Posted: July 26th, 2022

AASHTOWare Pavement ME Design v2.6 Local Calibration for Ontario flexible pavement

AASHTOWare Pavement ME Design v2.6 Local Calibration for Ontario flexible pavement.
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AASHTOWare Pavement ME Design v2.6 Local Calibration for Ontario flexible pavement
Historical Development of Pavement ME Design method
The pavement designs and construction processes have evolved over the years through the Roman era, the macadamia, and the Telford era. The AASHTO pavement ME design method was developed and released in 2008 by the American association of state highway and transportation. The design method was fundamental, as it involved entering data into a computer program about climate, materials, traffic, and a structural pavement through a hierarchical method. The data was primarily collected to determine the long-term pavement performance(LTPP). The evolution of pavement designs focuses more on the united states and the united kingdom than other countries. According to history, the flexible pavements and the hot-mix asphalt pavements. Other types of pavements include:
• The jointed reinforced concrete pavement(JRCP).
• The stressed concrete pavements(PCP).
• The jointed plain concrete pavement(JPCP, and the Portland cement concrete(PCC).
The united states have many roads made from different pavement, where ninety-three per cent of the roads are developed using asphalt (Hamdar, and Chehab, 2017). The main difference between the flexible and rigid pavements is the pavements’ ability to flex, which makes the pavements deflect, caused mainly by traffic load. On the other hand, the rigid pavement has stiffer concrete, hence having a wider soil area.
Hamdar and Chehab (2017) state that the AASTO guide for designs of pavements structure has been the most used over the years, since its development in 1958. The project is considered a multi-million dollar construction project, according to (Abu Ahmed Sufian (2016). According to the article, pavements are affected by factors, such as climatic conditions, traffic, and truckload. However, the design methodology has been improved and enhanced through the use of asphalt materials, which tend to improve the thickness, speed, and traffic volume (Vásquez-Varela, and García-Orozco, 2021). The thickness property plays an important role when it comes to climate changes. According to (Hamim et al.,2020), an essential material for the development of the mechanical-empirical pavements design is the use of asphalt concrete, where some formulae and laboratory tests have been used to determine the frequency and temperature levels.
Asphalt pavements have evolved and advanced over the years, where the designs have evolved from empirical to mechanical-empirical, which will improve in future to a more mechanical design (Vásquez-Varela, and García-Orozco, 2021). According to the article, flexible pavement designs had evolved since 1980, when mixture designs were first introduced. Due to intense laboratory experiment and test over the last fifteen years has assisted in evaluating the performance of the materials and develop future designs.
According to Nelson Fernando and Cunha Coelho (2016), the design of pavement structures have gone through some steps, which has promoted the development of the pavements based on stress, moisture, magnitude, and temperate. The development of pavements is based on the nature of the environment, such as temperatures and freezing points.
According to Nelson Fernando and Cunha Coelho(2016), some tests have been developed to determine the rationality and accuracy of the pavement design method through a performance test (Hamdar, and Chehab, 2017). The first test site was first developed in 1968, known as the ASHOO road test, which studies the performance of pavements under different conditions. Over the years, some versions have been developed, for instance, in 1986, 1993, and 1998, which assisted in addressing faults present in the design procedure. The first AASHTO pavement design guide was introduced in 1993, used by the country, transportation industry, and various states.
The evolution of the AASHTOWare ME pavement design has been promoted by advancing technology, where today various performance models have been developed through the help of computers ad computational mechanics. Development of knowledge concerning pavement performance, climate effects on pavements, materials, and traffic has evolved over the years through the mechanical-empirical pavement design guide(MEPDG) manual of practice (Vásquez-Varela, and García-Orozco, 2021). As a result, today pavement performance information can be derived from a large amount of pavement performance information. The software AASHTOWare pavement ME design TM is a software model that handles computational projects. The current design is the mechanical-empirical pavement design guide(MEPDG), developed by the NCHRP, used to calculate cumulative damage.
Pavement ME design software was first released in 2004, being a cooperative highway research program(NCHRP). The first version of the project was, however, considered as version 1.1. Since then, the other versions have been advanced, having more features, such as Ashton version 2.5 and version 2.6. After some advancements, the MEPDG was rebranded in 2011, as the Darwin -ME, made under the NCHRP. According to Crawford et al. (2010), the length of the pavements is significant, which is determined by the coefficient of thermal expansion (CTE) (Hamdar, and Chehab, 2017). According to the historical development of ME design methods, more features were included. The features include
 the climate data editing tools,
 Improvement of output reports for PDF and Excel formats.
