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Advanced Quality Systems: Guidelines for Establishing and Maintaining Construction Quality Databases

CHAPTER 4. DEVELOPMENT OF MODEL DATABASE

4.1 PURPOSE AND OBJECTIVE OF THE DATABASE

4.1.1 Introduction

The guidelines for the development of a construction quality database were developed after a thorough assessment of the various uses within the agency and benefits it can provide. Information collected from the survey responses and the in-depth interviews provided insight into the multiple databases maintained by the agencies, the data categories stored, the analysis performed, links to other SHA databases, and the reports generated. It is evident that the nature of information collected, the level of detail in the process, and the length over which this information is retained differ significantly from agency to agency. In each of the agencies surveyed, the systems have been established with certain goals that are State-specific, and most represent an as-built database. Furthermore, the current systems differ considerably in their architecture, purpose, data collection and access procedures.

Recommendations from this study will provide a set of comprehensive guidelines that can help SHAs upgrade and maintain more systematic and efficient database systems to manage their information. It is not expected that an agency will develop a new system based on these recommendations; instead, these recommendations will be useful for agencies desiring to upgrade or improve their database system to make it more useful to the agency. The recommendations describe the mechanics of developing efficient databases and cover basic database functions.

It was also found during the surveys that the States have been collecting extensive data for several years, but do not often or necessarily utilize it to draw valuable conclusions or to perform advanced analysis for broader use. In other words, agencies appear to be “data rich and information poor,” as stated by one agency. Another interesting statement made was that agencies are “mostly focused on entering, not retrieving data.” One main reason for this status-quo is that there are no dedicated positions assigned with the responsibility to oversee an overall efficient management of the various databases. This position will involve significant inter-departmental coordination, and the selected individual for this role should have a wide understanding of the functions and needs of all departments involved.

During the surveys, pavement management staffs in most agencies expressed an interest in (and recognize the benefits of) correlating construction quality to pavement performance data, or linking it across other databases, such as cost data or safety data. They realized that they were not “closing the loop” to show how improvements in specifications and construction affect performance. The major limitation in implementing this type of an analysis is the fact that the databases have been established with different referencing systems leaving no common tie to match all data collected on a given pavement section.

Databases also have seen very limited use in cost-benefit analysis, so important for agencies in establishing policies or in making changes to their specifications/practices. For instance, States implementing PRS (where all AQCs are measured in the same sublots/lots) will perhaps be able to correlate increase in initial construction cost against increase in service life. However, without a further link to life-cycle costs, the increase in design life cannot be translated to a decrease in life-cycle costs. An efficient database system should provide agencies an opportunity to clearly assess/appraise the benefits they receive in making changes to their construction practices. Likewise, improvements in safety and accident records can also be traced through appropriate linking of individual databases to specific sections of pavement.

The guidelines provided in this report were therefore developed based on a thorough evaluation of current and future needs of an agency, while not using any specific agency’s database system as the basis for the recommendations.

4.1.2 Agency and Contractor Needs

A construction quality database has several users, including both agency and contractor personnel. Needs of the users of the database were fully considered while formulating the features and components of a model construction quality database.

The SHA and the contractor have the following minimum requirements from a database:

• User-friendly interface and configuration.
• Access by several individuals and departments, such as materials testing lab, construction engineers, field inspectors, designers, management, contractors, subcontractors, and researchers who might all operate from different locations.
• Different levels of access security and operational privileges, such as viewing data, entering data, modifying data, generating outputs, analyzing data, etc., depending on role in the organization or role in the project (inspector vs. contractor).
• The ability to make “user group” assignments; users can belong to multiple groups.
• Audit and tracking information to trace users and their activities.
• Offline use of input modules or linkage to wireless devices to aid in timely data entry and processing.
• System for logging and tracking of material samples, related tests, and results.
• Capability to store construction details and contractor activities for each lot such as PCC curing practices, thickness of HMA lift in each paving operation, and traffic opening time, etc.
• Aid in decision-making for QA, PRS, and warranty projects.
• Ability to perform fundamental and routine analysis for QA operations, including pay factor calculations.
• Ability to generate system outputs and ad hoc or standardized reports that can be electronically distributed or published online.
• Ability to interface/link with other agency systems (e.g., PMS database, financial/cost accounting system) via lot location identification.
• Analysis capabilities to perform advanced analysis to correlate construction and material test data with performance (pavement management) and cost (bids, maintenance) databases.
• Common referencing across other key databases to perform analysis types listed in item above.
• Potential to use or switch to GPS coordinate referencing and bar coding technologies.
• Flexibility to customize the analysis for specific cases when required.
• Overall system stability (e.g., backups and disaster recovery) and security (e.g., firewall protection on machines accessing central database).

