Reliability Evaluation of Pavement Life-Cycle Assessment Model

Inventory reliability of the life-cycle assessment (LCA) model highly depends on the data quality and normally exhibits significant uncertainty. A rigorous statistical methodology was established to capture and quantify the inherent uncertainties linked to the results of the LCA model. Two sources of uncertainty, data quality and model, were identified. ,e former was captured by converting the deterministic value to probability density function using beta distribution according to the evaluation matrix of data quality; the latter was assessed by prescribing variation interval through defining uncertainty factor.,e functional equivalent pavement structures were designed, and the corresponding energy consumption and CO2 emission were calculated by the LCA model. A 10% variation was observed for the LCA results and within 30-year analysis span, at the 95% confidence level, and environmental burdens of cement pavement are higher than those of asphalt pavements while the comparison between the two asphalt pavements is not significant statistically. ,erefore, the established statistical methodology is capable of capturing the uncertainty of the LCA model and quantifying the reliability the LCA results.


Introduction
At present, energy conservation and emission reduction in transportation engineering have become the most important aim in China development planning.However, it is just a macroscopic one.erefore, a data base about the CO 2 emission and energy consumption is needed to accurately assess the effect of technologies for energy conservation and emission reduction.Besides, developing an evaluation template on the bituminous pavement construction and the benefit of environmental protection of maintenance technology is also necessary [1].Conducting studies on the evaluation system of technologies for energy conservation and emission reduction can provide theoretical foundation and technical support for evaluating the effect of technologies for energy conservation and emission reduction.erefore, it is extremely necessary to carry out the relevant studies.
e rapid development of road construction brings about serious environmental issues, such as energy consumption, CO 2 emission, and land occupancy [2].us, quantifying and reducing the influence of roadway on environment in its lifetime becomes gradually important and attracts wide attention [3,4].As a tool for assessing environmental implication, the life assessment (LCA) has been widely used in roadway engineering and becomes the main tool for analysis of influence of roadway on environment [5][6][7].However, some weaknesses exist in the theory of LCA, particularly the uncertainness of data quality and model [8].e accuracy of LCA for roadway or else objects depends on data and model parameters with high quality.Due to the absence of data and their reliability, the data utilized in LCA are not necessarily the expected one.
erefore, even though optimal data can be acquired or certain existing data can be combined, a trade-off has to be adopted.For example, the authors have collected numerous information and found that the consumption intensity for cement production is 4.6-7.3MJ/kg and that for bitumen production is 0.7-6.0MJ/kg.e great difference is shocking and resulted from the differences between system boundary, manufacturing technique, and manufacturing process in the local area.Besides, for bitumen material, the environmental influence depends on the distribution of different chemical products, such as petrol, diesel oil, and plastics.e difference of distribution style results in significantly different results [9][10][11].In the process of LCA establishment, the assumption and simplification are involved, which also affect the reliability of assessment results of environmental influence.
erefore, based on a practical case, a LCA model was developed and the influence of roadway on the environment was carried out.e uncertainness of assessment results of the LCA model is captured from the perspective of data quality and model assumption to establish a rigorous methodology for reliability assessment of results of the LCA model.

The Establishment of LCA Model
Roadway is a complex structure and susceptible to several external factors, such as climate, transportation intensity, and the production and transportation of material.us, the authors took advantage of the existing data model and established a LCA model which includes the following module: (1) Material production module: consideration on the process involved in the material production (2) Transportation module: description about the environmental influence during the material transportation (3) Construction maintenance module: consideration on the oil consumption of facilities and its environmental influence (4) Postprocessing module: different postprocessing scenes can be used according to the system boundary and the setup of consumption e function unit of the LCA model is set in an analysis cycle of 30 years and the service performance is applicable to a roadway with single lane and a length of 1000 m.

Methodology for Reliability Evaluation
e application of LCA depends on large amounts of data input which defined the characters of the model such as specification for road design, input and output stream of material energy, and environmental impact.e results of LCA are highly dependent on the reliability of input parameters with a certain degree of uncertainty inevitably. is investigation establishes corresponding methodologies for quantitative assessment from data uncertainty and model uncertainty.

Data Uncertainty.
e factors of data uncertainty are divided into experiential evaluation, measurement error, and nonrepresentativeness which includes data obsoleting and excessive technical differences, even data missing.Ideally, the solution to data uncertainty is establishing corresponding probability density function by collected data and putting it into LCA.However, the measurement data, samples, and experience that we need are usually rare.In this case, the descriptive index was employed to assess the quality of data, which forms the data quality evaluation matrix as shown in Table 1.
e data quality index (DQI) can be obtained by the established data quality system matrix.
e DQI can be converted to probability density function as input parameters of uncertainty analysis, specifically shown as follows: where α and β are the shape parameters of distributions and a and b are the interval end points.e reasons why choosing beta function are shown as follows: (1) e shape and probability of the distribution established by shape parameters are unknown, and endpoint parameters limit the range of prefabrication (2) e distribution shape of the beta function is various due to the shape and endpoint parameters.

