AC pavement life cycle cost and carbon emission prediction method based on fatigue curve

Abstract

The invention discloses an AC pavement full-life cost and carbon emission prediction method based on a fatigue curve, and the method comprises the steps: modeling an asphalt pavement structure of a structure A and a structure B, and enabling the structure A and the structure B to be different in surface layer material and surface layer thickness; the structure A adopts a surface layer material with a known PQI exponential decay rule, and the structure B adopts a novel surface layer material to be calculated; respectively carrying out fatigue tests on surface layer materials of the structure A and the structure B to obtain respective fatigue curves; performing PQI-damage curve fitting on the structure A to obtain fitting parameters; modeling through fatigue index migration, predicting a PQI curve of the structure B, and determining a maintenance scheme of the structure B according to the PQI index of the structure B; and calculating the whole life cycle cost and carbon emission of the structure B. According to the method, the repair time interval during the road use period can be quantitatively predicted, so that the whole life cycle cost and carbon emission of the road structure are calculated, and the adaptability evaluation efficiency of the new material pavement structure in the early stage is improved.

Description

Technical Field

[0001] This invention belongs to the field of road pavement structure design technology, and relates to a method for predicting the life-cycle cost and carbon emissions of AC pavement based on fatigue curves. Background Technology

[0002] Calculating the life-cycle cost of asphalt pavement structures can predict the construction and maintenance costs, as well as the potential repair frequency, for different pavement structures, helping decision-makers to better prepare budgets and allocate resources. It can also predict the technical condition of the pavement at different points in time, thus providing a basis for maintenance decisions. This helps ensure that the pavement maintains optimal performance throughout its service life, reducing over-maintenance and unnecessary costs. By calculating carbon emissions at each stage, the environmental impact of pavement construction and maintenance can be more comprehensively assessed. This provides environmentally friendly optimization solutions for road construction, meeting the requirements of green transportation and sustainable development.

[0003] In the process of realizing this invention, the inventors discovered at least the following problems in the prior art:

[0004] 1. Limitations of previous methods for evaluating pavement structure during the design phase;

[0005] (1) Focus on initial construction costs: Previous designs focused more on low cost and compliance with specifications, while neglecting long-term maintenance and operation costs.

[0006] (2) Ignoring the economic and environmental impact of the entire life cycle: The carbon emissions caused by frequent repairs and maintenance in the later stage were not fully considered during the design phase.

[0007] 2. The maintenance of roads during their service life is often determined by empirical methods, but these methods cannot be applied to new pavement structures such as long-life pavement structures.

[0008] (1) The analysis of the pavement structure of the control group is based on historical performance data and existing maintenance experience. Through the known PQI attenuation curve and axle load history, the typical maintenance time nodes and performance degradation trend can be estimated relatively accurately.

[0009] (2) Lack of experience support for new pavement structures: The design of new pavement structures is still in the research stage, the number of related projects in China is limited, and the actual service life is short. It is difficult to predict the maintenance needs and service performance of new pavement structures through experience.

[0010] 3. Existing pavement structure assessment methods cannot be used in the design phase of new pavement structures;

[0011] (1) Existing evaluation standards are applicable to service period testing: For example, the "Highway Technical Condition Evaluation Standard" is mainly based on data such as the actual ruts, smoothness, and skid resistance of the road, and comprehensively obtains the PQI index for condition assessment.

[0012] (2) Lack of analysis on carbon emissions and energy consumption: Existing assessment standards fail to cover environmental protection goals, and relevant analysis methods need to be introduced in the design phase. Summary of the Invention

[0013] To address the aforementioned issues, this invention provides a method for predicting the life-cycle cost and carbon emissions of AC pavement based on fatigue curves. This method can quantify and predict the maintenance intervals during road use, thereby calculating the life-cycle cost and carbon emissions of the road structure and improving the efficiency of adaptability evaluation of new material pavement structures in the early stages.

[0014] The technical solution adopted in this invention is a method for predicting the life-cycle cost and carbon emissions of AC pavement based on fatigue curves, comprising the following steps:

[0015] Step 1: Model the asphalt pavement structures of Structure A and Structure B. The only difference between Structure A and Structure B is the surface layer material and surface layer thickness. Structure A uses a surface layer material with a known PQI exponential decay law, while Structure B uses a new surface layer material to be calculated.