4.2 MODEL CONSTRUCTION QUALITY DATABASE

The features and the software architecture of a model database are discussed in this section. Also summarized are the list of outputs and the analysis capabilities of this database.

4.2.1 Referencing System

The referencing system is the technique adopted to identify specific points or segments of a pavement. The referencing method is a very critical component of the system and forms the crosswalk between different databases for data integration. Data from one dataset, say the construction quality database, can be cross-referenced with the corresponding data from a second database (say, PMS or traffic) only by matching the referencing scheme.

The findings from this study suggest that lots are definable entities in a construction database that can be linked to a PMS. Lots are defined by the beginning and end stations. It is essential that data in PMS and maintenance records be maintained with the same referencing system to make meaningful correlations between datasets. Agencies should consider incorporating this common referencing scheme for immediate implementation or in their short-term plans. Without a common referencing system, no comparison of construction quality with performance can be accomplished.

For the long term, use of GIS, a spatial referencing method, will provide greater flexibility in identifying attribute data, and is amenable for use in relational database systems and in databases where dynamic segmentation is necessary. Some agencies are implementing or conducting pilot projects to use GPS technologies in collecting inventory and survey records. In these cases, GIS-based referencing is a natural path to follow. The study recommends that agencies adopt a GIS-based referencing when establishing new databases, as well as in their efforts to improve the current system.

4.2.2 Software Architecture and Features of the Database

Overview of Software Architecture

The proposed database is a Web-based or wide-area network (WAN) based system that can be accessed by several users in various district offices of the State through their shared networks. In other words, all communication channels are via the Internet. A client-server architecture that enables multi-user updating and SQL communication can be used across the client and server. A database server can provide reliable data storage, access, integrity, and security to clients over the Internet. This system will allow the storage of data in one comprehensive system, eliminating duplication and improving the accuracy of information retrieved by all end-users.

A software application (or a set of applications, as the case may be) providing the interface to input data and generate outputs will be installed on every client machine. In addition, this system aims at enabling an electronic data entry tool for field staff thereby eliminating the need for paper records and duplication of efforts in entering field test data. This can be achieved by the use of a static data entry form (similar to an input data file for a specific input category) that can function on an offline machine (e.g., a pen-based, handheld device). The completed form or input file can be eventually uploaded to the central server after field staff returns to the client machine. Figure 7 illustrates the automatic file upload feature. Data entry forms can be created using any programming language that support web-based technology (e.g., ASP.NET, Perl, Python). The applications that drive the data entry have to be developed using a graphical user interface (GUI) standard.

Figure 7. Conceptual sketch – field data transferred from Web forms to database server.

Database Modules and Feature

Figure 8 shows the architecture proposed for an effective construction quality database system that will meet the agency and contractor needs identified in this study. This section discusses the main data categories in the database, while detailed discussion of input data types are discussed in section 4.2.3.

Figure 8. Architecture for construction quality database system.

A Data input by project management staff from SHA’s central or district office.

F Data input by agency field personnel and may be input using standalone input interface.

C Data input by contractor, may be input using stand-alone input interface.

* Information common to the entire system and entered by the agency.

# Analysis/calculations performed for specific needs may require data integration with other databases.

The proposed system consists of four modules, as described in the sections below.

Database Server Module :

The database server module forms the core of the architecture and functions as the key component of the construction quality database system. All other modules connect with the server though the Internet. The database server stores system data information and provides services for different users through the internet. This server is centrally located and will provide QA services to specified or authorized users in remote locations. The level of access and privileges for each user—agency or contractor personnel—will be customized depending on the role of the individual in relation to the project and/or organization. Data can be uploaded or retrieved through the internet using a simple customizable software program as an interface, which will be installed on every client machine.

