Download Development of a Methodology and a Tool for the Assessment of Vulnerability of Roadways to Flood Induced Damage

Use of System Dynamics to Understand the
Long Term Impact of Climate Change on Pavement Performance and Maintenance Cost
Rajib B. Mallick, PhD, PE
Professor
Civil and Environmental Engineering Department
Worcester Polytechnic Institute (WPI)
Worcester, MA 01609
Phone: (508) 831 5289
Fax: (508) 831 808
Email: [email protected]
Michael J. Radzicki, Ph.D.
Associate Professor of Economics
Department of Social Science & Policy Studies
Worcester Polytechnic Institute
Worcester, MA 01609-2283
Phone: (508) 831-5767
Fax: (508) 831 5896
Email: [email protected]
Jo Sias Daniel, Ph.D., P.E.
Professor
W171 Kingsbury Hall
33 Academic Way
Department of Civil Engineering
University of New Hampshire
Durham, NH 03824
Phone: (603) 862-3277
Fax: (603) 862-2364
Email: [email protected]
Jennifer Jacobs
Professor
Department of Civil Engineering
University of New Hampshire
Durham, NH 03824
Phone: (603) 862 0625
Fax: (603) 862 3957
Email: [email protected]
Paper prepared for consideration for presentation at the 2014 Annual Meeting of the
Transportation Research Board (TRB) and Publication in the TRB Research Record: Journal of
Transportation Research Board
Total number of words: 6202+ 1 Table+7 Figures = 8,202
Use of System Dynamics to Understand the
Long Term Impact of Climate Change on Pavement Performance and Maintenance Cost
ABSTRACT
An increase in the vulnerability of the nation’s roadway network to changes in climatic
conditions has become an issue of significant concern. Heavy rainfall and high temperatures are
just a few examples of climatic factors that can affect pavement performance and conditions in a
very detrimental way. Some of these effects, specifically on costs associated with constructing
and maintaining roads, may not be significant in the short term, but could become very
significant in the long term. The objective of this study was to present a framework to utilize
system dynamics to understand the long term impact of climate change on pavement
performance and maintenance activity. A system dynamics model was created with available
pavement performance and climate change data, and the effects on various key factors such as
pavement life and maintenance cost were evaluated through simulation. Preliminary results show
that the long term effects of changes in air temperature, rainfall, sea water level rise and number
of hurricanes on pavement performance are significant, and that costs are expected to increase
very significantly (>160% in 100 years) and non-linearly, in the future. Based on these findings,
recommendations are made for obtaining more accurate and reliable data on relevant climate
change factors and for using a system dynamics approach to integrate the multidisciplinary
topics of climate change, pavement design and performance, and economics into comprehensive
studies. 1 INTRODUCTION
Research has clearly shown, and continues to show, that the nation is becoming increasingly
vulnerable to devastating damage to the infrastructure as a result of changing global climatic
patterns – for example floods associated with hurricanes of increasing frequency and rise in sea
water and ground water level (1, 2, 3, 4, 5, 6). The Intergovernmental Panel on Climate Change
(IPCC) has stated that the frequencies of heavy precipitation, as well as rainfall from tropical
cyclones, are likely to continue to increase in this century (7). Evidence from three recent
hurricanes, Katrina, Irene and Sandy, in the US supports the research findings very strongly; the
resulting floods would affect much of the coast and millions of citizens. As projected by the
IPCC, rising sea levels is virtually certain (>99% probability of occurrence) to continue, and
increases in intense precipitation events are highly likely (>90% probability of occurrence) to
become more frequent in widespread areas of the United States (8). As a consequence, along
coastal areas and low-lying river areas flooding will be expected to occur more frequently, such
as the Midwest flooding along the Mississippi river (9).
Pavements constitute the most widely used part of the nations’ infrastructure for the
transportation of people and goods. An increase in pavement temperature as a result of rising air
temperature, reduction in subgrade and hot mix asphalt (HMA) modulus due to high rainfall
(saturation), and flooding due to rise in sea water level (in coastal areas) and hurricanes can
inflict significant damage to pavements, making them unserviceable – causing a major negative
impact on the nation’s economy. The ingress of moisture in pavements due to flooding can also
accelerate the onset of other types of non-water related distress such as rutting and cracking.