[0016] Step 2: Conduct fatigue tests on the surface materials of structures A and B respectively, obtain their respective fatigue curves, and thus obtain the fatigue life N of the surface material of structure A under stress σ. f (A) The fatigue life N of the surface material of structure B under stress σ f (B) ;

[0017] Step 3: Fit the PQI-damage curve to structure A to obtain the fitting parameters;

[0018] Step 4: Based on the fitting parameters obtained in Step 3, predict the PQI curve of structure B through fatigue index transfer modeling, and determine the maintenance plan of structure B according to the PQI index of structure B, including the number of minor repairs, medium repairs and major repairs.

[0019] Step 5: Based on the number of minor, medium and major repairs determined in Step 4, calculate the total life cycle cost and carbon emissions of Structure B.

[0020] Furthermore, step three includes:

[0021] (1) Miner cumulative damage:

[0022]

[0023] DA (t) represents the cumulative Miner damage in year t; N i The number of axle load repetitions in year i; t is the service life;

[0024] (3) Curve fitting of PQI and Miner damage:

[0025] PQI A (t)=α·D A (t) β (2)

[0026] PQI A (t) represents the performance index in year t; α and β are fitting parameters obtained through nonlinear least squares fitting.

[0027] Furthermore, in step four, a minor repair is performed when the PQI index is below 90, a medium repair is performed when it is below 80, and a major repair is performed when it is below 70.

[0028] Furthermore, in step five, the formula for calculating the total life cycle cost of structure B is:

[0029] LCC = C0 + C b ×N b +C m ×N m +C r ×N r (3)

[0030] Where: LCC: Life Cycle Cost; C0: Initial Construction Cost; C b The cost required for a single major overhaul; N b : Analyze the number of major overhauls within the service life; C m The cost required for a single intermediate repair; N m : Analysis of the number of intermediate repairs within the service life; C r The cost required for a single minor repair; N r : Analyze the number of minor repairs within the specified years.

[0031] Furthermore, in step five, the formula for calculating carbon emissions is shown in (4):

[0032] CE = E0 + E b ×N b +E m ×N m +E r ×N r (4)

[0033] Where CE: total life-cycle carbon emissions; E0: initial construction carbon emissions; E b Carbon emissions per major overhaul; N b: Analysis of the number of major overhauls within the service life; E m Carbon emissions from a single mid-term overhaul; N m : Analysis of the number of intermediate repairs within the service life; E r Carbon emissions per minor repair; N r : Analyze the number of minor repairs within the specified years.

[0034] Furthermore, the method for determining the initial construction carbon emissions E0 is as follows:

[0035] Determine the types, quantities, and unit carbon emissions of raw materials and machinery required in the five stages of initial construction: raw material production, asphalt mixture production, transportation, paving, and compaction. Multiply the quantity of each type by the unit carbon emission to obtain the carbon emissions of different types of raw materials and machinery. Add the carbon emissions of different types of raw materials and machinery together to obtain the final carbon emission.

[0036] Carbon emissions from a single overhaul (E) b The determination method is as follows: determine the types, quantities, and unit carbon emissions of raw materials and machinery required for a single major overhaul in the five stages of raw material production, asphalt mixture production, transportation, paving, and compaction; multiply the quantity of each type by the unit carbon emission to obtain the carbon emission of different types of raw materials and machinery, and add the carbon emission of different types of raw materials and machinery together to obtain the result.

[0037] Carbon emissions E of a single mid-term overhaul m The determination method is as follows: determine the types, quantities, and unit carbon emissions of raw materials and machinery required for a single intermediate repair in the five stages of raw material production, asphalt mixture production, transportation, paving, and compaction; multiply the quantity of each type by the unit carbon emission to obtain the carbon emission of different types of raw materials and machinery, and add the carbon emission of different types of raw materials and machinery together to obtain the result.

[0038] Carbon emissions per minor overhaul E r The determination method is as follows: determine the types, quantities, and unit carbon emissions of raw materials and machinery required for a single minor repair in the five stages of raw material production, asphalt mixture production, transportation, paving, and compaction; multiply the quantity of each type by the unit carbon emission to obtain the carbon emission of different types of raw materials and machinery, and add the carbon emission of different types of raw materials and machinery together to obtain the result.

[0039] The beneficial effects of this invention are:

[0040] 1. This invention establishes a migration prediction model based on fatigue curves and the Performance Quality Index (PQI) index by combining laboratory fatigue test data with actual road service data. Using historical PQI decay data and axle load variations from a typical control pavement structure, a mathematical mapping relationship between the performance index and fatigue damage is constructed. This relationship is then applied to novel structures with only experimental fatigue curves available, enabling prediction of their service life and maintenance requirements even in the absence of long-term measured data. Compared to traditional statistical models relying on measured years of data, this method is more physically reasonable and has greater transferability, significantly improving the efficiency of adaptability evaluation for new material pavement structures in the early stages.

