified as the ratio of net community benefits provided by the road asset in its current category to net community benefits provided by the road asset in its desired category (Equation (7)):

$$\mathit{CB}={\displaystyle \frac{{\mathit{NCB}}_{\mathit{cc}}}{{\mathit{NCB}}_{\mathit{dc}}}}\ast 100$$

(7)

where CB is the community benefits index in %, NCB_cc is the net community benefits of the road asset in its current category in EUR, and NCB_dc is the net community benefits of the road asset in the desired road category in EUR.

3.5. Pavement Asset Condition Value

The calculation of the pavement asset condition value is based on both structural pavement condition and pavement serviceability. Structural pavement condition considers the pavement’s ability to withstand traffic loads, expressed as both the number of design axle passes it can sustain before reaching the failure threshold and its structural service life measured in years. Pavement serviceability measures the condition of pavement surface characteristics to ensure safe and economical vehicle operation in the pavement’s current state of deterioration.

3.6. Structural Pavement Condition Value

The structural pavement condition value is a financial metric calculated as the ratio of the current pavement’s structural life expectancy to the predicted life of a desired pavement, multiplied by the acquisition cost based on current prices (Equation (8)):

$$\mathit{SPCV}={\mathit{AC}}_{\mathit{NP}}\ast {\displaystyle \frac{\mathit{RSL}}{\mathit{DSL}}}$$

(8)

where SPCV is the structural pavement condition value, AC_NP is the acquisition cost based on present prices in EUR, RSL is the structural life expectancy of a current pavement in years, and DSL is the design life of the desired pavement in years.

Additionally, the structural pavement condition can be expressed as a ratio between the acquisition costs of the road asset in its current structural pavement condition and the acquisition cost of a road asset in the desired structural pavement condition (Equation (9)):

$$\mathit{SPCI}={\displaystyle \frac{{\mathit{RC}}_{\mathit{cspc}}}{{\mathit{AC}}_{\mathit{dspc}}}}\ast 100$$

(9)

where SPCI is the structural pavement condition index in %, RC_cspc is the acquisition costs of the road asset in its current structural pavement condition in EUR, and AC_dspc is the acquisition cost of a road asset in the desired structural pavement condition in EUR.

3.7. Pavement User Value

The pavement user value represents an economic measure of the value generated by the pavement asset through its ability to fulfil required operational functions for road users. This value is calculated as the ratio between the net road user benefits provided by the pavement in its current serviceability state and the net road user benefits provided by the pavement at its desired serviceability level. The pavement user value is quantified through the pavement user value index (Equation (10)):

$$\mathit{PUV}=\frac{{\mathit{NRUB}}_{\mathit{cs}}}{{\mathit{NRUB}}_{\mathit{dps}}}\ast 100$$

(10)

where PUV is the pavement user value index in %, NRUB_cs denotes net road user benefits of pavement in its current serviceability in EUR, and NRUB_dps denotes net road user benefits of pavement in the desired pavement serviceability in EUR.

3.8. Road Asset Value Calculation

The road asset value, shown in Equation (11), represents the total value of the road asset, accounting for both its current category and structural pavement condition value:

$$\mathit{RAV}={RC}_{CC}+\mathit{ACNP}\ast {\displaystyle \frac{RSL}{DSL}}$$

(11)

where RAV is the road asset value in EUR, RC_cc denotes the acquisition cost of the road asset in its current category in EUR, ACNP denotes the acquisition cost of pavement based on present prices in EUR, RSL is the structural life expectancy of the current pavement in years, and DSL is the design life of the desired pavement in years.

The capital investment required to achieve a desired road asset value depends on the rehabilitation or improvement costs needed to bring the road asset to its target performance and serviceability standards. When the existing road asset has salvage value, this amount can be deducted from the required investment. For example, if the road pavement retains sufficient bearing capacity, the following process applies: First, calculate its structural life expectancy; then design either an overlay or mill-and-replace rehabilitation to meet the desired service life requirements for new pavement. The overlay thickness design utilizes the structural life expectancy of both the road structure and its materials, enabling road authorities to optimize capital costs while achieving the target asset value.

4. Case Studies: Impact on Pavement Asset Value Modeling

Because pavement asset value is implemented in economic analyses and rehabilitation program optimization, it becomes necessary to analyze the effects of paving material quality and layer thickness on structural life extension and, consequently, the structural pavement condition value. It is equally important to analyze how different unevenness degradation functions affect the pavement user value.