The impact of change in climate on pavement performance has been investigated by a few
researchers in the past (10, 11, 12) and most recently, Meagher et al. (13) presented a
methodology to assess the impact of future climate change on pavement deterioration. Instead of
using traditional historic (stationary) data sets, they conducted a mechanistic-empirical analysis
of pavement structures (designed for four sites across New England) with predicted future
temperatures, and evaluated their effects on the rutting performance of the pavements. The effect
on the rutting potential of the HMA layer was found to be significant. The authors demonstrated
and proposed the concept of downscaling regional climatic model (RCM) temperature datasets to
create modified hourly climatic files in the mechanistic empirical pavement design
guide/software (MEPDG/S).
Increasing damage in pavements will definitely lead to more rapid deterioration of pavements,
reduction in pavement lives, and consequent increases in maintenance cost. The U.S. spends
nearly $200,000,000 per day building and rebuilding roads and bridges right now; more rapid
deterioration as a result of climate change will increase this number. Already overburdened
pavement maintenance and repair budgets will be stressed even further and the changing climate
will likely require new approaches to pavement design, maintenance and management. This
paper presents a framework for a methodology that can be used to answer one of the most
relevant questions for the development of new ways to manage and maintain pavements: what
will be the impact of future climate on pavement life and maintenance costs?
2 System Dynamics
System dynamics is an approach that helps develop a strategic view of a “system”, which could
be an industry, society or a nation, by modelling the different parts and simulating the dynamics
of interaction among the different parts. This helps to determine the changes over time, and
hence develop a view which could not be obtained from “spot” studies conducted with either a
few of the critical elements of the system or within the confines of a specific time (second, hour,
day or year) time. One of the most powerful elements of this approach is the ability to “link”
elements and model the interdependencies of the various elements across disciplines (for
example climatic science and civil engineering). Known as causal (or feedback) loops, these
links help to understand the dynamic nature of a problem, and simulate the systems over time.
The important aspect of simulating “over time” is that whereas impacts (such as that of unsustained growth in population or construction) may appear to be linear over a short time period
(say a span of five years), in reality, they may be of exponential nature over a decade or a few
decades. Having the ability to view this change over a long time period is essential for
developing policies for an industry or society or a nation and to make sure that the far reaching
consequences of adoption of these policies are indeed beneficial in the long run. This ability can
help differentiate good policies from bad policies – policies may appear to be “good” in the short
term, but in the long term may have disastrous consequences, and only a proper system dynamics
model can capture it. Another key aspect of the system dynamics approach is the ability to show
the root cause (or causes) of a problem. This is because a good system dynamics model can
include all of the essential elements and their interdependencies and hence, the relational
dynamics.
The steps in the modelling of system dynamics consist of identifying and defining a problem,
developing a dynamic hypothesis, modelling, simulating, and developing policies and evaluating
them. The components of a typical system dynamics model are stock, flow and connectors. Stock
represents a commodity that can increase or decrease in value or number or quantity. Flow is the
rate of increase or decrease in the stock, which can be influenced by “connectors”. A simple
example is that if the amount of available natural aggregate can be considered to be a stock, the
amount of use of aggregate in tons per km of a road can be considered to be a connector, that
dictates the rate of flow, which can be the same as the rate of use of aggregates or can also be
influenced particularly by the amount of recycled aggregates.