 The enterprise database is used for design consideration, traffic, storing projects, and sharing projects.
 The ability to run more than one designs simultaneously
 A redesigned user interface.
 Solving several stubborn software issues, such as the increased or high analysis run time.
More webinars was created approximately thirteen, mainly for the federal highway administration(FHWA), and the AASHTO pavement ME design task force. However, the webinar series consisted of topics such as traffic input, new asphalt pavement structure, climate input, ME designs, materials and design input, and climatic inputs.
• The AASHTOWare Pavement ME Design v2.6 Method
According to Kim et al. (2020), the AASHTOWare pavement ME Design V 2.6 method was first released on first July 2020. The design is an improvement of the 2.5.5 version, used to resolve some issues. The 2.6 version has some changes that have been reported, such as the top-down cracking, the EICM performance enhancement, the APADS performance enhancement, and an update of the climate map (Hamdar, and Chehab, 2017). The solutions developed to lead to changes through several to resolve the issues present in the 2.5.5 version. However, the calibration of the 2.6 model is not yet done; hence, the version is not yet in use. The AASHTOWare level 2.6 has a modified webinar, has an advanced and more flexible pavement, and consists of a new top-down cracking model (Kim et al.,2020).
However, the software developed is guided by some standards and regulations, which focus on improving the version. Additionally, several technical standards, such as database usage, are developed to fit into organizations (Vásquez-Varela, and García-Orozco, 2021). The accessibility of the ASHTONWare version 2.6 can be easily accessible, where software adheres to section 508 of the united states Rehabilitation Act and the consortium web accessibility initiative. However, the software tool is developed to enhance the daily operations of private and public pavement engineers and promote calculations of deflections, stresses, and strain according to material, climate, and traffic (Hamdar, and Chehab, 2017). The version provides high performance, reliability, as well as a user-friendly interface with high stability (Hamdar, and Chehab, 2017). The version assists in consultation and the decision making process concerning pavement construction.
On the other hand, version 2.6 provides guidelines and design requirements and evaluates several parameters to be considered in constructing future pavements designs. The 2.6 version promotes the need for management (Kim et al.,2020). AASHTONWare pavement version 2.6 provides an effective change in design structure, climate data, axle-load spectra, and material mechanics. The AASHTONWare ME design bring about a considerable change (Kim et al.,2020). Primarily through the new tools developed. The type of tools developed includes,
 Calibration assistance: The tool assists in detecting and identifying any biases during performance prediction. Also, the tool assists in knowing the cause of the problem to reduce standard errors.
 Drip: The tool works effectively in conducting the hydraulic design computation when it comes to analysis drainage.
 File and analysis APIs tolls, such as the CIM and JULEA.
 The MaP is another tool that creates the ME design projects files and contains information that assists in analyzing the pavement.
 XML Validator is another tool that assists in identifying errors that may be present in XML files and checks on data value.
 rePave scoping tool is another improvement of the AASHTONWare 2.6 version that assists in deciding what conditions drivers should use pavements (Vásquez-Varela, and García-Orozco, 2021).
 Back calculation tool is an essential tool that assists in deriving data files from the falling weight deflectometer(FWD) (Hamdar, and Chehab, 2017).
The tools, however, conducts more than one function, for instance, back calculations, pre-processing deflection of data, and analyzing raw deflection data files (Kim et al.,2020). Furthermore, the evolution of ASHTON pavement ME design software keeps evolving to meet the needs and requirements of the transportation agencies, especially in the construction of pavements and bridges.
The AASHTONWare project is a comprehensive project that assists in managing the agency construction program through several modules that meet the construction needs. The modules, however, focus on the management of labour and civil rights. The modules include the AASHTONWare project civil rights and labour, project analytic, pre-construction and construction, and material (Kim et al.,2020).