[0041] 2. This invention employs a fatigue-characteristic-driven modeling approach, utilizing Miner's damage theory to transform loading cycles into equivalent damage, and employing an exponential mapping model to achieve continuous PQI prediction, thereby assessing the temporal trend of pavement technical condition. This model has a simple structure, highly interpretable parameters, and is suitable for lateral comparisons of various pavement structures. Furthermore, the prediction results can be used for quantitative assessment of life-cycle maintenance costs and carbon emissions, providing a scientific basis for green road design. Compared to empirical fitting or black-box prediction methods, this invention possesses stronger physical traceability and engineering scalability.

[0042] 3. Based on the predicted PQI index, this invention can rationally plan the timing of minor, medium and major repairs, avoid over-maintenance and under-maintenance, improve resource utilization efficiency and reduce overall maintenance costs. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart illustrating an embodiment of the present invention.

[0045] Figure 2a This is a flowchart for determining the comparison structure in an embodiment of the present invention.

[0046] Figure 2b yes Figure 2a The horizontal tensile stress curve of the middle structure A along the driving direction (x).

[0047] Figure 2c yes Figure 2a The horizontal tensile stress curve of the middle structure B along the driving direction (x).

[0048] Figure 3This is a diagram showing the placement of the specimen in an embodiment of the present invention.

[0049] Figure 4 These are fatigue curves for two structures obtained from embodiments of the present invention.

[0050] Figure 5 This is a schematic diagram showing the cost and carbon emissions of the two structures within different analysis periods, derived from an embodiment of the present invention.

[0051] Figure 6 This is a schematic diagram illustrating carbon emissions generated at different stages, derived from an embodiment of the present invention.

[0052] Figure 7 These are schematic diagrams of the four structures analyzed in the embodiments of this invention.

[0053] Figure 8 This is a schematic diagram showing the cost and carbon emissions of four structures within different analysis periods, derived from an implementation case of the present invention.

[0054] Figure 9 This is a schematic diagram illustrating carbon emissions generated at different stages, derived from an embodiment of the present invention. Detailed Implementation

[0055] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0056] Example: A method for predicting the life-cycle cost and carbon emissions of AC pavement based on fatigue curves, such as... Figure 1 As shown, it includes the following steps:

[0057] Step 1: Under the premise that all other conditions are consistent (such as temperature, humidity, traffic flow, area, etc.), ensure that the horizontal tensile stress of the lower layer of the pavement is the same along the driving direction. Modeling software (MPave, Bisar, Daokedao, HPDS, etc.) is used to create a model, determining the control group structure (Structure A). Some modeling parameters can be found in Table 1, with data from the "Specifications for Design of Highway Asphalt Pavement" (JTG 050-2017). The base and subgrade materials of Structure A and Structure B are consistent, differing only in the surface layer material and thickness. Structure A uses a known surface layer material (such as AC-13), representing a typical pavement structure widely used today. Its fatigue life has been verified through long-term practice, possessing mature design, construction, and maintenance experience, and rich historical performance data (such as fatigue curves, PQI index decay laws, etc.), serving as a reliable benchmark for performance evaluation. Structure B selects new materials based on specific needs, aiming to evaluate its advantages in terms of life-cycle cost, carbon emissions, durability, and maintenance requirements by comparing it with Structure A. Using structure A as a reference helps to conduct in-depth analysis and objective evaluation of the comprehensive performance of the new structure.

[0058] Table 1 Reference Values ​​for Asphalt Mixture Design Parameters

[0059]

[0060] Note: In Table 1, ATB25 is the dynamic compression modulus under 5Hz conditions, while other asphalt mixtures are the dynamic compression modulus under 10Hz conditions. Higher values ​​are used for asphalt with high density, good gradation, or low porosity, and vice versa.

[0061] Step 2: Conduct fatigue tests on the new pavement structure (Structure B) and the control pavement structure (Structure A) respectively, and obtain their respective fatigue curves;

[0062] The fatigue test adopted the standard three-point or four-point bending fatigue test. Using the calculation formula (1), the fatigue curves of the control pavement structure (structure A) and the new pavement structure (structure B) were obtained, where N f For fatigue life, A and n are experimental parameters, σ represents the stress level (MPa), and the fatigue curves of structures A and B are obtained from formula (1). Therefore, the fatigue lives of structures A and B are respectively expressed in N. f (A) N f (B) Express.