System dynamics has been utilized since the nineteen sixties in modeling and simulation of a
wide range of problems, including social, industrial, sustainability, food, population growth and
natural resources. Forrester (1971, 14) introduced the subject through his sustainability model on
world dynamics, and was followed by Meadows (1972, 15) who presented the limits to growth
model. This study has been updated twice since then (16, 17). A model to illustrate the
unsustainability of our civilization was presented by Sterman (2012, 18), which includes growth,
carrying capacity and technology, as well as the delays in regeneration of resource, development
of technologies and adoption of policies. The carrying capacity of the earth system and
detrimental effect of unsustainable growth on the environment has also been investigated with
system dynamics modeled by Radzicki (1995, 19), Bueno (2011, 20) and Bockernmann et al
(2005, 21). System dynamics has been used to investigate the relationship between quality of life
and sustainability by Beck (2011, 22, 2012, 23), whereas Grosskurth (2007, 24) has looked at a
3 case study on regional sustainability in Netherlands. Predictive models for the forecast of fossil
fuel predictions have been developed by Maggio and Cacciola (2012, 25), whereas the link
between resource productivity, consumption and sustainability has been investigated by
Schmidt-Bleek (2008, 26).
Objective
The objective of this study is to develop a framework or methodology to utilize system dynamics
to understand the long term impact of climate change on pavement performance and
maintenance activity.
MODELING AND SIMULATION
The overall pavement analysis approach and its consideration in the model are shown in Figure
1. A system dynamics model linking climatic changes to pavement maintenance and costs was
developed, as shown in Figure 2. The “connections”, in the form of equations, and the
parameters are explained in Table 1. The increase in cost of maintenance was determined on the
basis of increase in thickness of HMA that is required to maintain the deteriorated roads. Since
the “paving for maintenance” depended on the fraction of the “roads needing maintenance”, as
the latter increases, the cost should also increase. However, note that here the term maintenance
has been applied for both maintenance and rehabilitation, since here maintenance is assumed to
be applied for roads that have exhausted their structural performance “lives” as predicted by the
MEPDG/S. There is scope of improvement in this analysis (beyond the scope of this paper) by
differentiating between surface distress related maintenance and structural rehabilitation. In this
case, the rate of use of HMA per two lane-km has been calculated on the basis of an average of
75 mm (between 50 mm of maintenance overlay and 100 mm of rehabilitation layer) thick HMA,
with a density of 2,355 kg/m3.
This paper uses a single pavement cross section with rutting as the primary distress to illustrate
the power of using a system dynamics approach. A two-lane highway, which lies along the coast
of New Hampshire, was selected as the model road. To represent changes in climate, four
factors were considered: 1. Rise in air temperatures, causing a rise in pavement temperature, and
hence decreasing modulus of HMA; 2. Increase in average annual rainfall, which affects duration
for which the subgrade soil can be expected to be at or close to saturation, and hence affects the
modulus of the subgrade soil; 3. Increase in sea water level and; 4. Increase in number of
Category 3 hurricanes, both of which are expected to increase the number of inundation
(flooding) occurrences, and hence cause a lowering of the modulus of HMA and subgrade. The
single pavement structure was analyzed for a range of temperatures and subgrade moduli values,
to evaluate the effect of these climate changes on its rutting life (that is, years to failure by
rutting).
