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Page Design of a Long Term Pavement Monitoring System for the Canadian Strategic Highway Research Program
Section 1 OVERVIEW OF THE
PAVEMENT MONITORING PROGRAM AND ITS CONTRIBUTION TO
PAVEMENT DESIGN AND ASSET MAINTENANCE 1.0 OBJECTIVES OF THE REPORT This report has a number of objectives.
In the narrowest sense, it is a report detailing how to
measure the life cycle cost of a particular pavement
section given the information obtained from long term
pavement performance monitoring. In a broader sense, this
report sets forth an integrated, comprehensive pavement
monitoring, performance prediction, and pavement
management system. In particular, we have endeavored to
begin with the long term pavement monitoring gameplan of
the Strategic Highway Research Program and augment and
modify it so that it meets Canadian needs. Our objective
is to develop for Canada an immediately workable system
that can be used to manage Canadian pavement assets
effectively beginning immediately. 1.1 RELATIONSHIP OF OUR
FINDINGS TO THE STRATEGIC HIGHWAY RESEARCH PROGRAM LONG
TERM PAVEMENT PERFORMANCE MONITORING PROGRAM The analysis described in this report
has considered carefully and comprehensively the prior
design work done by the long term pavement performance
(LTPP) program of the Strategic Highway Research Program
(SHRP) in the United States. We have borrowed ideas
directly from the SHRP long term pavement performance
project design when we believe those ideas increase the
probability of success for the Canadian program. However,
we have superseded those aspects of the SHRP long term
pavement performance program that we believe compromise
its prospects for success. In part, we have used SHRP as
a "straw man" and have attempted to improve it
systematically. Our analysis has identified two serious
flaws in the SHRP design, flaws that could obviate the
ultimate success of the program. Our report discusses
those flaws at some length and advances specific
remedies. It is not our intent to denigrate the SHRP long
term pavement performance program. On the contrary, we
recognize the difficulty of the challenge facing SHRP and
the seminal contributions SHRP has made. However, given
our mission to design an integrated, coherent, workable
long term pavement performance program for Canada, we
have sought to borrow from the SHRPLTPP program where
appropriate yet to supersede those portions of the
SHRPLTPP program we believe to be defective,
unworkable, or limiting. We reiterate that our paramount
objective is to design a coherent and integrated long
term pavement performance program for Canada. We will
draw from the United States SHRP program as we deem it
appropriate for Canada. We believe that certain aspects of SHRP
will be adequate in supporting prudent and effective long
term pavement monitoring and delivering key information
to decision makers. However, we have identified and
proposed alternative approaches for two areas of the
SHRPLTPP program we believe to be problematic: (1.) the
statistical methodology proposed and (2.) the limitations
that the proposed statistical methodology will impose on
pavement management systems that should use the
SHRPLTPP information. We have been somewhat surprised at the
rather cursory and presumptive discussion in the
SHRPLTPP documentation related to the statistical
methodology that will be used to process the long term
pavement performance data. The challenge of monitoring
and managing pavement assets if far too complex and too
important to accept without serious inquiry or design
modification the use of standard, prepackaged classical
statistical techniques. We are confident that "plain
vanilla" classical statistical techniques will not
work and may in fact prove to be an Achilles heel for
SHRPLTPP as presently conceived. This report gives a
good deal of attention to explaining the classical
statistical approach, indicating its limitations with
regard to the problem at hand, and outlining and
proposing a more advanced, more stateoftheart
methodology that overcomes these shortcomings and
maximizes the probability of success for Canada. Coincidentally, we have also been
surprised at the lack of focus in the SHRP documentation
on the question: Who are the customers of long
term pavement performance monitoring? How will the
information gained from long term pavement performance
monitoring benefit those customers? In what way
will those customers be better off as a result of
receiving an expensive stream of long term pavement
deterioration information over the next fifteen or more
years? We have given a great deal of attention to
answering these questions in this report, and we believe
we have a workable, positive answer. Our analysis of
these questions revolves around the notion that better
information has zero value until and unless people use
that information to make different decisions than they
would otherwise make. Information has value only if it
motivates changes in people's decisions. Long term
pavement performance data has value only if it changes
the way pavements are designed and maintained. If we accept the proposition that
pavement performance monitoring and pavement decision
making are intimately coupled, it is clear the design of
pavement management systems must be intimately conjoined
with the design of long term pavement performance
monitoring. In other words, the design of pavement
management systems to be used by provinces and states has
profound implications for the design of the long term
pavement performance monitoring program designed to
provide data to those systems. Conversely, the type of
data assembled and the way it is manipulated has major