[0063] N f =Aσ -n (1)

[0064] Step 3: Fitting the PQI-damage curve for structure A;

[0065] (1) Miner cumulative damage:

[0066]

[0067] D A (t) represents the cumulative Miner damage in year t; N i N represents the number of axle load repetitions in year i (in times); f (A) σ represents the fatigue life of material A under stress σ (determined by its fatigue curve); t represents the service life (years).

[0068] (4) Damage relationship between PQI and Miner (exponential fitting):

[0069] PQI A (t)=α·D A (t) β (3)

[0070] PQI A (t) represents the performance index in year t; α and β are fitting parameters obtained through nonlinear least squares fitting.

[0071] Table 2 Examples of Common PQI

[0072]

[0073] Note: The data in Table 2 are from the literature (Li Hailian, Yang Siyuan, Qi Zengtao, et al. Prediction of asphalt pavement service performance based on rough set theory and PCA-APSO-SVM [J / OL]. Journal of Chongqing Jiaotong University (Natural Science Edition): 1-8 [2024-05-26]. http: / / kns.cnki.net / kcms / detail / 50.1190.U.20240408.1615.002.html; Han Jinchuan, Zhuang Fengming, Wang Lianxiang, et al. Prediction method of pavement service performance based on equal-dimensional grey number supplementary model method [J]. Journal of Hebei University of Water Resources and Electric Power, 2023, 33(03): 59-64. DOI:10.16046 / j.cnki.issn2096-5680.2023.03.010). Table 2 plays a role in the calculation of D in this invention. A (t) together determine α and β.

[0074] Step 4: Based on the fitting parameters obtained in Step 3, predict the PQI curve of structure B through fatigue index transfer modeling, and thus determine the maintenance plan for structure B.

[0075] (1) Miner cumulative damage:

[0076]

[0077] D B (t) represents the cumulative Miner damage in year t; N i The number of axle load repetitions in year i (unit: times); σ represents the fatigue life of material B under stress σ (determined by its fatigue curve); t represents the service life (years).

[0078] (2) Using transfer mapping to predict PQI B :

[0079] PQI B (t)=α·D B (t) β (5)

[0080] PQI B (t) represents the performance index in year t; α and β are the fitting parameters obtained in step two through nonlinear least squares fitting.

[0081] The essence of the exponential curve migration method is to "preserve the form of the exponential function and change the scale of the horizontal axis". In structure A, PQI is the Miner damage function; in structure B, the fatigue life is different and the damage is different each year, so the PQI curve is "stretched" or "compressed" on the time axis; however, the embodiment of the present invention does not change the shape of the function, but only replaces the input variables to achieve prediction without measured data, thus maintaining interpretability and physical consistency.

[0082] (3) PQI index prediction and maintenance plan decision-making;

[0083] Based on the PQI index predicted in step four, and referring to the "Highway Technical Condition Assessment Standard" JTG5210-2018, minor repairs are required when the PQI index is below 90, medium repairs below 80, and major repairs below 70. Based on this prediction, the future maintenance plan and repair intervals for the new pavement structure are determined.

[0084] Formula (5) can predict the relationship between PQI and the year, and then predict the years in which the PQI value will be 90, 80, 70 and so on, to determine the repair interval.

[0085] Maintenance plan development: Based on the predicted PQI index and set thresholds (such as 90, 80, 70), a scientific maintenance plan can be developed. When the PQI index drops to a certain level, the timing of maintenance is determined based on experience or historical data. The prediction results help reduce the risk of over- or under-maintenance.

[0086] Step 5: Based on the number of minor, medium and major repairs determined in Step 4, calculate the life cycle cost and carbon emissions of the new pavement structure according to Equation (6).

[0087] 1) Life cycle cost calculation:

[0088] The formulas for calculating the life-cycle cost of structures A and B are as follows:

[0089] LCC = C0 + C b ×N b +C m ×N m +C r ×N r (6)

[0090] Where: LCC: Life Cycle Cost; C0: Initial Construction Cost; C b The cost required for a single major overhaul; N b : Analyze the number of major overhauls within the service life; C m The cost required for a single intermediate repair; N m : Analysis of the number of intermediate repairs within the service life; C r The cost required for a single minor repair; N r : Analyze the number of minor repairs within the specified years.

[0091] C t =C M +C L +C E +C T +C I ′ (7)

[0092] C0, C b C m C r The general formula for calculating the cost per transaction is shown in equation (7). C M Material costs; C L Labor costs; C E : Cost of using mechanical equipment; C T Transportation costs; C I Other indirect costs (such as project management fees, temporary facility fees, regulatory fees, insurance, safety and civilized construction fees, etc.).