• Design Method and flexible pavement performance prediction models
The pavement performance prediction model can be developed based on past performance data. The ability to forecast future pavement condition has been an area of concern, primarily through the assistance of pavement management systems. However, the prediction has not yet been effectively made, due to some challenges, such as difficulties collecting pavement performance data (Manoharan, Chai, and Chowdhury, 2021). The leading cause of the difficulties are caused mainly by the use of different pavement materials, the complex pavement construction situation. However some models have been developed to predict pavement performance, for instance, the use of the pavement condition evaluation system(PACES), /./mainly to analyze the pavement condition of the pavements. Also, the use of multiple testing methods, such as the PACES and the PACES rating, has worked. However, the multiple linear regression method is the most used and effective method used to determine the performance of pavements (Kim et al.,2020). On the other hand, achieving flexible pavements has been a problem, primarily due to cases of disintegration, surface deformation, cracking, thermo-mechanical, car traffic systems, internal elements, the pavement system, and complex mechanical problem.
According to the article, the performance prediction method provides the uncertainties, the variability within time, and complexities. Some of the models used include the bottom-up fatigue model, permanent deformation model, transverse thermal cracking model, and top-down longitudinal cracking model (Manoharan, Chai, and Chowdhury, 2021).
Bottom-Up Fatigue or Alligator Cracking Model Bottom-Up Fatigue or Alligator Cracking is produced by repeated applications of tensile strain resulting from wheel loading. Once developed, the cracks propagate upwards from the bottom of the HMA layer to the top. Bottom-Up fatigue cracking is usually a loading failure. Still, other factors can contribute, such as inadequate structural support (loss of base, subbase or subgrade) and poor drainage or spring thaw resulting in a less rigid base. The tensile strain magnitude at the bottom of the asphalt concrete increases when soft layers are placed directly below the asphalt layer, and consequently, the probability of fatigue cracking increases.
This distress is characterized by a series of interconnecting cracks in the asphalt layer. Tensile strains are higher at the bottom of the HMA layer in thin pavement structures, where cracks initiate and progress upwards in one or several longitudinal cracks. Agencies report this cracking based on severity; it is measured as a percentage of the total area and classified as low, medium, or high. 19 Alligator cracking is calculated by first predicting damage. Then, the damage is converted into a cracked area. The Asphalt Institute method was adopted to MEPDG and calibrated based on LTPP data (Hamdar, and Chehab, 2017). The equation developed is used to convert square feet to the percentage of alligator cracking. Top-Down or Longitudinal Cracking Model Top-down cracking develops at the pavement surface and propagates downward. Inflexible pavements, longitudinal cracking development is conceptually identical to “alligator” fatigue cracking.
Tensile strains at the top of the asphalt concrete surface layer caused by traffic loading generate cracks. Longitudinal cracking generally develops parallel to the pavement centerline and is usually produced by fatigue failure due to repeated traffic loading; however, other factors could contribute to poor construction paving joint, shrinkage of the asphalt, temperature variations, and underlying reflection layers. Agencies report this cracking based on severity. It is measured in meters per kilometre, and it is classified in terms of low, moderate or high level (Hasan, Rahman, Tarefder, 2020). For top-down fatigue cracking, the damage is converted into longitudinal fatigue cracking. The maximum length of linear cracking resulting from two-wheel paths of a 500 feet section is 1000 feet. A factor of 10.56 is used to convert the longitudinal cracking from feet per 500 feet into feet per mile.
Transverse Thermal Cracking Model Thermal cracking is caused by fantastic/heat cycles in the asphalt concrete. The pavement’s surface cools down promptly and more intensively than the pavement core 22 structure, which causes thermal cracking at the pavement surface (low temperatures prevent friction at the bottom of the HMA surface). Thermal cracking generally manifests and extends in the transverse direction across the entire width of the pavement. Cracks initiate at the pavement’s surface when the tensile stress at the bottom of the HMA layer exceeds its tensile strength. Moisture in the pavement, daily temperature cycles and cold weather are other conditions that also contribute to the development of thermal cracking. Thermal cracking is reported based on severity, measured in meters per kilometre, and classified in low, medium, or high levels (Manoharan, Chai, and Chowdhury, 2021). The Paris law is used to compute the crack propagation for a given thermal cooling cycle that stimulates a crack to propagate.
Permanent Deformation (Rutting) Model Permanent Deformation (Rutting) is defined as a depression in the wheel path. Rutting is load-associated distress generated by cumulative load applications when the HMA has the lowest stiffness, i.e., at moderate and high temperatures. Rutting is commonly categorized into three stages. Primary deformation emerges early in the service life and is associated with mixture design. In the secondary stage, deformation increments are lower at a constant rate, and the mixture is experiencing plastic shear deformations.