4 Change in Sea Water Level Change in Maximum Air Temperature Change in Maximum Pavement Temperature Change in Number of Category 3 Hurricanes Change in Average Annual Rainfall Change in Number of Inundations Change in Months with 100% or Close to 100% Saturation of Subgrade Soil Temperature Adjusted Minimum HMA Modulus Effective Inundation plus Temperature adjusted Minimum Modulus Effective Subgrade Resilient Modulus MEPDG/S Analysis Rutting Life of a Pavement=f(Subgrade modulus and HMA Modulus) Deterioration Rate Considered in the System Dynamics Model Figure 1. Pavement analysis approach. Note: Data in filled text boxes are from climatic
data analysis, rest are from pavement related literature and/or MEPDG/S analysis
5 Legend
Figure 2. System Dynamics Model
6 Table 1. Equations Used In The System Dynamics Model (explanations within parentheses)
______________________________________________________________________________
Hot_Mix_Asphalt(t) = Hot_Mix_Asphalt(t - dt) + (-Hot_Mix_Asphalt_Use_Rate) * dt
INIT Hot_Mix_Asphalt = 1e+20 tons (this number has been chosen arbitrarily)
OUTFLOWS: Hot_Mix_Asphalt_Use_Rate =
Annual_Km_Paved*Hot_Mix_Asphalt_Use_Per_Km
Number_of_Inundations_per_Year(t) = Number_of_Inundations_per_Year(t - dt) +
(Increase_in_Number_of_Inundations_per_Year) * dt
INIT Number_of_Inundations_per_Year = 0.267+1+0.150
INFLOWS: Increase_in_Number_of_Inundations_per_Year =
10.695*Rate_of_Change_in_Sea_Water_Level+1*Rate_of_Increase_in_Number_of_Flood_Cau
sing_Hurricanes
Roads_Needing_Maintenance(t) = Roads_Needing_Maintenance(t - dt) + (Deterioration Paving_for_Maintenance) * dt
INIT Roads_Needing_Maintenance = 50 (this number has been chosen arbitrarily)
INFLOWS: Deterioration = New_Roads/Average_Pavement_Life_Adjustor
OUTFLOWS: Paving_for_Maintenance =
Roads_Needing_Maintenance*Fraction_of_Roads_Maintained
Maximum_Pavement_Temperature(t) = Maximum_Pavement_Temperature(t - dt) +
(Temperature_Increase) * dt
INIT Maximum_Pavement_Temperature = 58 (maximum temperature at project location)
INFLOWS: Temperature_Increase = 0.78*Rate_of_Change_in_Maximum_AirTemperature
Months_with_100%_Saturation(t) = Months_with_100%_Saturation(t - dt) +
(Increase_in_100%_Saturation_Months) * dt
INIT Months_with_100%_Saturation = 2
INFLOWS: Increase_in_100%_Saturation_Months =
(0.0071*Rate_of_Change_in_Average_Annual_Rainfall)
New_Roads(t) = New_Roads(t - dt) + (Paving_of_Rehabilitated_Road +
Paving_for_Maintenance - Deterioration) * dt
INIT New_Roads = 0
INFLOWS: Paving_of_Rehabilitated_Road = Authorized_Rehabilitations_Kms_Per_Year
Paving_for_Maintenance = Roads_Needing_Maintenance*Fraction_of_Roads_Maintained
OUTFLOWS: Deterioration = New_Roads/Average_Pavement_Life_Adjustor
Annual_Km_Paved = Paving_of_Rehabilitated_Road+Paving_for_Maintenance
Authorized_Rehabilitations_Kms_Per_Year = 2
______________________________________________________________________________
Note: Each “Stock” has an inflow and in some cases, an outflow as well. These inflows and
outflows are controlled by “Flows”, and both Stock and Flows can be affected by/can affect
“Converters”; INIT – Initial; Climatic Factors are explained in the next section
7 Table 1. Equations Used In The System Dynamics Model (explanations within parentheses)
(Continued)
______________________________________________________________________________
Average_Pavement_Life = 25.574+0.000551*Effective_Resilient_Modulus_of_Subgrade_Soil+0.0000452*Effective_Inundation_pl
us_Temperature_Adjusted_Modulus_of_HMA (based on regression equations)
Average_Pavement_Life_Adjustor = If (Average_Pavement_Life>1) THEN (Average_Pavement_Life)
ELSE (1)
Effective_Inundation_plus_Temperature_Adjusted_Modulus_of_HMA =
Temperature_Adjusted_Minimum_HMA_Modulus0.03*Number_of_Inundations_per_Year*INIT(Temperature_Adjusted_Minimum_HMA_Modulus)
Effective_Resilient_Modulus_of_Subgrade_Soil = 26,500*((12Months_with_100%_Saturation_Adjustor_for_Maximum_of_8_months)/12)+8,690*(Months_with_100
%_Saturation_Adjustor_for_Maximum_of_8_months/12)
Fraction_of_Roads_Maintained = 0.1
Hot_Mix_Asphalt_Use_Per_Km = 636 tons (for average of 50 mm maintenance and 100 mm
rehabilitation layers)
Increase_in_Cost_of_Road_Construction_and_Maintenance_% = 100*(Hot_Mix_Asphalt_Use_RateINIT(Hot_Mix_Asphalt_Use_Rate))/INIT(Hot_Mix_Asphalt_Use_Rate)