[0093] When t = 0, C t =C0, C on the right side of the equation M C L C E C T C I ′ represent the material costs, labor costs, machinery and equipment usage costs, transportation costs, and other indirect costs during the initial construction phase, respectively. Similarly, when t = b (major repair time), m (medium repair time), and r (minor repair time), the cost items C in formula (8) are calculated as follows. M C L C E CT C I These correspond to material costs, labor costs, machinery usage fees, transportation costs, and other indirect costs during the major, intermediate, or minor repair stages, respectively.

[0094] 2) Calculation of carbon emissions over the entire life cycle;

[0095] Carbon emissions are calculated using a factor method, where QC n c represents the carbon dioxide equivalent (kg) for the nth stage of asphalt pavement construction. i The CO2 emission factor for the target energy source is calculated using equation (8) based on data provided in the IPCC Fourth Assessment Report and the energy section of the China Energy Statistical Yearbook:

[0096] CE = E0 + E b ×N b +E m ×N m +E r ×N r (8)

[0097] Where: CE: Carbon emissions over the entire life cycle; E0: Carbon emissions during initial construction; E b Carbon emissions per major overhaul; N b : Analysis of the number of major overhauls within the service life; E m Carbon emissions from a single mid-term overhaul; N m : Analysis of the number of intermediate repairs within the service life; E r Carbon emissions per minor repair; N r : Analyze the number of minor repairs within the specified years.

[0098] E0, E b E m E r The determination method is the same, and the carbon emissions for a single maintenance are calculated according to formula (9). When t = 0, E t =E0 represents the carbon emissions during the initial construction phase; similarly, when t = b (major overhaul time), m (intermediate overhaul time), and r (minor overhaul time), they correspond to the carbon emissions during the major overhaul, intermediate overhaul, and minor overhaul phases, respectively.

[0099] E t =∑QC n (9)

[0100] QC n =∑(m i ×c i (10)

[0101] QC in formula (10) ndenoted by m, where n represents the five stages, including raw material production, asphalt mixture production, transportation, paving, and compaction. i This represents the consumption of various energy sources (specifically gasoline, diesel, coal, heavy oil, and electricity), primarily generated by the use of machinery during construction, such as fuel used in transportation and electricity used in raw material production. i The carbon emission coefficient representing each energy source, c i The standard formula is obtained from equation (11), but most c i The coefficients can be found in the IPCC (Intergovernmental Panel on Climate Change) database, some of which are c i The coefficients are shown in Table 3.

[0102] c i =NCV×CC×COF×44 / 12 (11)

[0103] Where: c i : Emission Factor (kg CO2 / kg fuel); NCV: Net Calorific Value (kJ / kg); CC: Carbon Content per Unit Heat (kg C / kJ); COF: Carbon Oxidation Factor (dimensionless, usually 0.98-1); 44 / 12: Molar mass ratio of carbon to carbon dioxide (44 is the molar mass of CO2, 12 is the molar mass of carbon).

[0104] Table 3 Carbon Emission Coefficients per Unit Mass of Energy

[0105] Gasoline (kg / L) Diesel fuel (kg / L) Coal consumption (kg / kg) Heavy oil (kg / kg) Electricity / (kg / (kw·h)) 2.925 3.096 2.852 3.020 0.714

[0106] The relationship between cost and carbon emissions:

[0107] There is a direct link between carbon emissions and costs because most road construction and maintenance activities (such as construction, transportation, and machinery use) require energy consumption, which in turn generates carbon emissions.

[0108] Construction phase: The materials (such as asphalt mixtures) and construction equipment (such as road rollers and transport vehicles) used in the construction of new pavement structures consume energy and generate carbon emissions, which directly increases the initial construction costs.

[0109] Maintenance phase: Carbon emissions are generated during road maintenance, especially during medium and minor repairs, involving the use of machinery and the transportation of materials. These emissions increase the carbon cost of each maintenance session.

[0110] Costs include all economic costs (such as the construction and maintenance costs mentioned above) as well as carbon costs arising from carbon emissions. In a life-cycle analysis, carbon costs are calculated through life cycle assessment (LCA), which reflects the environmental impact of carbon emissions at each stage, from raw material production to construction and maintenance. This impact is not merely a "hidden" cost, but may also lead to social and legal liabilities (such as carbon taxes or environmental compensation costs).

[0111] In conclusion, cost and carbon emissions are closely related, as more energy consumption and more equipment use will lead to more carbon emissions.