Model Uncertainty.
e inherent uncertainty of the model associated with the decision affects the analysis results of the LCA.e model refers to the concept of generalization and has a wide range of extensions.It can be either a specific regression model, for example, prediction of asphalt mixing energy consumption, or input parameter assumptions, such as the amount of asphalt used in grading design and the organization of construction platoons.For the variability of LCA model input values, such as the ratio of oil and stone and transportation distance, it can be described by defining "uncertainty factor (UF)" where the uniform distribution and lognormal distribution are common distribution forms.
For the uniform distribution, the minimum and maximum values of the input parameters M x are defined using the following equation: where M x, min , M x, max , and M x,mod are the minimum, maximum, and modus, respectively.For the lognormal distribution, the fluctuation range of the input parameters M and UF are defined in the following equation: where the parameters are defined in Equation (2).

Case Analysis
is case analyzed the energy consumption and carbon emissions of highways cement pavement structure and asphalt pavement structure during their life cycle under the 2 Modelling and Simulation in Engineering same traffic volume and weather conditions and demonstrated the reliability of the calculation results.

Waterway Asphalt Pavement Structure Design.
For highways with asphalt pavement structures, the design period is generally 15 years.For cement highway structures, the design period is generally 30 years.In order to make the environmental impact analysis consistent, the functional unit is set to provide an acceptable road service performance for a single-lane one-kilometer-long road surface in a 30-year analysis cycle.e design index of the road structure tends to and does not exceed the design tolerance value.For asphalt pavement structural design, there are two situations: (1) e design life of asphalt pavement structure is set to be 30 years to achieve a certain degree of long-life asphalt pavement design.(2) e asphalt pavement design period is 15 years.
Assuming that, in the 15th year, the basement structure of the asphalt pavement was not damaged, only the asphalt surface structure was resurfaced by milling.
In order to ensure the functional equivalence of the cement and asphalt pavement structures, the pavement structure needs to be carefully designed so that the control index of the pavement structure approaches its limit at the end of the design life cycle, ensuring that the pavement structure has neither "excessive design" nor "insufficient design".Under this guiding principle, the equivalent design parameters of cement and asphalt pavement structures are shown in Table 2.
Although the asphalt pavement designed for 30 years of life will not suffer structural damage, it is still necessary to periodically perform conservation actions to ensure the functionality of the surface structure.At present, China has not established a strict decision-making system for pavement maintenance.erefore, the conservation manual in California is referred to maintain the three types of pavement structure [13].e maintenance plan is as follows: (1) For an asphalt pavement structure with a design life of 30 years, a 2.5 cm thick overlaying in thin layer is laid at the end of the 20th year, and a 4 cm-thick pavement structure is milling-resurfaced at the end of the 24th year (2) For an asphalt pavement structure with a design life of 15 years, a 6 cm-thick pavement structure is milling-resurfaced at the end of the 15th year, a 4 cm-thick overlaying in thin layer is laid at the end of the 20th year, and a 4 cm-thick pavement structure is milling-resurfaced at the end of the 25th year (3) For a cement pavement structure, the pavement structure is replaced with the cement board at the end of the 15th year and the end of the 25th year, respectively 4.2.Calculation Based on LCA Model.e environmental impact on each module has been computed based on the LCA model constructed before.e calculation factors include energy consumption and CO 2 emissions, using unit mass standard coal (kgce) and kg-CO 2 as their unit, respectively.

Material Producing Module.
Environmental impact of unit productivity on road materials has been determined through document retrieval, theoretical calculations, and on-site inspections, and the result is shown in Table 3.
According to the pavement structure design maintenance parameters and Table 3, the environmental impact on material producing module can be obtained.and JTG/T B06-03-2007 as the fundamental documents.e main steps are as follows: first, determining the number of per-unit-production of mechanical shifts according to the construction process flow stipulated in the budget quota; second, determining the total energy consumption by the energy consumption parameters of the unit machinery and equipment specified by the mechanical station cost quota; the last but not the least, calculating per-unit energy consumption.

Postprocessing Module.
e asphalt pavement and cement pavement reach the end of design life after the 30year life cycle.us, for the asphalt pavement, the pavement layer needs to be milled and the RAP needs to be transported to mixing building, and for the cement pavement, the pavement layer needs to be smashed and the RCM needs to be transported to the accumulation point, where the transportation distance is assumed to be 25 km.

Calculation Results of LCA Model.
Calculation of the environmental impact of the cement asphalt pavement structure under life cycle with the LCA model is done, and the results are shown in Table 4.
It can be seen that the stage of producing pavement material consumed the most energy and released the most CO 2 in all structures with any pavement.By comparing the structure with the asphalt pavement (15-year or 30-year design) and the cement pavement, the structure with the cement pavement consumes less energy and emits less CO 2 , which means the cement pavement structure has a greater probability of reducing the environmental impact during the life cycle.
In this paper, two life-cycle asphalt pavement structures were designed.e asphalt roads with a design life of 15 years have less impact on environment than those with a design life of 30 years, but the difference is not so significant.Also, such conclusion is closely dependent on the parameter assumptions in the LCA modeling process and needs further confirmation.