Finally, shear failure occurs in the tertiary stage, and the rupture of the mixture takes place. Before this stage is achieved in pavements in operation, preventive maintenance and rehabilitation are required. Empirical models predict rutting in each layer throughout the analysis period, but only primary and secondary stages are outlined. The model for HMA materials is an improved version of LeLeahy’sodel (1989), modified by Ayres (1997) and Kaloush (2001). For unbound materials, the model is based on Tseng and LyLytton’sodel (1989), modified by Ayres and then by El-Basyouny and Witczak (NCHRP, 2004). This distress is not based on an incremental approach and instead measured in absolute terms. The empirical models included in MEPDG must be calibrated accounting for local conditions, given that temperature and moisture content are embedded in the computation of permanent deformation by their effect on dynamic modulus for asphalt concrete and resilient modulus for granular layers. The computing total permanent deformation model uses the plastic vertical strain under specific pavement conditions for the total number of repeated loads within that condition (AASHTO, 2008). The total rutting is the summation of the rut depths from all layers, as follows: Equation (16) 2.4.4.1 Asphalt Concrete Model The AC layer is subdivided into sublayers, and the total estimated rut depth for the layer is computed as follows:
International Roughness Index (IRI) Model Pavement Roughness is generally defined as a manifestation of irregularities in the pavement surface that negatively affects the ride quality. Roughness is recognized as the most representative distress of the overall serviceability of a roadway. Fatigue and thermal cracking, and permanent deformation are acknowledged as the most prevailing distress affecting 27 roughness. Other influential factors are environmental conditions and supporting base type (Hamdar, and Chehab, 2017). International Roughness Index (IRI) is a roughness index obtained from measured longitudinal roadway profiles. LTTP data was used in the calibration process to develop three models for flexible pavements with distinct base layers: conventional granular base, cement-stabilized base, and asphalt-treated base. All roughness models have a similar form. Equation initial IRI after construction of combined fatigue cracking (alligator, longitudinal, and reflection cracking in the wheel path), in % of total lane area (Manoharan, Chai, and Chowdhury, 2021).
Some of the models used in predicting pavement performance in the future include the gamma process model, the distress-based pavement performance prediction model, also known as the distress deterministic performance prediction model for management of flexible pavements. Other models include the finite element model and the multi-layer elastic theory (MLET). The model used to determine climatic effects on pavements includes the enhanced integrated climate model(EICM). The models are used to predict distress in the pavements (Dong, Yuan, Hao, 2020). According to the article, calculations are involved, especially in determining fatigue cracking. The finite element model is very effective in determining the multi-layer pavement section that consists of material properties that are different vertically and horizontally. Some of the characteristics of the pavement consider by FEM includes the behavior of asphalt concrete, both linear and non-linear. The model, however, determines the freeze-thaw cycle for unbound materials, the asphalt concrete temperature variations, and the unbound moisture variations. The main reason for the performance test is that pavement designs experience several uncertainties, which are determined through mean predicted values and standard deviation.
According to Khaled Abaza, the deterministic performance prediction model for management of flexible pavements is a model that uses the concepts and regulations from the American association of state highway and transportation Officials (AASHTO). The management of flexible pavements requires data and feedback that assist in measuring pavement distress (Hamdar, and Chehab, 2017). According to the model, the model, the outcomes, provides a curve for pavement structures. The pavement curve is achieved by using an equivalent single axle load(ESAL) and the serviceability index(PSI). The parameters are related to the features, such as environmental conditions, material properties, reliability, performance trend, and environmental conditions. The basic equation used in performance prediction is based on AASHTO 1993.

The equation assists in calculating some items, such as incremental deterioration. The results and information derived from the test are used to evaluate the pavement’s performance, which determines the nature of the curve. The curve appears downward if the results show a fine pavement structure and upward when showing an inferior pavement structure. The performance is, however, evaluated through the curve formed through the PSI versus service time. The pavement. The method, however, provided results in mathematical form. According to Maher Mahmood and Mujib Rahman, a distress-based pavement performance prediction model is another name given to the deteriorating that explains some variables, such as the cracking area, age, climate, cumulative equivalent, and the single axle load(ESAL). However, the deterministic deteriorating model provides performance and evaluates the maintenance effect of the pavement determined by the level of thickness. The most used deterministic deteriorating model include,

A successful, deterministic deteriorating pavement performance assists in managing the pavements, where the results are primarily developed through a chain technique.