[0112] Calculate the carbon emissions of each stage of the new pavement structure and identify the main sources of carbon emissions; calculate the stage according to the five stages of raw material production, asphalt mixture production, transportation, paving and compaction using formula (10), and propose suggestions for improving the new pavement structure with lower cost and lower carbon based on the calculation results.

[0113] In the embodiments of the present invention, the "novel asphalt pavement structure" refers to an asphalt pavement structure that is in the experimental stage and has not been used on the road, such as a long-life pavement structure or a recycled modified pavement structure in the experimental stage. The method of the embodiments of the present invention can predict carbon emissions and costs within different analysis years.

[0114] Implementation Case:

[0115] Step 1: As Figures 2a-2c As shown, under the premise of maintaining consistent external conditions such as temperature, humidity, traffic load, and geographical area, to ensure that the horizontal tensile stress of each layer of the pavement structure in the driving direction is basically equal, the implementation case uses MPAVE software for structural modeling and stress verification. Other modeling software (Bisar, Daokedao, HPDS, etc.) can also be used for modeling, thereby determining the control group structure A. Structure A uses a typical asphalt pavement structure widely used at present, with its surface layer material being AC-13SBS modified asphalt mixture. It has mature engineering experience and complete performance data, making it suitable as a reference benchmark. Structure B is the novel pavement structure scheme proposed in this invention. Structure B is consistent with Structure A in terms of base course and subgrade material composition and thickness; the difference lies in the surface layer design. The surface layer of Structure B consists of a 3cm thick AC-13 asphalt mixture on top and a 1cm thick ultra-high performance concrete (UHPC) layer on the bottom, with a structural connection between the two through a crushed stone bonding layer. The method described in this invention enables quantitative analysis of different pavement layer combinations during the pavement structure design phase, systematically evaluating their economic costs, durability, and environmental impact throughout their entire life cycle. This method helps construct a performance- and sustainability-based pavement design decision framework, providing a scientific basis for selecting the optimal structure.

[0116] Step Two: Placement of Indoor Fatigue Test Specimens Figure 3 As shown, the positions of the supports and mid-span were marked on a standard 250mm×40mm×40mm beam specimen with a span of 200mm. The specimen was placed in a 15℃ environmental chamber for at least 4 hours. Then, following the principle of aligning the supports with the marked lines and ensuring the specimen's orientation matches its forming direction, it was placed on the supports of the UTM testing machine. To accelerate the fatigue process and test the material's performance under extreme conditions, the loading interval was not considered in this experiment, ensuring the beam specimen remained in the most unfavorable fatigue failure state. A half-sine waveform was selected as the loading waveform, and the loading frequency was set to 10Hz. The final fatigue curve is shown below. Figure 4 As shown.

[0117] Step 3: Based on the fatigue curve of the new pavement structure obtained in Step 2 and the historical PQI index and axle load data of the control structure in Step 2, this embodiment of the invention realizes the prediction of the PQI index of structure B within its service life by constructing an index mapping relationship between PQI and Miner cumulative damage.

[0118] Step 4: Based on the fitting parameters obtained in Step 3, the PQI curve of structure B is predicted through fatigue index transfer modeling, thereby determining the maintenance scheme for structure B; and then the typical maintenance timing and repair interval can be calculated accordingly, as shown in Table 4.

[0119] Table 4. Repair time intervals predicted for different structures in embodiments of the present invention.

[0120]

[0121] Step 5: Calculate the life-cycle cost and carbon emissions of the new pavement structure. The results are as follows: Figure 5 As shown, this figure illustrates the costs (including initial construction costs and subsequent maintenance costs) and carbon emissions (including initial construction carbon emissions and subsequent maintenance carbon emissions) for different analysis periods and structures. This figure allows decision-makers to analyze the carbon emissions and costs of each structure over different service lives, providing a reference for decision-making. With increasing analysis periods, pavement structure B exhibits more significant LCA economic benefits and energy-saving and emission-reduction effects, demonstrating a significant cost advantage, especially in the 50-year and 60-year cycles where cumulative savings are substantial. Pavement structure A serves as the control group. With increasing analysis periods, utilizing minor, medium, and even major repairs to extend the road's service life, the maintenance costs and carbon emissions over 30 years are 2.19 times and 0.71 times those of the initial construction period, respectively. Over 60 years, the maintenance costs and carbon emissions are 11.93 times and 2.12 times those of the initial construction period, respectively.