QA Data Input Module :

The QA data input module will be used to record all information relevant to each construction project and for each contract. This module provides an interface and a storage feature for all data relevant to the project, from the time of project initiation to the final acceptance of the construction. It includes data entered by various agency and contractor personnel through the production stage, plant mix design, lab testing, and construction testing. Note that the QA data input module merely provides a framework to input data in several categories, but will not provide the analysis engine required to perform detailed QA analysis. The following is a list of all categories and a brief description of the data elements to be included for each data category:

• General—This includes general information pertinent to the construction project and entered by the project manager of the job from within the SHA. The project is identified preferably by the contract number but adequate references to the route number, lanes, and milepost will also be made. Contract documents and other general comments and information can be entered here.
• Certification and Qualification—This category includes a list of laboratories certified to perform QA testing for the State. Also included is a list of certified material suppliers and qualified in-house staff to perform various tests.
• Material and Design—This category refers to details of the materials and job mix formula (JMF) being used in the construction. Examples of data items included in this category are concrete mix design, HMA mix design, and gradation of aggregates. Test data from production stage performed by the agency to certify the materials for use in construction is also be stored here. A provision to import standard mix designs that are typical to the agency (stored in material library files) is provided here.
• QC Testing—This category refers to all test data collected from the QC operations during construction typically by the contractor (or the testing subcontractor). These data can be entered either through the client machine or using the web form on a standalone program on any PC/handheld device. In the latter case, QC data should be uploaded back to the database server system. Examples of data in this category include mat density and concrete slump.
• Acceptance Testing—This category includes acceptance data collected by the agency, or the contractor in cases where contractor data is used for acceptance, after the completion of construction for each lot. Again, the flexibility to use web forms to upload data from a stand alone machine to the server is offered in this case. Examples of data inputs in the construction testing class include thickness, HMA aggregate gradation, mat density, compressive strength of PCC, smoothness, etc. This information will be used by the system to calculate pay factors.
• Construction Records—This category consists of all information relevant to the construction operation and the conditions prevailing at the time of construction. Weather conditions during construction, lift thicknesses used in HMA paving, observed segregation in HMA, curing methods adopted for PCC, construction sequence, and change in construction equipment are examples of data inputs in this category.
• Checklist—This category consists of data indicating the conformance of several items in the specifications that are not routinely tested. A checklist for each material type (HMA, PCC, earthwork, stabilized materials) is recommended for the database, and each checklist covers several attributes. The recommendation stems from the construction checklist maintained by the ADOT. The data input in this category assumes three values, “conformance,” “nonconformance,” or “N/A” for each attribute in the checklist. These data are entered by the agency personnel, and are offered the flexibility for use in offline web forms.
• IA Evaluation—This category includes data collected by the agency for IA purposes. These data can be collected on standalone machines in the field for future upload to the database server.

Note that data items entered in the last seven categories above use the same reference system created in the first category (General). For acceptance testing and some QC testing, the user enters data for each lot for each material.

During the development of this module, as well as during the implementation stage, due consideration should be given to the level of data that are input into the database. Care should be taken to enter unbiased data. In the event biasness of any form exists in the data, the bias issue has to be noted so that the data can be used with caution during the analysis stage.

QA Management Module:

The QA management module utilizes data input into this module to analyze the quality of construction in the project. There are five subsystems in this module:

• Acceptance System—This category contains the necessary calculations to determine the acceptance of the construction based on QA data in the database server. It includes the accept/reject procedures and is governed by the Specification System in the QA management module.
• Specification System—This category contains the current specifications of the State and monitors the adequacy of specifications. Agencies can routinely perform analysis to identify inconsistencies in specifications with relation to desired performance and to evaluate the effectiveness of specifications. The system will make possible continued improvements to performance-related specifications and QA system. Note that the Acceptance System utilizes data from the Specification System to calculate pay factors. The Specification System should also be appropriately linked to the input module so that the specifications for each material can be compared against the quality of construction.
• Pay System—This system uses data from the Acceptance System to provide guidelines for pay factors commensurate with the construction quality provided.
• Analysis System—This system will provide statistical analysis, quality performance simulations and other modeling tools for QA data. A detailed discussion of data analysis is presented in chapter 5 of this document.
• Information/Output System—This system will be an information manager for QA data information. It will include QA data queries and retrievals, generate reports and track qualifications of material producers, testing laboratories and technicians, etc.