According to Ruwini Edirisinghe1, Sujeeva Setunge, and Guomin Zhang (2016), the gamma process deterioration model is the best model s for predicting performance in pavements. Predicting the deterioration level of pavement is essential in the building management process, especially in all stages of the process. The main processes where the deterioration prediction can be made include data collection, condition rating, forecasting, decision making, and cost forecasting. However, the deterioration prediction method is the fifth process among another processes, focusing on condition, age, exposure, and type of use. Some of the artificial intelligence techniques used in prediction include the fuzzy set theory, case-based reasoning, and the neural network.
The AI techniques are considered the best because they are insensitive to noise and do not lead to over-fitting. According to the author, the statistical model, however, applies several statistical theories (Dong, Yuan, Hao, 2020). The gamma process effectively provides the uncertainties of pavement, which is considered the rate of deterioration. Some countries, such as Somalia, used the gamma process to determine deterioration in bridges. The lifetime cumulative probability of the lifetime is
Represented by the following equation.
. The gamma deteriorating predicting model is classified into three major models, the artificial intelligence model, the deterministic model, and the statistical model. The deterministic deterioration model, which can be linear or non-linear, is mainly used where the number of features and phenomena is considered similar.
• Design Input Levels (Level 1, Level 2, Level 3)
The design input of the AASHTOWare pavement ME design process follows three primary levels, which include level one, level two, and level three. Level 1 is the field test that is a result of specific input property. Level two, on the other hand, involves an estimate of properties, together with other tests. While level 3 is the stage of the best estimates of the input parameters.
The design input levels are hierarchical, providing the flexibility of using the available materials, pavement conditions, traffic, and resources. The levels are, however, determined by their nature of accuracy, according to the author. For instance, level 1 consists of accurate parameters, which are originally derived from the site, hence less likely to contain uncertainties (Dong, Yuan, Hao, 2020). The calculations of the input are done through expensive experimentation. The level also is significant for site conditions, site materials, and traffic. Level 1 also used axle load spectra, classification, and volume based on the project. For traffic data importation, level 1 should gather information concerning traffic, classification, axle angle, and volume.
On the other side, level 2 provides accurate traffic loading data achieved through AVC and WIM measurements. Level 2 uses volume classification and traffic to provide accurate data concerning the project. To achieve level 2 input, the data should be accurate, especially traffic data, which should be taken at least weekly and determine truckload variations. Level 2, however, is not as accurate as level 1, hence considered to provide intermediate accuracy. Inputs in level 2 are calculated through regression equation, which is based chiefly on practical level one database. The data represented in level 2 is also a representation of regional DOT data.
Level 3 is the least accurate as far as input parameters are considered (Dong, Yuan, Hao, 2020). The level is only applied where there is less likelihood of failure, primarily used in low volume roads. The third level also uses regional vehicle classification and axle load spectra, which produces poor accuracy. Through site vehicle encounters, the organization consists of the average annual daily traffic(AADT). However, the data is concerned with the axle load distribution, truck percentage, truck traffic, and truck distribution.
• Global Calibration of flexible pavement performance prediction models and Limitations
The global calibration coefficient of flexible pavements uses The AASHTOWare pavement ME design to make initial predictions. Through the method, the AASHTONWare ME design was predicted to experience alligator cracking and rutting. As a result, the global calibration of flexible pavement is considered inaccurate compared to the local calibration, reducing biases.
The global calibration parameters were used to predict pavement distress, according to the article. The mechanical-empirical pavement ME design was globally calibrated, where the results provide have BEEN MONITORED through the long-term pavement performance program. According to an article, the results of global calibrated designs are slightly more prominent than those of local calibration, for instance, in thickness, different from the local calibration.