4.1. Structural Life Extension

Residual structural life can be calculated based on stress analysis using the numerical elastic half-space model method along with Equations (2) and (3). Structural life extension can be modeled by adjusting rehabilitation parameters such as overlay thickness and material quality. The overlay material may match the existing surface material, with the required thickness calculated accordingly. Alternatively, using a material with a higher modulus of elasticity can extend the residual life. The structural pavement condition value, which considers alternative overlay thicknesses, different moduli of elasticity, residual life extension, and rehabilitation costs, is presented in

Table 6. Structural life extension in relation to the different overlay thicknesses.

Overlay Thickness	Complex Modulus of the Overlay Layer	Deformation on the Bottom Bound Mixture (µm/m)	Residual Lifecycle (DAL)	Residual Life Expectancy (Years)	Overlay Construction Cost EUR/m2	Structural Pavement Condition Value
0 mm	-	105.93	1,378,078.00	4	-	EUR 8.20
40 mm	5000	87.3	3,636,704.00	10	EUR 7.76	EUR 25.46
40 mm	6000	86.26	3,861,498.00	10	EUR 9.30	EUR 27.84
40 mm	7000	85.41	4,057,509.00	11	EUR 10.14	EUR 29.71
40 mm	8000	84.68	4,235,443.00	11	EUR 10.60	EUR 31.28
50 mm	5000	82.89	4,712,945.00	13	EUR 9.17	EUR 33.89
50 mm	6000	81.77	5,044,674.00	14	EUR 10.99	EUR 37.52
50 mm	7000	80.84	5,341,602.00	14	EUR 11.98	EUR 40.44
50 mm	8000	80.06	5,606,931.00	15	EUR 12.53	EUR 42.87
60 mm	5000	78.76	6,085,196.00	16	EUR 11.29	EUR 45.50
60 mm	6000	77.57	6,566,503.00	18	EUR 13.53	EUR 51.09
60 mm	7000	76.59	6,997,497.00	19	EUR 14.75	EUR 55.60
60 mm	8000	75.76	7,389,299.00	20	EUR 15.42	EUR 59.38

. These values were calculated using Equation (5) and represent relative values per square meter of the pavement. The current acquisition cost of the pavement structure was estimated at EUR 43.97 per m².

Figure 9 presents the analysis of total calculated deformation at the bottom edge of bound pavement layers. The results demonstrate a linear relationship between the complex modulus and deformation at the base course’s bottom edge.

The calculation of residual life after overlay application—determined via Equation (3)—and its dependency on the overlay’s complex modulus are shown in Figure 10.

As shown in Figure 10, material quality significantly impacts the structural pavement condition asset value. Using materials with a high complex modulus can increase residual service life by up to 30%, ultimately leading to a substantial improvement in the structural pavement condition asset value.

4.2. Unevenness Deterioration

From the perspective of pavement serviceability value, a decisive parameter is the decrease in pavement serviceability represented by the IRI, which leads to increased user costs. However, pavement serviceability decline is not linear and is, therefore, expressed through a polynomial degradation function. The resulting increase in user costs due to worsening IRI can be monetized using tools such as the Highway Development and Management program. Pavement rehabilitation improves IRI, generating benefits through reduced user costs. The relationship between road user benefits, degradation functions, and increased traffic loading is determined through Equation (11). The impact of diminishing benefits relative to worsening IRI parameters for semi-rigid arterial pavements, represented by degradation functions (shown in Figure 8) across different traffic intensities, is calculated in Figure 11. The unevenness parameter deteriorates according to applied design axle loads (DALs), with user benefits decreasing proportionally to unevenness. While the function shapes remain consistent for higher traffic loads, the benefit values vary as they depend on traffic intensity. By incorporating these values into Equation (7), a new road asset’s pavement serviceability value can be determined and evaluated using the pavement user value index (shown in

Table 7. Pavement User Value Index.

Year	2023	2024	2025	2026	2027	2028	2029	2030	2031	2032
User benefits (EUR)	6583	4959	3972	3434	3174	3040	2895	2619	2103	1254
Pavement User Value Index	100%	75%	60%	52%	48%	46%	44%	40%	32%	19%

).

Applying the user benefits evaluation principle to different unevenness deterioration functions and comparing functions from accelerated testing facilities with long-term monitoring sections (Figure 9) allows for the analysis of their varying impacts. The pavement user value index appears in

Table 8. Pavement user value index for different functions over time.