implications for the design of workable pavement
management systems. Long term pavement monitoring cannot
be designed independently from pavement management, nor
can pavement management be designed independently from
long term pavement monitoring. We have given a good deal of attention
to the coordinated design of an integrated pavement
monitoring, performance prediction, and pavement
management system. We view the problem as a system problem
amenable only to a system solution. It is not a problem
that can be solved one piece at a time, gathering data to
support in some vaguely defined way some vaguely defined
pavement management system of uncertain specification. In
this report, we have specifically tailored our proposed
pavement monitoring program and subsequent statistical
manipulation so that it can support probabilistic as well
as deterministic pavement management systems. 1.2 KEY OBJECTIVE: TO SUPPORT
IMPROVED PAVEMENT CONSTRUCTION AND MAINTENANCE DECISIONS Much of the SHRP documentation from the
United States articulates the objective of the long term
pavement monitoring program as "recalibrating the
AASHTO design equations" and "extending the
life of pavements by 10 percent or more." The tacit
assumption in such statements is that long term pavement
monitoring only has value to the pavement design
decision. We would sharply dispute this tacit assumption,
for we feel it dramatically understates the benefits of
long term pavement performance monitoring and statistical
inference. Equally importantly in our view, and largely
unstated and/or neglected in the SHRP documentation, is
the fact that maintenance as well as design decisions
will be guided by the results of the long term pavement
performance monitoring program. To ignore maintenance
while considering only design is to systematically
understate the benefits of the long term pavement
monitoring program and to perhaps omit important design
considerations that can have tremendous value in guiding
maintenance decisions. Our approach painstakingly seeks
to consider design and maintenance decisions as an
integrated asset management unit. To illustrate our point by analogy, for
a 767 jet would the service life be more affected by
maintenance actions undertaken throughout the life of the
jet or by the initial design? For a Mercedes Benz, would
the service life be more affected by maintenance
undertaken throughout the life of the car or by the
initial design? Is the service life of the Golden Gate
Bridge more strongly a function of the initial design or
of the intensive maintenance strategy undertaken since
the bridge was erected? Empirical observation across a
range of assets over a range of industries suggests that
the service life (and life cycle cost) of an asset is an
inextricably intertwined function of both the initial
design of the asset and the maintenance strategy applied
over the life of the asset. Empirical work also suggests
that people tend to systematically underestimate the long
term benefits of routine maintenance and underestimate
the costs of rehabilitation and refurbishing. While
people pay lip service to the adage "A stitch in
time saves nine," people do not behave according to
the adage to a sufficient degree. To concentrate long term pavement
performance monitoring on initial design decisions as the
SHRPLTPP documentation implies is to dramatically
understate the value of performance monitoring and the
value delivered to pavement users from the program.
Performance monitoring will give invaluable information
that helps people make better maintenance decisions as
well as better design decisions. However, in order to
take advantage of such benefits, we must give careful
attention to implementing pavement management systems
that can accept as input the long term pavement
monitoring data and can prudently and correctly
coordinate design and maintenance decisions. The approach
we have designed and outlined here is intended to offer a
better integrated design and maintenance strategy one
that is continually guided by long term performance data
as it is assembled. In brief, long term pavement
performance monitoring should not be viewed strictly as
"science expedition." The mission is not one of
scientific inquiry, although scientific inquiry promises
to be a byproduct of the design we will set forth. We
recommend a colder, harder, more pragmatic view of long
term pavement performance monitoring. Our view is that
the objective of long term pavement performance
monitoring is to assemble better information to support
better decision making in the field and deliver that
better information to decision makers so they can make
better decisions immediately. Highway users should not be
asked to wait until all the information is in and all the
science can be checked out and debated in academic
circles or research laboratories. Highway users need and
deserve the best possible service now. 1.3 SMALL SAMPLE SIZE PROBLEMS
WILL BESET THE CANADIAN LONG TERM PAVEMENT PERFORMANCE
PROGRAM (CLTPP) There is no way to escape the small
sample size problem in long term pavement monitoring; it
is endemic. The care, cost, and time required for long
term pavement monitoring program necessarily restricts
the number of sites that can be considered. If we wish to
estimate say fifteen or twenty parameters that
prospectively affect long term pavement performance,
classical statistical methods would require at least
fifteen data points, i.e., fifteen sites per parameter,
to achieve a bare minimum of statistical significance. To
achieve a desirable level of statistical significance,
one would require systematic, sustained, accurate,
longterm monitoring of some 225300 sites over the
next fifteen or more years for each pavement type.