Data Translation Module:

This module provides the tools to translate data information to communicate with other systems (such as the PMS) with a standard format. This module acts as a communication channel with other systems to provide desired QA testing data to other systems, as well as provide a platform to request other data (e.g., performance, traffic, cost, safety) into this system.

4.2.3 Data Types in Input Categories

This section discusses the data types in the various input categories of the database. The discussion here points to tests that are considered critical or important for maintaining an efficient database and a cost-effective QA program in the agency. HMA, PCC, and unbound material test data, smoothness, and construction records are included in this discussion. Clearly, this discussion might not cover the entire set of tests and input data collected by all agencies; each agency will have to address issues specific to its needs.

Hot-Mix Asphalt

SHAs use a variety of different HMA mixtures to pave roads. Some of these mixtures serve specific purposes. Porous Friction Course (PFC) mixtures, for example, are typically used to reduce splash/spray conditions and noise. Dense-graded asphalt mixtures are the most commonly used and have been in use for decades on low-to-high traffic volume roads.

Recent advances in pavement technology have led to the development of “performance” oriented mixture design approaches. Superpave was developed to address the demands of higher traffic volumes on roads and the stone-on-stone design approach of stone matrix asphalt (SMA) mixtures was developed to eliminate permanent deformation or rutting. Thus, pavement engineers have a larger selection of candidate mixtures that may be used to address performance concerns. Characteristics of these mixtures may differ significantly and as a result the performance criteria of one mixture may not necessarily apply to another. Consequently, mix design and specification criteria for mixture QC and verification for different asphalt mixtures may vary and must be established separately.

For QC purposes, it is necessary to distinguish between different types of asphalt mixtures serving specific purposes. These may be broadly categorized into the following groups:

• Dense-graded HMA.
• Superpave or Performance-Designed HMA.
• Porous Friction or Open-Graded HMA (PFC, OGFC).
• Stone-Matrix Asphalt (SMA).

These asphalt mixtures may be further sub-divided into grouping relating to aggregate size and gradation to distinguish coarse, intermediate, and fine mixtures. This separates mixtures for base and surface course applications. This sub-division is necessary, as the volumetric property criteria of HMA are related to aggregate size and gradation. Division of asphalt mixtures by binder or asphalt grade generally is not necessary, although performance criteria applied to performance-designed mixtures may vary for softer binder grades relative to stiffer binder grades.

QC procedures for HMA must address the quality of (a) materials or mixture components, (b) the mixture design process for identifying an appropriate JMF, and then (c) the production phase during mixture placement and construction.

Materials:

Materials used for the manufacture of HMA include aggregate, asphalt binder, additives (lime or anti-stripping agents), fibers (for SMA and PFC), reclaimed asphalt pavement (RAP), and other recycled materials. It is the agency’s responsibility to ensure that the quality of the individual components comprising the asphalt mixture is adequate. This is done well before the materials are used for construction of the HMA so that it is not necessary to control or verify material component quality during construction, except perhaps to verify the grade of the asphalt binder. Furthermore, material components may be used for manufacturing a range of different products and used on different projects. For this reason, it may be adequate to record the supplier of the materials, the aggregate classification and stockpiles used and the binder grade. It is important, however, to be able to reference properties of the base materials comprising the HMA. These properties would be entered and stored in a separate database table from the asphalt mixture information with links to primary fields identifying material codes common to both.

The aggregate component of HMA typically makes up 80 percent by volume of the mixture. Aggregates form the skeletal structure of the mixture and provide structural stability. Therefore, it is necessary to ensure the aggregates have adequate strength and resist abrasion. Other critical factors influencing mixture compaction and performance are aggregate shape, angularity and crushed faces. Crushed cubical aggregates perform better than rounded or flat and elongated particles. It is recommended that SHAs establish Aggregate Quality Monitoring Programs (AQMPs) to track the properties of aggregates at the source or quarry. Recycled Asphalt Pavement (RAP) is commonly used in asphalt mixtures. SHAs may place a restriction on the percentage of RAP allowed in certain HMA mixtures.