• Former Local Calibration Studies
Calibration is a word that is attributed to a mathematical process for the MEPDG. According to the article, The performance methods used in identifying the performance of the pavements used the local calibration method, which is the latest method and an improved version of the global calibration process. The local calibration of the mechanistic-empirical pavement design guide consists of several stages used in selecting the input parameter’s hierarchical input during pavement analysis (Lee, Wilson, and Hassan, 2017). The input parameter, however, is influenced by several factors, such as the materials, laboratory testing, data, consistency, traffic data, and construction specifications, according to the AASHTO MEPDG manual practice, for instance, the input level for asphalt concrete material properties parameters include

The local caliberation on the other side follows a number of steps, one, the review pavement design and construction practice, which ensures traffic, materials and designs are compatible, where the mechanistic-empirical pavement design guide plays a major role. Another process is the pavement design and construction practices, which consist of a number of designs practices, such as roadway classes, rehabilitation maintenance and preservation practices, design life and distress limits, region specific policies, and design strategies (Guo, and Sollazzo, 2021). The pavement types, is very important, in the local caliberation, for instance, use of CRCP new, and overlays, the AC pavements and overlays, the SJPCP overlays of flexible pavements, or use of both the SJPCP overlays and CPR ovelays. The pavement design determines the performance criteria, as well as the threshold value at the end of the design life. For instance, the IPCP new, and CPR uses the mean joint faulting, the IRI, and percent transverse slab cracking.
On the other hand, the CRCP new and overlays, uses the IRI, and the punchouts. The local calibration measures the s triggers, and possible implications for maintenance, rehabilitation, ad preservation (Dong, Yuan, Hao, 2020). The method looks for the observed limits, time increments, and a comparison of the design life, and failure criteria. On the other hand, the local caliberation may be biased, and can limit the number of increments. The construction, and site investigation practices, for the calibration process , must be done on the construction materials, and requirements, a site condition assessment, the lab and field testing procedures, and the structural layer thickness and material type databases.In lab testing, the measured properties, includes,
 The dynamic modulus
 Creep compliance
 Poison ratio
 Aggregate specific gravity
 Effective asphalt content by volume
 Air voids
 Unit weight
 Voids filled with asphalt
 Tensile strength.
The most recommended data source, for the local caliberation includes,
 AASHTO T 322
 AASHTO T 308
 AASHTO T 342
 AASHTO T 27
 AASHTO T 166
 AASHTO T 209
 AASHTO T 84.
The site condition assessment, is done trough a field testing , which checks on the resilience modulus, the DCP, and the CBR. Additionally, the local caliberation measures the backcalculate layer moduli, thickness, and the cracked and intact location. The local caliberation measures the construction material practices, which based on traffic level dependent, region specific level, climate dependent, and layer dependent. The data however is very important, especially in avoiding biases, where data management is key, especially the data compatible with LTPP distress identification manual, and PMED.
The information needed includes, information on traffic, pavement distress, structure of the pavement, construction history, as well as the site condition. The data is mostly sourced from a number of research studies, design drawings of the structure, field testing, and investigation, as well as the historical construction records (Guo, and Sollazzo, 2021). The man impact of the local caliberation is development of confidence towards the pavement, prevent biases, prevent standard errors, and for reliability. The major considerations according to the article when it comes to local calibration is knowing your data, and making sure the selected inputs are compatible.
o What was the Input Data Level (Level 1, Level 2, Level 3) for material, climate and traffic used for flexible pavement performance prediction models?
Flexible pavement performance prediction models are very important according to Rabbira Garba Saba, (2007). Performance prediction as an important in making decisions, follows three input data levels for traffic, materials, and climate. In level one, the parameters, are estimated through laboratory testing based on the AASHTO certified standards for paving materials.
Input level one consist of direct measurements that use a permeability test(AASHTO T215) when measuring the saturation of the hydraulic conductivity. On the other hand, a direct measurement is conducted when testing the dry thermal, conductivity, and the heat capacity. The required testing are done through a filter paper, a temp cell testing, and a pressure plate.Te testing process is made based on the AASHTO T 180, AASHTO T 99, and AASHTO T 100.
On the other hand, climate effect on pavement is measured through use of the integrated climate model(ICM). The ICM, however measures the content of temperature, and moisture between the pavement layers, as well as resilience modulus (Guo, and Sollazzo, 2021). On the other hand, the ICM estimates, or rather measures the seasonal changes in soil moisture content, which requires a number of input information.The ICM analysis evaluates the ground water related information, materials, and pavement structure, mostly on material properties, asphalt, and thickness of the layer. Also, the climate effect tool measures general information, such as the traffic opening month,year,construction month,and year, and the types of design.Also,the tool measures the ground water, or the depth of the water, and any weather-related information based on hours.For instance, the hourly precipitation, relative humidity, wind speed, sunshine.