Months_with_100%_Saturation_Adjustor_for_Maximum_of_8_months =
IF(Months_with_100%_Saturation<=8)THEN(Months_with_100%_Saturation)ELSE(8)
Rate_of_Change_in_Average_Annual_Rainfall = 2.8189 mm
Rate_of_Change_in_Maximum_AirTemperature = 0.024C
Rate_of_Change_in_Sea_Water_Level = 0.0093 m
Rate_of_Increase_in_Number_of_Flood_Causing_Hurricanes = 0.000556
Temperature_Adjusted_Minimum_HMA_Modulus = 158,800316012*Maximum_Pavement_Temperature
______________________________________________________________________________
Note: Each “Stock” has an inflow and in some cases, an outflow as well. These inflows and
outflows are controlled by “Flows”, and both Stock and Flows can be affected by/can affect
“Converters”; INIT – Initial; Climatic Factors are explained in the next section
8 Climatic factors
The climatic data used in this model were obtained from the USGS (27) hosted at the http://regclim.coas.oregonstate.edu (accessed on 7/14/2013). The climate data include two 30year periods that were analyzed, 1970 to 1999 and 2040 to 2069. The GFDL CM2.0 general
circulation model from the U.S. Department of Commerce/National Oceanic and Atmospheric
(NOAA) Geophysical Fluid Dynamics Laboratory (GFDL) USA was dynamically downscaled
using the RegCM3 regional climate model. RegCM3 is a high-resolution atmospheric model
coupled with a physically based model of surface processes. The atmospheric composition in the
RegCM3 simulations match the 20th century and A2 scenario time series that were developed for
the IPCC AR4 (Intergovernmental Panel on Climate Change, 2007) and used in the driving
GCMs. A grid cell located in Massachusetts was used for the analysis (Lat: 42.12946 N, long:
72.10891 W). Note that in the model, two parameters were used for the variables that are
dependent on the climatic factors: an initial value and a rate of change per unit change in the
specific climatic factor.
Temperature: The daily average air temperature record (average and maximum air temperature)
for two 30-year periods were analyzed, 1970 to 1999 and 2040 to 2069. The 30-yr average value
for each day of year was determined for both periods. Average daily temperature differences
were calculated for each day of year then averaged for the entire year resulting in an average
temperature change projected to occur in 70 years. A linear increase over time was assumed. On
the basis of these data an increase in maximum air temperature of 0.024oC per year was
predicted and utilized in the maximum pavement temperature versus maximum air temperatures
model (LTTP model) to estimate a coefficient of change in the maximum pavement temperature
per degree change in the maximum air temperature.
In this initial systems analysis, the projected climate is highly simplified. A single location with
one climate model, one emissions scenario, and a constant rate of change was used to provide
estimates of climate data. Extending beyond a single model and scenario is strongly
recommended because there is considerable uncertainty in the climate factors. Sources of
uncertainty result from unknown relationships between climate and atmospheric concentrations,
seasonal differences, difference among model physics and parameterization, and future carbon
emission which depend on human activities. Relative to recent regional studies that have
evaluated ensembles of models under different emission scenarios, the values used in this study
are not unreasonable. The temperature increases are quite similar to the ensemble average values
but the precipitation values are somewhat higher than those found by Hayhoe et al. (28). Hayhoe
et al. provide insight to the uncertainty that might be expected in the northeastern U.S. They
found approximately a 20% increase in temperature for the higher A1F1 scenario and a 15%
decrease in temperature for the lower B1 emission scenario. The annual temperature change
differed across the nine models reviewed by up to 75%. Furthermore, projected temperature and
precipitation changes differ by location. For example, the CMIP 3 projected temperature
increases are more modest across the southeastern United States and western coast as compared
to other regions (29). Precipitation is projected to increase across the northern U.S., but decrease
in the Southwest.