Function	APT Facility		LTPP Section 1		LTPP Section 2		LTPP Section 3
Year	User Benefits [EUR]	PUV [%]	User Benefits [EUR]	PUV [%]	User Benefits [EUR]	PUV [%]	User Benefits [EUR]	PUV [%]
2023	17,974.04	100%	17,950.67	100%	17,974.04	100%	17,974.04	100%
2024	13,464.06	75%	15,569.87	87%	15,168.47	84%	15,151.17	84%
2025	10,723.57	60%	13,932.58	78%	13,058.22	73%	13,213.93	74%
2026	9217.17	51%	12,750.76	71%	11,428.26	64%	11,859.15	66%
2027	8470.80	47%	11,770.81	66%	10,088.47	56%	10,819.24	60%
2028	8066.89	45%	10,770.77	60%	8871.59	49%	9859.34	55%
2029	7639.73	43%	9557.73	53%	7631.42	42%	8774.62	49%
2030	6871.21	38%	7965.36	44%	6241.04	35%	7387.77	41%
2031	5486.81	31%	5851.64	33%	4591.15	26%	5546.69	31%
2032	3251.90	18%	3096.74	17%	2588.61	14%	3122.25	17%
Total	91,166.18		109,216.93		97,641.27		103,708.20

, while Figure 12 displays the user benefit evaluation for different unevenness deterioration functions over time.

As shown in Table 7 and Table 8, the total value of user benefits and the pavement user value vary depending not only on traffic intensity but also on the degradation functions used in their calculation. The average total cumulative user benefits amount to EUR 100,433.15, calculated as the mean of user benefits derived from APT and three LTPP sections. Among the evaluated degradation functions, the minimum value reaches 91% (APT) of the average user benefits, while the maximum value reaches 109% (LTPP 1). This represents an 18% dispersion, which is statistically significant.

4.3. Rehabilitation Programs

The output of the decision-making processes (Figure 1) consists of pavement rehabilitation programs. By evaluating structural life extension, pavement unevenness deterioration, and pavement asset value, pavement rehabilitation designs can employ cost–benefit analysis (CBA) methods to optimize value for money through maximizing the net present value, internal rate of return, and benefit–cost ratio. However, achieving these parameters throughout the pavement lifecycle requires incorporating rehabilitation performance optimization methods into the schedule. This optimization considers overlay thickness designs based on structural evaluation, unevenness parameter degradation until reaching critical thresholds, cost factors, and user benefit calculations at different time intervals. The optimization can be performed using various methods grounded in economic analysis or through an optimization Index.

The economic analysis-based optimization strategy for rehabilitation program funding involves identifying combinations of rehabilitation methods with varying quality levels across different representative road sections to maximize the road network’s net present value during the reference period. As part of this optimization process, an economic analysis evaluates all alternatives across all sections and budget constraint scenarios. The optimization procedure itself utilizes integer linear programming methods through Equations (12) and (13):

$$MaxOPT\left[X_{sm}\right]=\sum _{s=1}^S\sum _{m=1}^{M_s}{OPT}_{sm}\times X_{sm}$$

(12)

where S is the road section (element of the road network matrix), M_s is the number of maintenance standards alternatives for road section s, m is the maintenance standard, OPT_sm is the optimization criterion (max NPV, max ΔIRI), sm is the combination of maintenance standard alternative m on road section s, and X_sm is a binary variable, where X_sm equals 1 if alternative m is chosen for section s, and X_sm equals 0 if alternative m is not chosen for section s.

$$\sum _{c=1}^c\sum _{m=1}^{m_s}{RZ}_{smt}\le {TR}_t$$

(13)

where RZ_smt is the funding demand for financing the road rehabilitation program in year t; TR_t is the undiscounted annual funding limit in year t, and t is the funding period.

The advantage of optimization is that the result is mathematically an absolute optimal solution. Optimization using the optimal index method is based on a mathematical model of costs and benefits over the pavement lifecycle (Equation (14)), applying rehabilitation methods at the times illustrated in Figure 13.

$$OI={\displaystyle \frac{{RC+NPAV}_{BR}+{NPAV}_{AR}}{T_t}}$$

(14)

where OI is the optimization index, RC denotes rehabilitation costs [EUR], NPAV_BR is the net present asset value before rehabilitation [EUR], NPAV_AR is the net present asset value after rehabilitation [EUR], and Tt is the extended lifecycle [EUR].

Comprehensive rehabilitation programs incorporate both pavement rehabilitation and road modernization programs, including category and width modifications. Equations (15) and (16) enable the determination of budget requirements, specifically, the allocation of limited funding for road sections:

$${TO}_b=max\left[{TS}_1\left({TO}_{1,s}\right);{TS}_2\left({TO}_{2,s}\right)\dots \dots \dots \dots \dots \dots {TS}_n\left({TO}_{n,s}\right)\right]$$

(15)

$$Price\left({TO}_b\right)\le b$$

(16)

where TO_b is the program action plan of rehabilitation actions on the road network, TS_i is the value of the performance criterion of asset “i”, derived from the technical condition of the asset and its max. values (see Section 3), and, for the case of implementing rehabilitation technology TO_i,s, TO_i,s is a variant of rehabilitation technology s on an asset i, price (TO_b,s) is the price of rehabilitation technology s on asset i; b is the budget limit for program i.