Climate input are only applicable in level 1 and 3, where in level one, latitudes, longitudes, and elevation parameters are measured. Through a google map, the site specific location, longitude, and latitudes are identified. Traffic however is measured by taking information concerning the annual average daily truck traffic, which is a two-way AADTT (Guo, and Sollazzo, 2021). The two-way AADTT, measures the number of trucks that pass along the pavement on a daily, where the values are summed up to annual average data.Additionally, the number of lanes are measured in design direction. The percentage of truck in design direction provides total number of tracks passing over the pavement which only takes place in level 3.The percentage number of design lanes are calculated through the product of annual average daily traffic(AADT). Another information collected is about operational speed, which is the speed the drivers use in a free-flow condition (Lee, Wilson, and Hassan, 2017).The operational speed however is based on different types of highways, the collector, local, arterial, and freeway. Other traffic Parameters measured includes, the traffic capacity, the axle load distribution,the hourly adjustment factors, growth function, monthly adjustment factor,and the axle per truck. The traffic data inputs are classified according to levels.
On the other side, the local calibration follows some steps, one, the review of pavement design and construction practice, which ensures traffic, materials, and designs are compatible, where the mechanistic-empirical pavement design guide plays a significant role. Another process is the pavement design and construction practices, which consist of some design practices, such as roadway classes, rehabilitation maintenance, preservation practices, design life and distress limits, region-specific policies, and design strategies. The pavement types are fundamental in the local calibration, such as the use of CRCP new and overlays, the AC pavements and overlays, the SJPCP overlays of flexible pavements, or the use of both the SJPCP overlays and CPR overlays. The pavement design determines the performance criteria and the threshold value at the end of the design life. For instance, the IPCP new and CPR uses the mean joint faulting, the IRI, and percent transverse slab cracking.
On the other hand, the CRCP new and overlays use the IRI and the punchouts. The local calibration measures the s triggers and possible implications for maintenance, rehabilitation, ad preservation. The method looks for the practical limits, time increments, and a comparison of the design life and failure criteria. On the other hand, the local calibration may be biased and can limit the number of increments. The construction, and site investigation practices, for the calibration process must be done on the construction materials and requirements, a site condition assessment, the lab and field testing procedures, and the structural layer thickness and material type databases. In lab testing, the measured properties include,
 The dynamic modulus
 Creep compliance
 Poison ratio
 Aggregate specific gravity
 Effective asphalt content by volume
 Air voids
 Unit weight
 Voids filled with asphalt
 Tensile strength.
The most recommended data source for the local calibration includes,
 AASHTO T 322
 AASHTO T 308
 AASHTO T 342
 AASHTO T 27
 AASHTO T 166
 AASHTO T 209
 AASHTO T 84.
The site condition assessment is done through field testing, checking the resilience modulus, the DCP, and the CBR. Additionally, the local calibration measures the back-calculate layer moduli, thickness, and the cracked and intact location. Finally, the local calibration measures the construction material practices based on traffic level-dependent, region-specific level, climate-dependent, and layer-dependent. However, the data is fundamental, especially in avoiding biases, where data management is critical, especially the data compatible with the LTPP distress identification manual and PMED.
The information needed includes information on traffic, pavement distress, the structure of the pavement, construction history, and the site condition. The data is mainly sourced from some research studies, design drawings of the structure, field testing, investigation, and historical construction records. The man’s impact on the local calibration is the development of confidence towards the pavement, preventing biases, preventing standard errors, and reliability. According to the article, the significant considerations when it comes to local calibration are knowing your data and making sure the selected inputs are compatible.
o What was the Input Data Level (Level 1, Level 2, Level 3) for material, climate, and traffic used for flexible pavement performance prediction models?
Flexible pavement performance prediction models are significant, according to Rabbi Garba Saba (2007). Performance prediction is essential in making decisions, follows three input data levels for traffic, materials, and climate. In level one, the parameters are estimated through laboratory testing based on the AASHTO certified standards for paving materials.
Input level one consists of direct measurements that use a permeability test(AASHTO T215) when measuring the saturation of the hydraulic conductivity. On the other hand, a direct measurement is conducted when testing the dry thermal conductivity and the heat capacity. The required testing is done through filter paper, temp cell testing, and a pressure plate. The testing process is made based on the AASHTO T 180, AASHTO T 99, and AASHTO T 100.