Monitoring at such a broad scale is beyond the
anticipated scope of the program and probably beyond the
patience and financial resources of the political bodies
that would be asked to provide fifteen or more years of
continuous funding. Furthermore, as we shall continually
reemphasize here, the world of pavements and pavement
monitoring is far from ideal, necessitating the maximum
conceivable number of sites. The complexity of the
pavement situation all but guarantees that pavement
monitoring faces perhaps the worst possible statistical
situation. The United States program has attempted
to circumvent the small sample size problem in two ways.
First, the U.S. program will monitor presently existing
sites that are partway through their ultimate life.
Second, the U.S. program will consider several different
types of roadways The approach we will recommend
overcomes this difficulty by squarely addressing the
small sample size problem. We have appealed to a rather
unique technique for conquering the inevitable small
sample size problem, a technique called Bayesian
statistics. Much of this report describes the
classical statistical approach (which incidentally is an
integral part of the proposed SHRP long term pavement
performance program), identifies its shortcomings, and
shows how the Bayesian statistical approach overcomes
those shortcomings with regard to the needs of long term
pavement performance monitoring. We also give special
attention to showing why the data acquired by the long
term pavement performance program can be identical
whether the classical statistical approach or the
Bayesian statistical approach is selected. The difference
lies not in the nature of the data gathered but rather in
the calculations that are made using that data. In short,
the "magic" is in the data processing, not the
data assembled. 1.4 LONG TERM PAVEMENT
PERFORMANCE MONITORING MUST BE DESIGNED TO SUPPORT BETTER
PAVEMENT DESIGN AND MAINTENANCE DECISIONS The flavor of the SHRPLTPP documentation revolves around the notion, indeed the faith, that long term pavement monitoring can give · the right data; data that is
carefully controlled, uncorrupted, and understood. · the right science. That is,
the right data will allow us to identify the
deterioration forces at work. For example, the right data
will allow us to make the right calibration of the AASHTO
design equations. We doubt whether any monitoring program
for an asset as heterogeneous, confounded, and complex as
pavement can ever generate the right data and the right
science. Too much can go wrong. Too much confounding can
occur. Too much measurement error can occur. There are
too many possible combinations of causal or independent
variables. There are too many prospective combinations of
design and maintenance alternatives to test them all to a
sufficient degree. The objective by which we have
developed our design for Canada states the objective of
long term pavement monitoring slightly differently. The
system we have designed includes a pavement monitoring
system and a coordinated and compatible pavement design
and management system that · help people make better
pavement design and management decisions in the field. and · do so beginning with the very
first data from the program and continuously improving
thereafter. We have commented in our own prior
pavement design and management work that the only
legitimate objective of gathering better data is to make
better decisions. The only benefits people can claim from
long term pavement performance monitoring are those
embodied in future pavement design and management
decisions. Gathering better data so that people feel more
comfortable about the underlying deterioration science or
so they can feel more comfortable doing the same thing
they were going to do anyway is not a legitimate reason
to fund a program as ambitious and expensive as long term
pavement performance monitoring. Gathering better data is
justifiable only insofar as that better data leads people
to make different and better decisions sooner than
they otherwise would. We believe the CLTPP program has the potential to help people make better decisions than they otherwise would, starting immediately with the inception of the program. With the proposed design, CLTPP will not have to wait ten or more years for "sufficient data." To achieve immediate impact, however, requires the specific design features proposed here, i.e., the specific extensions of the SHRPLTPP program we have conceived. It requires the integration of Bayesian statistical methods with quantitative pavement management techniques. There must be a clear and unequivocal path from long term pavement monitoring through statistical inference through pavement management decision making to the field where different and more effective strategies are executed. As the remainder of this report will show, our design provides this clear and unequivocal path.
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