Rainfall: An increase in average annual rainfall of 2.8 mm per year was predicted using the
same approach as above. Using these rainfall data, soil moisture was modeled using a onedimensional bucket style model (30) to predict the change in the number of times the soil could
9 be expected to be at or close to saturation. The bucket model is a simple model for soil moisture
dynamics, which includes pavement wetting, infiltration into the pavement structure, and
drainage to field capacity. The model was forced using daily precipitation data for the current
and future 30-yr study periods for a 1 m thick subgrade consisting of silty loam soil. Daily soil
moisture was used to estimate an increase in the number of days (and hence months) at which the
soil will be close to saturation.
Sea water level: Sea level rise values were estimated from Kirshen et al.’s (2010,14) study that
determined local Stillwater Elevations (SWEL) for Portsmouth, NH. These local SWEL values
were based on data obtained from the Fort Point station located in Newcastle, NH and from
methodology included in the published work entitled “Coastal flooding in the Northeastern
United States due to climate change” (31). An increase in sea water level of 0.0093 m per year
was estimated. While inundation will vary by specific location, based on the available data, a site
having a current inundation of once every year in the future might reasonably be expected to be
inundated approximately 5 times annually (during astronomical high tides) for the 40 year future
data period. The change in inundation per m rise in sea water level was determined.
Number of category 3 hurricanes: On the basis of currently available historical hurricane data
(http://www.aoml.noaa.gov/hrd/hurdat/) the number of Category 3 or higher hurricanes was
estimated as 0.15 per year for the current 30 year period. Future 30 year period were estimated to
be 0.167 per year, using Done et al.’s (2012, 32) estimated 10 to 15% increase in the frequency
of high-impact events in future climates. For each occurrence of the hurricane, an occurrence of
inundation was considered for the pavement.
Pavement properties
Two critical pavement properties were considered for assessing the impact of the changes in
climatic factors: resilient modulus of the subgrade soil and HMA. An effective modulus of the
subgrade soil was calculated, considering the months at which the soils could be considered to be
dry and those at which the soil could be considered to be fully saturated. A 100% modulus was
assumed for the soil in the dry months, whereas for the saturated months, a modulus of 50% of
that at dry condition was considered, on the basis of data and recommendations given in
NCHRP, 2004, Appendix DD-1:Resilient Modulus As Function Of Soil Moisture-Summary of
Predictive Models (33). Finally, a weighted average modulus was calculated on the basis of the
moduli and the corresponding months of saturation.
Using the MEPDG, the minimum surface HMA moduli were determined for cites with different
high temperatures. A linear model relating maximum pavement temperature (calculated, as
described earlier, from maximum air temperature) to minimum modulus was determined. This
modulus was then adjusted for the effect of water through inundation. Note that this may not be
an accurate approach, but it has been used here as a practical approach to illustrate the use of a
system dynamics framework.
In the case of the effect of inundation on the modulus of HMA, it has been concluded (34) that
the duration of the inundation period does not seem to have an effect on the damage of the
pavements. Therefore, instead of inundation period, the number of cycles of inundation
(comparable to number of cycles of conditioning in the laboratory) was assumed to have a
significant effect on the modulus of the HMA. For example, a study (35) showed that the tensile
strength ratio of a moisture susceptible mix dropped from 0.9 to 0.6 from one moisture
conditioning cycle to ten, which is a reduction of 0.03 times the strength per cycle of inundation.
10 This relationship was utilized to represent the change in HMA modulus as a function of
inundation cycles.
Pavement performance analysis
The temperature-pavement life and subgrade modulus-pavement life relationships were
developed using the pavement structure for a two lane highway in coastal NH. The MechanisticEmpirical Pavement Design Guide (MEPDG) software version 1.1 was used to analyze the
pavement structure under different climates and subgrade modulus values. The pavement
structure consisted of 3 inches of asphalt concrete (12.5 mm NMAS, PG 64-28) over 16 inches
of gravel, 24 inches of sand, and an A-1-b subgrade. Modulus values and other MEPDG inputs
were obtained from NHDOT. Default values were used when specific information was not
available. (Note that the thicknesses and moduli values used in this paper are given in US
customary units as they have been used in the MEPDG software).
