Dissertation/Thesis Abstract

Statistical Validation Implementation in Pavement Construction Projects
by Nimeri, Mohamed A., Ph.D., University of Nevada, Reno, 2019, 278; 22618024
Abstract (Summary)

In the development of a Quality Assurance (QA) program, a state highway agency (SHA) must make several important decisions regarding policies and procedures for assessing how well the materials and construction used by a Contractor on a project satisfy the SHA specifications. One of the key decisions is whether the SHA will conduct the acceptance sampling and testing or utilize Contractor data for acceptance sampling and testing. Title 23 Code of Federal Regulations Part 637 Subpart B (23 CFR 637B) permits SHAs to use Contractor data for construction materials acceptance, as long as SHAs validate the Contractor data with independent test results.

Therefore, identifying procedures and guidelines for validating Contractor test data for construction materials is essential to QA programs. These procedures need to be statistically sound and practical for validating Contractor construction materials data. This dissertation documents and presents the results of studying procedures and guidelines for validating Contractor test data. The procedures developed address different applications (materials and procurement types) and related issues, like sampling method, sample size, retesting, associated risks, and practical constraints that have led SHAs to deviate from the AASHTO manuals and specifications. A guide was also prepared in AASHTO format to describe appropriate processes for validating Contractor results and recommend subsequent actions when the results are validated or not validated.

Indexing (document details)
Advisor: Hand, Adam JT
Commitee: Sebaaly, Peter E, Hajj, Elie Y, Siddharthan, Raj V, Ahn, Mihye
School: University of Nevada, Reno
Department: Civil and Environmental Engineering
School Location: United States -- Nevada
Source: DAI-B 81/4(E), Dissertation Abstracts International
Subjects: Civil engineering
Keywords: acceptance quality characteristics, outlier detection, quality assurance, quality control, validation
Publication Number: 22618024
ISBN: 9781687943248
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