Reliability Analysis for LCA Model.
ere remains some uncertainty during modeling the LAC model due to data quality defects and model parameters assumptions, which are needed to be evaluated.

Data Uncertainty Analysis.
With the data quality assessment methodology established in Section 3.1, the quality of road material environmental data was evaluated.Taking bituminous materials as an example, the energy consumption of matrix asphalt production is 170.34 kgce.
e calculation for energy consumption of baseasphalt production uses the calculation model of Eurobitume 2011 and the data of China Statistical Yearbook as reference.
erefore, the evaluation scores for the indicators according to Table 1 are {3.0,5.0, 4.0, 4.0, 3.0, 5.0, 3.0}, and thus its DQI is 3.85.By using Table 5 as a reference, the parameter of beta function can be de defined.Its shape function α, β is (3,3), and the interval endpoint is (−20%, +20%).e probability density function of the production of matrix asphalt materials is shown in Figure 2.
According to the probability density function established in Figure 2, the energy consumption value and reliability of the matrix asphalt materials can be evaluated.Analogously, a probability density function curve can be established for the production of carbon emissions from matrix asphalt materials and other road material, providing a data source for the Monte Carlo analysis.Modelling and Simulation in Engineering mixing energy consumption are introduced, and the inuence of those parameters on the LCA calculation results is needed to be examined.ose parameters and their uctuation range are speci ed in Tables 6-7.

Results of LAC Model Uncertainty Analysis.
e Monte Carlo analysis was performed based on the parameters inputted in Sections 4.3.1 and 4.3.2.After 50 times of iteration, the results are shown in Figure 3.With the data in Table 5, the analysis chart can be drawn, shown in Figure 1.Modelling and Simulation in Engineering In Figure 3, the left Figure 3(a) shows the average energy consumption and its standard deviation for di erent scenarios, and Figure 3(b) shows the probability density function for each scenario.From Figure 3(a), the energy consumption of the PCC scheme is smaller than the HMA scheme generally.It can be found that the coe cient of variation of the PCC program, 30year, and 15-year design of the HMA program are 7.4%, 9.0%, and 9.1%, respectively, when it is used as the indicator of energy uctuation range.e volatility of the PCC program is slightly less than HMA program.Since the maximum volatility of all programs is about 10%, which is quite limited, the energy consumption indicators of di erent programs have a good stability.
De ne R as the ratio of the energy consumption of design 1 to the energy consumption of design 2, and then P (R<1) means the probability that R is less than 1.According to Figure 3(b), 50000 Monte Carlo analyses were performed, and the results are shown in Figure 4.
It can be seen from Figure 4 that the probability that the energy consumption of the cement pavement structure is less than the 15-year design asphalt pavement structure is 97%, and the probability that the energy  Energy consumption (kgce)  Modelling and Simulation in Engineering consumption of the cement pavement structure is less than the 30-year design asphalt pavement structure is 99%.Under the conditions set in this study, the cement pavement structure consumes less energy during the life cycle at the 95% confidence level.Comparing these two asphalt pavement designs, the probability that the energy consumption of the 30-year design asphalt pavement structure is less than that of the 15-year design asphalt pavement structure is 47.5%.It is difficult to have sufficient reason to detail the difference in environmental impact between the two structural designs.Similar conclusions can be drawn for carbon emissions indicators and will not be repeated here.

Conclusion
(1) A road LCA model with complete system boundary has been constructed.is model can not only describe the environmental impact of the road during service, but also can be used to assess the environmental impact of road materials in a measurable way.(2) A rigorous method for evaluating the reliability of LCA model calculation results is constructed, which can be used to evaluate the reliability of LCA model settlement results under data quality and model assumptions.
(3) Under the established research background, the environmental impact of the cement pavement structure is less than the asphalt pavement structure at the 95% confidence level.e average energy consumption and carbon emission of the cement pavement is 21.6%-28.2%lower than the asphalt pavement during the life cycle.(4) e reliability analysis shows that the results of the LCA model are stable and the fluctuation range is about 10%.(5) Under the research background setup, there is no obvious environmental difference between the 15-year design life asphalt pavement and the 30-year design asphalt pavement at the 95% confidence level.

Figure 1 :
Figure 1: (a) Energy consumption and (b) CO 2 emission of the three pavement structures.

Figure 2 :
Figure 2: Probability density function of base asphalt production energy consumption.

Figure 3 :Figure 4 :
Figure 3: Energy consumption comparisons of the three pavement structures.

Table 1 :
Data quality evaluation matrix.

Table 3 :
Design parameters of pavement structure.

Table 5 :
Life-cycle inventories of the three pavement structures.

Table 4 :
Energy consumption and CO 2 emission of pavement materials in China.

Table 6 :
Parameter uncertainties of the asphalt pavement LCA model.
Note. 1: uncertainty parameters determined by engineering investigation (the subscripts are similar); 2: the asphalt amount adjusted with SBS amount.

Table 7 :
Parameter uncertainties of the cement pavement LCA model.Note.1:uncertaintyparameters in the remaining stages are consistent with those in Table2.