The following is a listing of requirements typically used to evaluate aggregate quality:

• Surface Aggregate Classification (source, strength, friction properties, etc).
• Particle size distribution (gradation).
• Deleterious material (dirt or objectionable materials).
• Decantation.
• Micro-Deval abrasion.
• Los Angeles abrasion.
• Magnesium or sodium sulfate soundness.
• Coarse and fine aggregate angularity.
• Flat and elongated particles.
• Linear shrinkage (fine aggregate).
• Sand equivalent.
• Bulk specific gravity.

The asphalt binder component of HMA is categorized by binder grade in the U.S. using a performance grade (PG) system that evaluates the performance characteristics of the asphalt binder in terms of performance properties. The most relevant of these include:

• Flash point.
• Viscosity (determines the mixing and compaction temperatures).
• Mass loss after rolling thin-film oven test (RTFOT).
• Dynamic shear rheometer (DSR) stiffness (before and after binder aging).
• Creep stiffness.
• Direct tension.
• Elastic recovery (for modified binders).
• Specific gravity.

Mixture Design:

The final mixture design defines the JMF that will be targeted during the construction process. This entails the selection of an aggregate structure or blend of aggregate components and optimum binder content for asphalt mixtures. The mixture gradation and binder content are selected to ensure desired properties and performance. The JMF may change during production, and database structures must make provision for this change, as control and verification of material quality is always applied in reference to the approved JMF. In a construction quality database, this may be achieved by placing the JMF information in a separate table and providing a link to the QA data in another table that includes a field identifying the JMF to be applied. At a minimum, the JMF information will include target aggregate gradation and binder content.

Mix design and verification information to be collected include:

• Design and JMF changes.
• Asphalt content.
• Aggregate gradation (ignition oven or solvent extraction method).
• Laboratory-compacted density.
• Volumetric properties (VMA, VFA, etc).
• Film thickness (calculation based on AC content and gradation).
• Maximum theoretical specific gravity.
• Ignition oven calibration.
• Indirect tensile strength (tensile strength ratio).
• Wheel tracking test results (e.g., Hamburg, APA), if appropriate.
• Boil test (to evaluate stripping potential).
• Drain-down (PFC and SMA mixtures only).
• Cantabro Loss (PFC mixtures only).

The mix designer will select a mix design traffic category, which affects property limits and parameters such as design compaction level, aggregate angularity criteria, volumetric property criteria (VMA, VFA), etc.

Aggregate gradation is an important factor influencing asphalt mixture performance (Roberts et al., 1996). Some agencies have gradation requirements for the fine aggregate and mineral filler components of mixtures, but all apply gradation limits (master gradation bands) defining extents within which an aggregate gradation must fall. Gradation requirements for different mixture types will vary and control may not necessarily be required for each of the sieve sizes listed in the table. Mixture gradation control is critical during the mixture design and production phases.

Asphalt mixtures can be susceptible to moisture damage. The phenomenon known as “stripping” occurs when the bitumen coating on aggregates is removed under the action of water. This susceptibility to moisture damage may be evaluated using indirect tensile strength tests and the application of a tensile strength ratio (TSR) as defined in ASTM D 4867 (ASTM, 2004). The resistance to moisture damage and permanent deformation of asphalt mixtures is increasingly being assessed using wheel tracking devices such as the Hamburg Wheel Tracking Device (HWTD) and Asphalt Pavement Analyzer (APA).

As mentioned previously, asphalt mixture design is a laboratory procedure to determine the optimum binder content for an aggregate blend. Testing is usually done at different binder contents, and an optimum binder content is selected for desired mixture properties. If possible, a link should be made available to the mixture design information to obtain volumetric properties at the design binder content, as well as relevant mixing and compaction temperatures applied. The volumetric properties can easily be calculated and incorporated into construction quality databases.