5. Discussion

Following the four research points outlined at the end of the previous chapter, we can make these additional observations:

• The practical example demonstrates that residual pavement life can be calculated in relation to traffic load and time horizon (years) using mathematical stress and deformation analysis of layered elastic half-space systems, FWD measurements of elastic deflection curves, and analytical back-calculation to determine the actual moduli of elasticity for the surface layer, subgrade, and base, combined with experimentally obtained fatigue characteristics of asphalt pavement materials. A key issue for further research is the relationship between fatigue and service life when comparing data from APT with real-world performance under actual outdoor climatic conditions.
• APT data, collected using precisely defined parameters and built-in measurement sensors with various scanners, enables mathematically derived degradation equations with high accuracy. However, since these functions were also developed from long-term road inventory measurements under real-world conditions, further research should refine them by incorporating climatic factors and pavement structure material characteristics.
• New pavement asset value calculation methods, incorporating well-defined structural life and unevenness deterioration parameters, have been applied in case studies and implemented in rehabilitation projects, yet these often fail to achieve the intended design life. Practical examples demonstrate that the achieved life extension is limited by permanent deformations caused by insufficient overlay thickness and substandard material quality.
• The case study highlights that service life extensions achieved through rehabilitation fall short of the designed service life due to permanent deformations caused by inadequate overlay thickness and material quality.
• The importance of well-defined degradation functions has been demonstrated through economic efficiency calculations, with their impact proving crucial for determining road user benefits. Consequently, optimal resource allocation and accurate funding requirements cannot be achieved without reliable degradation functions combined with proper pavement structure service life calculations.

To successfully implement these research findings in practice, road managers must establish the necessary framework for applying these complex methodologies. This requires three key actions: first, clearly defining both current and long-term requirements from society and road infrastructure users; second, developing a comprehensive road inventory system with regular asset condition monitoring and control protocols; and third, creating specialized material testing equipment and calculation methods to assess fatigue characteristics of common pavement materials, which are essential for determining bearing capacity and predicting structural life expectancy.

In relation to future research aimed at improving deterioration models, it is necessary to consider the ongoing evolution of pavement materials and technologies. As these advancements continue, models must be updated to reflect new forms of degradation. The increasing use of recycled constituents—such as reclaimed asphalt pavement, crumb rubber, plastic waste, and used cooking oil—alters the viscoelastic properties and aging characteristics of pavement materials, thereby influencing their long-term performance [67,68]. At the same time, the integration of autonomous repair technologies, including induction heating systems, microencapsulated rejuvenators, and vascular healing networks, is actively changing the progression of damage by enabling in-situ healing processes [69,70].

These developments introduce new material behaviors and damage mechanisms that conventional phenomenological models are not equipped to capture. Consequently, there is a growing need for next-generation deterioration frameworks that incorporate micromechanical interactions and account for the time-dependent evolution of pavement properties under realistic service conditions.

6. Conclusions

The pavement service life decision process presented in this paper was developed in response to road managers’ needs, as current standard decision-making methods, pavement management systems, and road asset valuation approaches failed to deliver sufficient results. Consequently, this new decision-making process was designed to maximize pavement service life through optimal financial planning. While building upon traditional methods, this comprehensive approach prioritizes structural life extension derived from structural life expectancy calculations and the resulting reinforcement thickness requirements. Additionally, it incorporates rehabilitation method designs based on degradation models, economic analyses, and project prioritization.

Implementing this methodology required extensive research in four critical areas:

• Residual Life Calculation Methodology: Developing techniques to compute pavement structure residual life using numerical stress analysis and deformation modeling in layered elastic half-space systems, combined with experimental measurements of rehabilitation materials’ fatigue parameters.
• Unevenness Deterioration Modeling: Establishing degradation models through data from newly constructed experimental facilities and mathematical models derived from 20 years of road inventory measurements.
• Pavement Asset Valuation Formulas: Creating computational frameworks that incorporate residual life and time-dependent user costs, with benefit calculations based on degradation models.
• Decision Process Implementation: Integrating these methods into a comprehensive model that quantifies how material quality, dimensional specifications, and structural life extension affect structural pavement condition value, while mathematically representing how unevenness degradation impacts pavement user value.

This study introduces a comprehensive and integrative decision-making model for pavement rehabilitation that uniquely combines structural life expectancy, serviceability deterioration, and asset value evaluation within a single framework. Unlike traditional pavement management approaches that often treat these components separately, the presented model quantifies their interdependence using analytical equations grounded in fatigue theory, pavement performance modeling, and cost–benefit principles.