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QUALITY MANAGEMENT OF PAVEMENT
CONDITION DATA COLLECTION
SUMMARY This synthesis reviews the quality management practices being employed by public road
and highway agencies for automated, semi-automated, and manual pavement condition data
collection and delivery using in-house staff and contracted services. Although the review
focuses on the collection of distress data at the network level, it also covers smoothness, fric-
tion, and structural capacity data collection processes, and some elements of current quality
management practices for project-level data collection. The document is a compilation of
information from an extensive literature review, a survey of state and provincial practices and
data collection service providers, and follow-up communications with a select number of state
agencies. The survey was conducted electronically using interactive web-based commercial
software. Fifty-five agency responses, covering 46 states and 9 Canadian provinces, were
received. A shorter version of the survey was sent to private data collection service providers;
six responses from service providers were received.
Many transportation agencies are developing procedures and guidelines for managing the
quality of pavement data collection activities to ensure that the data collected meets the need
of the pavement management process. Pavement data quality is receiving increased attention
because: (1) data quality has a critical effect on the pavement management business decisions,
(2) data collection is one of the most costly parts of operating a pavement management system
(PMS), and (3) quality management is necessary to ensure that the collected data meets the
requirements of the PMS. The review of practice confirms that the type and quality of pave-
ment condition data required for network- and project-level decision making is generally
different. Whereas smoothness and distress are collected at the network level by most depart-
ments of transportation (DOTs) (98% and 95% of respondents, respectively), deflections and
friction are collected mostly at the project level.
The literature suggests that the most efficient way to achieve high-quality pavement con-
dition data collection services is to adopt a comprehensive, systematic quality management
approach that includes methods, techniques, tools, and model problem solutions. Although the
concepts of quality, quality management, quality control, and quality acceptance have been
extensively used in manufacturing industrial processes, these same principles, methods,
and tools have not been systematically applied to pavement data collection. This is partially
because in these services the "product" is not clearly known and the ground truth or
reference value often is difficult to determine.
Over the last decade, there has been an increase in the use of data collection service
providers for collecting both network- and project-level pavement condition data. This trend
has been fueled by a combination of three factors: (1) an increase in demand for timely
quality data to support pavement management decisions; (2) reductions in the public sector
staff; and (3) availability of more sophisticated equipment that can collect large quantities
of data quickly and efficiently, but that are often expensive and complex to operate. How-
ever, although most agencies have evaluated this possibility (81%), they still collect most
of their data using in-house staff. Pavement distress and smoothness data are the data types
that are most frequently outsourced (by 43% and 38% of the agencies, respectively). The
main factor considered for making the decision to outsource the pavement data collection is
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cost-effectiveness, followed by limitations of the in-house data collection capabilities, and
the amount of data that needs to be collected. More than two-thirds of the agencies that
have outsourced at least part of the data collection indicated that data collection outsourcing
was a positive step.
Independent of the mechanism used to collect the data, in-house or through a service
provider, a complete quality management system includes a clearly documented quality man-
agement plan. This plan is the "umbrella" document under which individual quality activities
are conducted and it includes a clearly documented quality control plan, detailed quality accep-
tance procedures, and established guidelines to monitor the entire process, with timelines, mile-
stones, and evaluation criteria. Most plans include activities that are conducted before, during,
and after data collection production. Approximately one-third of the DOTs (35%) already have
a formal plan and an additional 27% are working on developing such a plan.
The main techniques used for pavement data quality management are: (1) calibration
and verification of equipment and/or analysis criteria before the data collection, (2) testing
of known control or verification sites before and during data collection, and (3) software
routines for checking the reasonableness, consistency, and completeness of the data. Other
promising techniques that are not yet as commonly used include the analysis of time-series
data, both at the project and network level; independent verification and validation of the
pavement condition data by an independent quality auditor; and use of blind site monitor-
ing during the production quality acceptance process. The various techniques are included
in the quality control plans, quality acceptance procedures, and/or independent assurance
processes.
Quality control includes those activities needed to assess and adjust production processes
to obtain the desired level of quality of pavement condition data. These activities are defined
in a quality control plan and include checks on the equipment used to collect the data, the per-
sonnel responsible for the data collection, and the data collection process itself. The purpose
of the quality control plan is to quantify the variability in the process, maintain it within
acceptable limits, identify the source of variability that can be controlled, and take the nec-
essary production adjustments to minimize the "controllable" variability. In general, sources
of variability for pavement condition data collection can be related to equipment used, oper-
ation (including rater/operator training and skills), processing of the data collected, environ-
mental conditions, and shape and condition of the pavement surface.