Mixture Production:

Before addressing the mixture production QC and assurance inputs required for construction quality databases, certain external factors should be considered. These include recording construction equipment used on the job site, as well as technician certification requirements. Other relevant but perhaps not critical information includes project personnel, material delivery and storage details. This information is usually required as part of the Quality Control Plan (QCP) but seldom referenced when addressing production quality.
Asphalt mixtures are usually evaluated in intervals known as lots, which may be defined by time (e.g., a day’s production) or by tonnage. Control and verification testing are done on sublots, typically four, which make up the lot. Control and verification testing plans vary. Some states require contractor QC testing of every sublot with agency verification done randomly on the lot. In Texas, for example, both the contractor and the SHA test each of the four sublots during production.

With regard to mixture production, a distinction must be made between production testing and placement testing. Production testing required could include:

• Laboratory compaction density.
• Rice gravity.
• Gradation.
• Asphalt content.
• Control charts.
• Moisture content.
• Wheel tracking tests.
• Micro-Deval abrasion.
• Boil test.
• Aging ratio.

Placement testing is done during and after construction and may include:

• In-place density.
• Field compactor rolling patterns.
• Control charts.
• Ride quality measurements.
• Segregation control (density profile).
• Longitudinal joint density.
• Thermal profile.
• Tack coat adhesion (for multiple asphalt layers).
• Permeability (PFC mixtures only).

In addition, the paving date and time of each sublot should be noted as well as air temperature during paving operations, as this significantly influences mixture compaction and control of segregation related problems.

Portland Cement Concrete

QC procedures for PCC must address three main aspects (a) materials and mix design (b) hardened concrete properties, and (c) the placement and construction aspects.

Materials:

Concrete is a homogeneous mixture of cementitious materials, coarse aggregate, fine aggregate, and water. In most cases, certain admixtures are added to improve or control specific properties. Each component of the mix affects the properties of fresh and hardened concrete and therefore the characteristics of each of these materials have to be input into the database. Further, SHAs maintain material specifications for each individual component as well as mix design. Some of these specifications also relate to fresh concrete properties. The following inputs regarding the materials are recommended:

• Cement type—affects strength gain and shrinkage properties.
• Cement content—affects strength gain and shrinkage properties.
• Water-cementitious materials ratio—affects strength, shrinkage, coefficient of thermal expansion, permeability.
• Aggregate type—affects strength, shrinkage, coefficient of thermal expansion, workability, and moisture absorption.
• Aggregate gradation—affects the packing density, strength properties, workability issues.
• Percentage of other cementitious materials (such as fly ash)—affects long-term strength, early shrinkage, heat of hydration, workability, permeability, etc.
• Admixture type—affects workability, shrinkage, expansion, and freeze-thaw properties.
• Unit weight—affects strength and indicates level of consolidation.
• Slump—affects workability and controls segregation problems.
• Air content—affects freeze thaw resistance.

Hardened Concrete Properties:

The main hardened concrete properties that control pavement design and performance relate to strength characteristics and volumetric changes. Most agency specifications spell out a minimum strength value for the concrete at 28 days and at the time of opening to traffic. The following tests and input data are recommended for hardened concrete properties:

• Compressive strength—regarded by agencies as the controlling strength parameter and also forms a convenient comparison for future strength tests on cores removed from the pavement.
• Flexural strength—affects cracking in pavement and a key parameter for fatigue damage calculations.
• Coefficient of thermal expansion—a very important material property that has received attention in recent years; affects deformations resulting from thermal gradients and determines the amount of permanent built-in curl in the slab thereby significantly affecting fatigue damage and cracking in pavement.
• Shrinkage—controls early age distress due to loss of moisture, affects the extent of permanent deformation as a result of moisture gradients, and affects long term fatigue damage and cracking in pavement. This test is performed on laboratory specimens.
• Permeability—a key indicator of durability and is affected by quality of mix and the temperature of mix during placement and curing.

Placement and Construction:

Construction operations have a tremendous impact on the overall quality of the pavement and long-term performance. SHA specifications contain several items to control construction activities. The tests and input data from the placement stage that are considered important:

• Thickness—regarded as one of the main AQCs for rigid pavements and affects the long term performance of the pavement.
• Dowel bar and tie bar alignment—affects performance of transverse and longitudinal joints, and joint faulting.