For this particular pavement structure and traffic loading, rutting was the primary cause of failure
in the pavements with increasing temperatures and decreasing subgrade modulus values. There
was less than 10% alligator cracking predicted and the amount of longitudinal cracking decreases
with increased temperature and decreased subgrade modulus. Therefore, pavement failure was
defined as 0.75” of total rutting for the purposes of this project.
The temperature-pavement life relationship was developed by running the MEPDG analysis of
this pavement structure using climate files from cities along the eastern US coast. The number
of months to a total rut depth of 0.75” was determined from the MEPDG output. This time to
failure (defined as 0.75” total rut depth) was converted to years and then plotted against the
maximum pavement temperature determined for that location using LTPPBind. A linear
regression was performed to determine the relationship between years to failure and pavement
temperature.
To determine the subgrade modulus-pavement life relationship, MEPDG analyses were
conducted using the NH climate and a range of reduced modulus values. The original modulus
value for the subgrade was 26,500 psi. Modulus values of 95%, 90%, 80%, and 70% of the
original value were used for MEPDG analysis. The number of years to failure (defined as 0.75”
total rut depth) was determined from the MEPDG output and plotted against the subgrade
modulus percentage. A linear regression was used to determine the relationship between years to
failure and subgrade modulus.
Results - Climate change and impacts
The model was run for a time span of 100 years, and the results for the example pavement are
shown in Figures 3 through 7. As expected, because of predicted changes in the climate, the
maximum pavement temperature, the number of 100% saturation months and number of
inundations do increase over time, and result in reduced effective subgrade modulus and
minimum HMA modulus, and average pavement life. For the modeled pavement, the maximum
temperature increases by 1.8C, the number of inundations increases by 10, and the number of
months at which the soil is expected to be at or close to saturation increases from 2 to 4, over a
time span of 100 years. The corresponding change in effective resilient modulus of the subgrade
soil is from 23,500 to 20,500 psi, and that for the modulus of the HMA is from 625,000 to
11 400,000 psi.
Figure 6 shows the most important finding - the change in average pavement life over the years,
decreases from 16 to 4 years, over a time span of 100 years. The net effect of all of these changes
is reflected in Figure 7, which shows the percent increase in cost with and without considering
the effect of climate change. It can be seen that due to maintenance the overall cost decreases
initially, but starts increasing after 20 years for both cases. The case with no consideration of
climate change impact is a linear increase, with a positive increase only after 45 years, whereas
for the case with the climate change impacts it is a non-linear increase with a positive increase
after 35 years. At the end of 100 years, the scenario without climate impact increase is below
60% whereas the one with the climate impact is above 160% - a significant difference. Note that
in these calculations the effect of rising costs has not been considered. Note that in this paper an
asphalt mix overlay has been considered as the maintenance activity.
60.0
59.8
59.6
59.4
59.2
59.0
58.8
58.6
58.4
58.2
58.0
57.8
12
11
10
9
8
7
6
5
4
3
2
1
0
0
20
40
60
Time, Years
80
Number
Temperature, C
While these results are dependent on the specific pavement and traffic used and models that have
been developed in this study, and will change as more accurate models are applied, it is clear that
this reduction in life is caused by increasing damage that would result from detrimental effects
on key material properties (such as modulus) caused by higher temperature, rainfall, sea water
level and more Category 3 hurricanes. The key observation is that the changes in these factors
(which are considered to be critical indicators of climate change) do cause a significant change in
performance and deterioration of pavements. To protect our future generations from the burden
of these expected detrimental changes, work is needed to improve pavement design and
construction, to make pavements more resistant to the effects of climate change.