[0122] Table 5 Summary of carbon emissions during the raw material production stage of Structure B in the embodiments of the present invention

[0123]

[0124] Figure 6 This indicates that over 50% of carbon emissions originate in the raw material production stage, with transportation being the second largest contributor, each accounting for approximately 24% of the total carbon emissions for their respective maintenance projects. While minor repairs use fewer raw materials than medium-sized repairs, the reduction in machinery usage during transportation is limited. Therefore, the proportion of carbon emissions from the transportation stage increases significantly during minor repairs. This is because every 1000m of minor repair work... 2 The generated carbon emissions were 4186.26 kg CO2, only 4.46% of the total initial construction carbon emissions. Therefore, this does not affect the long-term carbon emission contribution of each stage. While the total carbon emissions increase with the increase in the analysis period, the carbon emissions of each stage increase proportionally. Since the emission reduction potential of raw materials is limited, and the main emissions come from mechanical operation, it is recommended to improve the carbon reduction efficiency of mechanical equipment and rationally plan transportation distances to reduce carbon emissions. Furthermore, priority should be given to low-emission or electrified mechanical equipment, optimizing the transportation routes of construction and maintenance materials, choosing low-carbon transportation methods, and reducing transportation frequency and distance. In material selection, low-carbon or renewable materials should be used as much as possible to reduce carbon emissions at the source; at the same time, carbon emission targets should be set at different maintenance stages to gradually reduce the environmental impact, thereby promoting the effective implementation of the LCA optimization strategy.

[0125] This invention combines indoor fatigue testing with the prediction of on-site maintenance needs: Traditional fatigue life prediction often relies on external environmental data or historical experience. Many technicians may over-rely on historical data or external conditions, neglecting the application of indoor test data, especially in environments with high uncertainty. This invention, by combining refined test data with regression methods, establishes the relationship between the fatigue curve and the PQI index, and reverse-engineers future maintenance needs. By combining indoor test data with long-term maintenance prediction, it overcomes the limitations of relying on external data, breaks through traditional thinking, and brings more accurate predictive capabilities.

[0126] Using the method of this invention, four types of surface layer structures, including structure B, were analyzed. The surface layer structure types are as follows: Figure 7 As shown, the base structure remains consistent. The results are as follows: Figure 8 , Figure 9As shown, this invention can be used to evaluate the cost distribution characteristics of different pavement structures throughout their entire life cycle, including initial construction investment and long-term operation and maintenance costs, thereby comprehensively measuring their economic viability. Simultaneously, by combining carbon emission factors and time points (such as 30 years, 50 years, and 60 years), the environmental impact of each structure can be quantified, providing scientific carbon reduction assessment results. Furthermore, embodiments of this invention are applicable to analyzing the engineering adaptability and performance of novel surface materials (such as ultra-high performance concrete, UHPC) in pavement structures, providing a theoretical basis for their widespread application. This method can also serve as an auxiliary decision-making tool, promoting a shift in pavement design concepts from simple initial cost control to a life-cycle performance orientation, achieving optimal comprehensive benefits in engineering construction.

[0127] While indoor fatigue testing can simulate various working conditions, translating these laboratory results into practical fatigue life and maintenance requirements remains a technical challenge. Although indoor fatigue testing can simulate the fatigue behavior of materials under different loads and stresses, the idealized conditions differ significantly from the actual road environment under various complex factors, making it difficult to directly use the test results to predict actual service life and maintenance requirements. To address this technical challenge, this invention proposes a fatigue performance-driven pavement performance migration prediction method. This method calculates the equivalent fatigue damage of materials under measured axle load histories to characterize the performance degradation process of structures under real road service conditions. Subsequently, by combining actual PQI degradation data of existing typical structures, an empirical mapping relationship between performance and damage is established. Based on this, this invention enables the prediction of PQI trends and potential maintenance cycles of novel materials under actual conditions, based solely on indoor fatigue test data, resulting in dynamic prediction results. This effectively transforms indoor test results into practical indicators applicable to engineering decisions, demonstrating good adaptability and promotional value. The embodiments of the present invention improve the adaptability and accuracy of prediction: by using fatigue data obtained from indoor tests, the model can more accurately reflect the road surface performance under different working conditions, ensuring the scientific nature and real-time performance of the solution.

