Use of Field Measured Data For Improving Pavement Mechanistic-Empirical Design and Analysis Procedure
With advance computational tools and techniques, it is possible to
estimate pavement responses due to mixed traffic, understand temperature and
moisture sensitivities of different pavement materials, and predict different
pavement distress over time. Pavement Mechanistic-Empirical (ME) analysis
procedure helped in determining pavement responses mechanistically and relating
those responses to pavement performances empirically. Layer Elastic Analysis
(LEA) or Finite Element Method (FEM) is commonly used for mechanistic analysis,
whereas the field measured data (e.g., Long Term Pavement Performance Study (LTPP)
Study) coupled with Accelerated Pavement Studies (APT) are used for empirical
analysis. A combination of these mechanistic and empirical approaches
contributed to developing a robust Pavement ME model. However, there are some
drawbacks in the existing ME models. When performing mechanistic analysis, the
pavement layers are assumed to be perfectly elastic and material is isotropic
and homogeneous. However, such conditions never exist in reality. Hence, the
predicted stress/strain responses at the different pavement layers may not be
accurate. As the damage models (a.k.a. transfer functions) are based on the
predicted stress/strain responses, it affects the pavement performance
predictions. Therefore, there is a need for improving the mechanistic model by
utilizing field measured sub-surface pavement responses.
The current ME design algorithm uses
sophisticated tools (e.g., numerical models based on LEA or FEM) for estimating
“mechanistic” pavement response due to vehicle loadings by considering mix
traffic scenarios. Although the vehicle load magnitude directly influences the
mechanistic responses, other factors such as variations in climatic conditions (e.g.,
temperature, moisture) and material properties (e.g., temperature and loading
rate dependency of asphaltic material; stress-dependency of unbound materials) also have significant influence. The pavement models usually consist of “elastic”
asphalt and aggregate base/subbase layers over subgrade “half-space.” The
assumed “elastic” behavior never exists due to heterogeneous material mix and
temperature- and stress-dependencies. Besides, a wide range of loading patterns
(varying loading frequency and magnitude) due to mixed traffic increases
complexity of “mechanistic analysis.” The rutting damage model estimates the
total surface deformation computed from vertical permanent deformations
accumulated in each pavement layer. Such vertical deformation in each layer is
predicted based on the mechanistic analysis. Similarly, the bottom-up asphalt
cracking model is depended upon the horizontal strain responses. Therefore, it
is required to predict layer-specific deformation (or strain) accurately for improving
the reliability of the ME model in performance prediction.
primary objective of this study is two-fold:
- Measurement of stress
and strain responses in pavement layers under various conditions such as different
layer configurations, different subgrade conditions (soft and firm foundations),
different material types (asphalt mix and aggregate base course mix), varying
climatic conditions, and mixed traffic.
- Comparison of field-measured
stress/strain data and numerical model predicted stress/strain responses, and improvement
in the mechanistic model by reducing the bias between the field measurement and
FHWA, FAA, USCOE, State DOTs, and
other transportation agencies already realized the need for accurate pavement
modeling and predicting pavement performance. Therefore, most of these agencies
have their Pavement ME design tool or similar tools. The basic principle of
those tools is related to finding stress/strain at different pavement layers.
It is now well recognized that no
two projects are the same. Due to variations in mechanical properties of
pavement material and foundation conditions, construction practices, each
pavement construction project has unique challenges. As such, these factors
increase the uncertainty in pavement performance prediction. In addition,
agencies are leaning toward using recycled material for reducing construction
cost as well as developing sustainable pavements. In other words, new or materials with unknown characteristics are being used for pavement construction, and it is
challenging to develop numerical models for such materials.
Beside the use of this
research for improving pavement models, sub-surface stress/strain measurements also can be used for the early detection of many pavement distresses, because many pavement distresses originate from underneath the paved surface. Eventually agencies may have the opportunity to save budget costs allocated for
pavement maintenance and rehabilitation with early detection. However, this is not in the scope of
work, and a separate RNS should be developed for looking at this aspect in
In order to achieve the objectives, the following tasks
should be completed:
Task 1: A comprehensive literature review of previous
related research and their findings; In-depth study of challenges and issues
pointed out by past research; detailed study of recommendations made.
Task 2: Selection of Pavement ME design procedure (e.g.,
AASHTOWare Pavement ME, FAA’s Panda-AP, FHWA’s FlexPave); Determination of
inputs required for the numerical model; Development of instrumentation plan to
measure such input.
Task 3: Selection of sensors in cooperation with DOTs; Seek
expert advice from APT owners and users of pavement instrumentation.
Task 4: Selection of sensor installation location and
projects; It is recommended that multiple agencies should conduct this research
so that the data could be collected under different climatic zones, and
different subgrade types.
Task 5: Deployment and integration of sensors and data collection
systems. It is required to collect traffic data as well (preferable Weigh in
motion (WIM) data).
Task 6: Coordinating with the numerical model developers
for comparison between measured and predicted pavement responses.
Task 7: Refining the numerical model and improving the pavement
ME design procedure. The researcher should compare measured stress/strain
responses from instrumented pavement sections with the ME model predicted
stress/strain responses. Adjustment should be made in the predicted stress/strain
responses, and the results should reflect on the damage models. In other words,
the damage models should predict pavement performances accurately.
Task 8: Report preparation;
Make suggestions for improving material characterization inputs and refining
AKG60 (previously known as AFS20) survey results showed that there is a high need for measuring stress/strain response in pavement layers
|Sponsoring Committee:||AKG60, Geotechnical Instrumentation and Modeling
|Research Period:||Longer than 36 months|
|RNS Developer:||Prajwol Tamrakar & Alessandra Bianchini|
|Index Terms:||Field studies, Pavement design, Pavements, Mechanistic-empirical pavement design, |