State-of-practice for risk-based quality assurance in state departments of transportation
Purpose Selecting an optimal quality assurance (QA) process can have significant implications on the long-term durability and lifecycle costs of transportation projects. Currently, the approaches used by state departments of transportation (DOTs) to optimize QA are undocumented and the impact of pro...
Gespeichert in:
Veröffentlicht in: | Engineering, construction, and architectural management construction, and architectural management, 2018-08, Vol.25 (7), p.958-970 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Purpose
Selecting an optimal quality assurance (QA) process can have significant implications on the long-term durability and lifecycle costs of transportation projects. Currently, the approaches used by state departments of transportation (DOTs) to optimize QA are undocumented and the impact of project-specific factors are unknown. The paper aims to discuss these issues.
Design/methodology/approach
State-of-practice was documented via a review of DOT guidance documents, standard specifications and minimum sampling and testing requirements; a survey of 58 state DOT representatives; and interviews with eight DOTs.
Findings
DOT approaches to QA management are very diverse but can be organized into five levels that range from ad hoc visual inspection of materials to DOT-managed sampling and testing. Project size, location and complexity have strong influence on the selection of a QA approach, but DOT demographics and project delivery method are less significant.
Practical implications
Present approaches to the selection of a QA approach are generally informal, subjective and do not involve formal risk analyses. A data-driven approach for transportation projects is clearly needed.
Originality/value
Understanding how state DOTs approach QA method selection will serve as a foundation for new methods of QA optimization. |
---|---|
ISSN: | 0969-9988 1365-232X |
DOI: | 10.1108/ECAM-06-2016-0143 |