Approximately two-thirds of state and provincial highway agencies (64%) have a formal
data collection quality control plan or require the service provider to develop such a plan. All
pavement data collection service providers indicated having a formal data collection quality
control plan. The main tools and methods used for quality control are: (1) calibration and
verification of equipment and methods before the data collection (used by 94% the agencies),
(2) testing of known control segments before data collection (94%) and during data collection
(81%), and (3) software routines for checking the reasonableness (57%) and completeness
(55%) of the data.
Quality acceptance includes those activities conducted to verify that the collected pave-
ment condition data meet the quality requirements and ensure that the final product is in
compliance with the specifications. It applies to the pavement condition data collected by the
agency and by service providers. Approximately half of the state and provincial highway
agencies (48%) have a formal quality acceptance plan. In the case of data collection contracts,
quality acceptance is often also linked to payments.
Important aspects of the quality acceptance plan include the establishment of acceptance
criteria (data accuracy and precision, and reliability) and an appropriate sample size neces-
sary to validate that the data meet these criteria. The main tools and methods used for qual-
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ity acceptance by state and provincial highway agencies are: (1) calibration and verification
of equipment/methods before the data collection (used by 80% the agencies); (2) testing of
known control segments before data collection (73%); (3) testing of known control or veri-
fication segments during data collection (71%); and software routines that check the rea-
sonableness (71%), completeness (61%), and consistency (50%) of the data, and compare
the production data with existing time-series data (50%). A small percentage of agencies
(21%) currently use blind control sections for quality acceptance. Some agencies are also
starting to use geographic information system-based tools to support the quality acceptance
process.
Some agencies also incorporate an independent assurance by using a third party to resur-
vey or reevaluate a sample of the data and compare the results with the production results.
Typically, the techniques and approaches used for this independent verification are simi-
lar to those applied for the quality acceptance. Although quality engineering practices gen-
erally recommend the inclusion of external audits in the quality management plan, only
4% of the agencies surveyed use independent verification for quality control and 12% for
quality acceptance.
The implementation of the discussed approaches for quality management is illustrated in
four case studies that document the data management practices of state and provincial DOTs.
The review included an agency that conducts most of the data collection in-house and three
agencies that contract most of the network-level pavement condition data collection with
service providers. The first case, the Maryland State Highway Agency, provides an example
of an agency that collects data in-house using an automated system. Its' quality control plan
includes control site testing and checks to identify abnormalities and verify that all fields are
processed and saved. The quality acceptance is conducted by a quality assurance auditor, who
checks the data management spreadsheets; verifies that the data are complete, saved, and
backed-up; and rechecks a random sample of 10% of the data collected. Time-series com-
parisons of the percentage of the network in acceptable condition are used to flag potential
data quality problems.
The other three cases cover agencies that collect data using service providers. The Vir-
ginia DOT case provides an example of an agency that has established a well-documented
systematic process for quality management. This process includes an independent valida-
tion and verification of a 10% random sample of the pavement deliverables. Among other
criteria, the acceptance plan requires that 95% of the data checked fall within plus or minus
10 index points of the data collected by a third-party validation and verification rater. The
Oklahoma DOT case illustrates the use of very detailed automatic data quality assurance
checks for quality acceptance. Finally, the British Columbia Ministry of Transportation
quality management procedures provide an example of the use of blind control sites, which
are manually surveyed in advance. These blind sites are situated along various highways in
each region.
The review of practice showed that there are some issues that would benefit from further
research. For example, it is clear that the type of data collected and the approaches followed
to manage the quality of the data collection process vary significantly among agencies.
Although there appears to be common agreement that data quality is important for effective
pavement management, several agencies still do not have formal quality management plans.
The adoption of automated/semi-automated data collection technologies has created chal-
lenges for the roadway agencies that verify that the new equipment results are consistent
with the historical practices. There are also problems with the consistency of their location
referencing systems, especially as the agencies migrate from linear to geodetic methods.
Changing business practices, such as the reassessment of the highway PMS or the adoption
of mechanisticempirical pavement analysis and design methodologies, are also influencing
the pavement condition data detail and quality requirements.
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Topics that were identified for future research include the identification and demonstra-
tion of "best quality management practices," investigation of the effect of emerging pave-
ment data collection technologies on the quality of the pavement management decisions, and
cost-effectiveness analysis of the implementation of different quality management tools,
methods, and programs. These efforts could be used to develop an AASHTO Standard Practice
that provides "generic" quality management, control, acceptance, and independent assur-
ance plans that agencies can customize for their specific needs. The development of a workshop
or training course on quality management of pavement data collection could also be beneficial.