In addition, the paving date and time of each sublot should be noted, as well as air temperature during paving operations, as this significantly influences shrinkage, built-in curl warp, joint opening, etc.

Unbound Materials

Unbound materials consist of the existing subgrade layer at the project site, as well as any additional aggregate layers placed. The existing subgrade has to be compacted and rolled to the specified density levels. For all unbound layers, the following tests are recommended:

• Dry Density.
• Gradation.
• Minus 200 Material.
• Moisture Content.
• Atterberg Limits.
• Strength, Dynamic Cone Penetrometer, CBR, R-value.
• Resilient Modulus.

Smoothness and Surface Characteristics

Ride quality measurements, typically reported in terms of IRI or PI, can be measured using profilographs or inertial profilers. Smoothness measurements are taken along a section of road and averaged from measures in the left and right wheelpaths for both flexible and rigid pavements. Also recommended are tests for friction and texture to determine the skid resistance and noise abatement features of the surface layer.

4.3.4 Outputs and Analysis

The outputs and analysis desired from the system control the design and architecture of the database system; outputs required from the database drive the inputs. The following are examples of outputs that can be obtained from the recommended construction quality system:

• Pay factors for all materials of interest in a format desired by the SHA.
• Generation of statistical reports and graphs for use by the SHA in a standard format (e.g., .doc, .xls, .txt, .txt, .html, .rtf, .pdf).
  • Contract report on pay factor.
  • Actual pay factor awarded to the contractor.
  • Other parameters for calculating variability.
• Chronological data for quality and compliance audits and for historical reference.
• Mixture design information for HMA and PCC.
• Construction Quality Report for a specific contract or project.
• Contractor Quality Report for a specific contractor on their projects.
• Appropriate QA data, location, climate, and other information needed to conduct an analysis for the correlation of the quality of construction (measured by the various AQCs) with the future performance of the pavement.
  • Construction sequence or field notes.
  • Conformance to specifications for all attributes in checklist database.
• Appropriate QA data, location, and other information needed to evaluate the specifications under which the project was built.
  • Sample size.
  • Test procedure.
  • Location of sample.
  • Estimation of variability and statistical parameters.

In addition, several analyses can be performed using data from the database as well as by linking it with data from other agency databases (e.g., performance, traffic, safety, cost). Detailed discussion on types of data analysis and examples are provided in chapter 5.

4.3.5 Benefits

A well-developed construction quality database system offers the following benefits:

• Automation of data entry across various units (e.g., materials, construction, specifications, etc.) of the agency.
• Centralized entry and storage of testing data and contract documents in an electronic format that is easily accessible by the central office, district offices, project field offices, and other agency personnel or their consultants.
• Possibility for hierarchical data access.
• Ability to securely upload or import QC test data and IA data from external sources (e.g., vendors, contractors, consultants).
• Electronic approvals.
• Automated means to calculate pay factors and make acceptance decisions.
• Ability to highlight specification non-conformance in real-time (i.e., during construction) and the opportunity to take timely remedial actions by the agency’s decision makers. This includes specifications that are tested as well as those inspected during the preparation of construction checklists.
• Generation of ad-hoc and standardized reports in a manner that can be easily incorporated into documents.

  Ability to perform various engineering analyses including:

  • Rating asphalt and concrete plants based on quality they produce, which can be assessed from QC records in the database. The agency can pre-qualify material suppliers and producers, which can result in cost savings in QA programs.
  • Testing the effectiveness of current specifications or QA processes and to revise them as necessary based on performance or cost analysis.
  • Assessing contractor performance.
  • Tracking overall system performance and the performance of new and innovative materials, construction, and testing technologies.
  • Forensic evaluation of pavements (with both good and poor performance) using lot-specific materials, construction, and climatic data.
  • Aiding in improving pavement design and pavement management processes.
  • Aiding in the development of pavement performance models.
  • Establish basis for materials and performance warranties as well as performance-based specifications.

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This page last modified on 05/01/07