100
Maximum Pavement Temperature
Number of Inundations Per Year
Figure 3. Plot of number of inundations and maximum pavement temperature versus time
12 24,000
23,500
23,000
22,500
22,000
21,500
21,000
20,500
20,000
4.0
3.0
2.0
1.0
0.0
0
20
40
60
Time, Years
80
100
Resilient Modulus, psi
Number of Months
5.0
Months with 100% Saturation
Effective Resilient Modulus of Subgrade
Figure 4. Plots of 100% saturation months and effective resilient modulus of subgrade
versus time
Minimum Modulus of HMA, psi
675,000
600,000
525,000
450,000
375,000
300,000
225,000
150,000
75,000
0
0
20
40
60
Time, Years
80
100
Figure 5. Plot of minimum HMA modulus versus time
13 Pavement Life, Years
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
0
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Time, Years
80
100
Figure 6. Plot of average pavement life versus time
200
Increase in Cost, %
160
120
80
40
0
-40
-80
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95100
Time, Years
With Climate Change Impact
Without Climate Change Impact
Figure 7. Increase in cost of maintenance, with and without the consideration of climate
change impact on pavement performance
14 However, with rising costs and dwindling budgets, resources that are needed for such
improvements are becoming scarce. The proper way to tackle this situation is to optimize
pavement performance against the expected changes – and to do so, one needs accurate and
reliable data regarding climate changes, particularly those changes that will have significant
effects on pavement performance. While significant improvements in pavement design (both mix
and structural) and construction (rehabilitation and maintenance) have taken place over the
recent years, there is a big gap in understanding climate change and its effects on pavement
performance in the future. For sustainable development, this information must be obtained
through more collaborative research between climatologists and pavement engineers. Also,
available climate, pavement and economic (as well as environmental) data must be integrated in
system dynamics models and utilized for long term predictions. Such modeling and simulation
will be crucial for investigating the individual and combined effects of multiple factors (which
are not from the same discipline) and identifying the more critical ones. Furthermore, once the
critical factors are determined, critical levels of these factors can be identified, that can trigger
non-linear response of the key outputs, such as pavement life and costs of maintenance. Unless a
holistic evaluation is made with system dynamics, it will not be possible to optimize our
available resources to design and construct sustainable pavements.
CONCLUSIONS AND RECOMMENDATIONS
On the basis of this study, the following conclusions can be made.
1. System dynamics provide a practical way of evaluating the long term effect of climate
change on pavement performance
2. A framework of using system dynamics to integrate climate change, pavement
performance and economics of pavement maintenance has been proposed
3. Preliminary modeling and simulation show that the long term effects of changes in air
temperature, rainfall, sea level rise and number of Category 3 hurricanes on pavement
performance are significant
4. For the expected changes in climatic conditions, a significant reduction in stiffness
properties of both subgrade and HMA can be expected
5. As a result of reduction in stiffness of the different layers, faster deterioration and a
drastic reduction of average life of pavements are expected in the future
6. As evident from the evaluation of increase in cost of maintenance as a result of climate
change, costs are expected to increase very significant (>160% in 100 years) and nonlinearly, in the future.
7. The significant and early increase in cost is not apparent from predictions that do not take
climate changes and their effects on pavement performance into consideration.
Recommendations for Future Work
The work that is reported in this study clearly points out the necessity of the following.
1. To obtain more accurate and reliable data regarding relevant climate change factors, that
are specific to regions, particularly those that are expected to be more vulnerable, such as
coastal regions
2. To use system dynamics approach to integrate the multidisciplinary topics of climate
change, pavement design and performance, and economics into comprehensive studies 3. To develop better models and refine the accuracy of predictions 15 4. To conduct region or site specific modeling and simulations for accurate prediction of
impacts of climate change on pavement performance, disruption of transportation
networks and associated economic factors These recommended actions will allow us to have a clear idea about future needs, prepare
research plans for developing appropriate materials and methods, identify vulnerable areas, plan
properly for appropriate budget allocations and optimize the spending of our precious tax dollars.
ACKNOWLEDGEMENTS
The authors gratefully acknowledge the help of Dr. Paul Kirshen in writing this paper. The
ICNet project funded by the U.S. National Science Foundation research grant (CBET-1231326)
facilitated this collaboration.
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