On the other hand, the climate effect on the pavement is measured using the integrated climate model(ICM). However, the ICM measures the content of temperature and moisture between the pavement layers and resilience modulus. On the other hand, the ICM estimates or relatively measures the seasonal changes in soil moisture content, which requires several input information (Lee, Wilson, and Hassan, 2017). First, the ICM analysis evaluates the groundwater-related information, materials, and pavement structure, mostly on material properties, asphalt, and thickness of the layer. Also, the climate effect tool measures general information, such as the traffic opening month, year, construction month, year, and the types of design. Also, the tool measures the groundwater, the depth of the water, and any weather-related information based on hours. For instance, the hourly precipitation, relative humidity, wind speed, sunshine.
Climate input is only applicable in levels 1, and 3, wherein level one, latitudes, longitudes, and elevation parameters are measured. Then, through a google map, the site-specific location, longitude, and latitude are identified. Traffic, however, is measured by taking information concerning the annual average daily truck traffic, which is a two-way AADTT. The two-way AADTT measures the number of trucks that pass along the pavement daily, where the values are summed up to annual average data. Additionally, the number of lanes is measured in design direction.

Parameters Level 1 Level 2 Level 3
AADTT Number of lanes
Two way AADTT Percentage trucks in design direction  Percentage of truck n design direction
 Operational speed
Axle distribution Not applicable Tridem axle distribution Tandem axle per truck
Quad axle per truck
Single axle per truck
Not applicable
Traffic Volume Adjustment Growth rate
Growth function of truck traffic Vehicle class distribution
Tridem axle per truck
Quad axle per truck
Tandem axle per truck
Hourly adjustment factors
Monthly adjustment factors

Wheel base Not applicable Average spacing of long axle
Percent truck with short axle
Percent truck with long axle
Average spacing of medium axle
Percent truck with long axle
Percent truck with medium axle
Lateral traffic wander Design lane width Not applicable Traffic wander standard deviation
Mean wheel location

o What is the Data used for Climate?
The data used for climate input includes the drainage coefficient, wind speed, precipitation, and temperature. However, the temperature and moisture analysis are done through the enhanced integrated climate model(EICM), which measures the wind speed, percentage of sunshine, relative humidity, and air temperature (Guo, and Sollazzo, 2021). However, the mist of the climate data is obtained from whether stations, where the accuracy of the data is measured through an analysis of different weather stations to get the similarities and evaluation of data in different climatic zones. According to the article, predictions of pavement temperature histories are measured through the seasonal monitoring program(SMP).
The primary data sources include the NOAA, the NCDC, and other sources, such as automated airport weather stations, aircraft, wind profilers, radiosondes, NWS cooperative observers, radar, and satellites. Also, the local climatic data is used, especially that from climatology offices that work together with the NWS and RCCs. On the other hand, the automated surface observing system(ASOS), where the climatic data from the organizations is frequently updated after every twenty-four hours (Guo, and Sollazzo, 2021). However, the data provides information concerning all weather elements, such as precipitation accumulation, ambient temperature, fog, and any other obstruction of the visual element. Also, the data consist of the sky condition, the intensity of rain, wind direction, and visibility. Also, climatic data can be derived from the road weather information systems, which assist in understanding the impacts of whether of pavements.
The RWIS is considered as an environment sensor system that measures the hydrological conditions and atmospheric pressure. The ESS may also be a temperature point sensor, solar radiation sensor, cameras, and visibility sensors.
However, the systems assist in collecting information concerning the flooding water level, subsurface temperature, and road surface temperature. Another recent and promising data source is the MERRA, which collects data hourly after conducting atmospheric conditions observations The most used systems used for observation, include the earth observation, system version, and the national aeronautics and space administration (NASA) systems. Potential climatic data is determined by the data source and how accurate the data is. Climate data is fundamental in the pavement I design performance, especially in determining the cause of joint faulting, crack slabs, rutting, and cracking of flexible pavements. Temperature and moisture are the most measured parameters on an hourly basis based on the EICM.
• Summary Remarks
The development of the mechanical-empirical pavement design to AASHTOWare pavement ME design software has been very impactful, especially in providing data concerning the future development of pavements. The new AASHTONWare pavement ME design method is version 2.6, which improves version 2.5 through the development of new tools. In addition, the difference between different designs has been conducted through the performance prediction models through the assistance of the local calibration method.

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