[0128] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A method for predicting the life-cycle cost and carbon emissions of AC pavement based on fatigue curves, characterized in that, Includes the following steps: Step 1: Model the asphalt pavement structures of Structure A and Structure B. The only difference between Structure A and Structure B is the surface layer material and surface layer thickness. Structure A uses a surface material with a known PQI exponential decay law, while structure B uses a novel surface material whose calculation is yet to be performed. Step 2: Conduct fatigue tests on the surface materials of structures A and B respectively, obtain their respective fatigue curves, and thus obtain the fatigue life N of the surface material of structure A under stress σ. f (A) The fatigue life N of the surface material of structure B under stress σ f (B) ; Step 3: Fit the PQI-damage curve to structure A to obtain the fitting parameters; Step 4: Based on the fitting parameters obtained in Step 3, predict the PQI curve of structure B through fatigue index transfer modeling, and determine the maintenance plan of structure B according to the PQI index of structure B, including the number of minor repairs, medium repairs and major repairs. Step 5: Based on the number of minor, medium and major repairs determined in Step 4, calculate the total life cycle cost and carbon emissions of Structure B.

2. The method for predicting the life-cycle cost and carbon emissions of AC pavement based on fatigue curves according to claim 1, characterized in that, Step three includes: (1) Miner cumulative damage: D A (t) represents the cumulative Miner damage in year t; N i The number of axle load repetitions in year i; t is the service life; (2) Curve fitting of PQI and Miner damage: PQI A (t)=α·D A (t) β (2) PQI A (t) represents the performance index in year t; α and β are fitting parameters obtained through nonlinear least squares fitting.

3. The method for predicting the life-cycle cost and carbon emissions of AC pavement based on fatigue curves according to claim 1, characterized in that, In step four, a minor repair is performed when the PQI index is below 90, a medium repair is performed when it is below 80, and a major repair is performed when it is below 70.

4. The method for predicting the life-cycle cost and carbon emissions of AC pavement based on fatigue curves according to claim 1, characterized in that, In step five, the formula for calculating the life-cycle cost of structure B is as follows: LCC=C0+C b ×N b +C m ×N m +C r ×N r (3) Where: LCC: Life Cycle Cost; C0: Initial Construction Cost; C b The cost required for a single major overhaul; N b : Analyze the number of major overhauls within the service life; C m The cost required for a single intermediate repair; N m : Analysis of the number of intermediate repairs within the service life; C r The cost required for a single minor repair; N r : Analyze the number of minor repairs within the specified years.

5. The method for predicting the life-cycle cost and carbon emissions of AC pavement based on fatigue curves according to claim 1, characterized in that, In step five, the formula for calculating total carbon emissions is shown in (4): CE=E0+E b ×N b +E m ×N m +E r ×N r (4) Where CE: total life-cycle carbon emissions; E0: initial construction carbon emissions; E b Carbon emissions from a single major overhaul; N b : Analysis of the number of major overhauls within the service life; E m Carbon emissions from a single mid-term overhaul; N m : Analysis of the number of intermediate repairs within the service life; E r Carbon emissions per minor repair; N r : Analyze the number of minor repairs within the specified years.

6. The method for predicting the life-cycle cost and carbon emissions of AC pavement based on fatigue curves according to claim 5, characterized in that, The method for determining the initial carbon emissions E0 during construction: Determine the types, quantities, and unit carbon emissions of raw materials and machinery required in the five stages of initial construction: raw material production, asphalt mixture production, transportation, paving, and compaction. Multiply the quantity of each type by the unit carbon emission to obtain the carbon emissions of different types of raw materials and machinery. Add the carbon emissions of different types of raw materials and machinery together to obtain the final carbon emission. Carbon emissions from a single overhaul (E) b The determination method is as follows: determine the types, quantities, and unit carbon emissions of raw materials and machinery required for a single major overhaul in the five stages of raw material production, asphalt mixture production, transportation, paving, and compaction; multiply the quantity of each type by the unit carbon emission to obtain the carbon emission of different types of raw materials and machinery, and add the carbon emission of different types of raw materials and machinery together to obtain the result. Carbon emissions E of a single mid-term overhaul m The determination method is as follows: determine the types, quantities, and unit carbon emissions of raw materials and machinery required for a single intermediate repair in the five stages of raw material production, asphalt mixture production, transportation, paving, and compaction; multiply the quantity of each type by the unit carbon emission to obtain the carbon emission of different types of raw materials and machinery, and add the carbon emission of different types of raw materials and machinery together to obtain the result. Carbon emissions per minor overhaul E r The determination method is as follows: determine the types, quantities, and unit carbon emissions of raw materials and machinery required for a single minor repair in the five stages of raw material production, asphalt mixture production, transportation, paving, and compaction; multiply the quantity of each type by the unit carbon emission to obtain the carbon emission of different types of raw materials and machinery, and add the carbon emission of different types of raw materials and machinery together to obtain the result.

Images