Samuel Owusu-Ababio

Understanding Statistics in Maintenance Quality Assurance Programs

From the 1990s to now, transportation maintenance quality assurance (MQA) programs have been developed to ensure that maintenance quality is being achieved. MQA programs must be capable of detecting insufficient maintenance efforts, poor material performance, and incorrect procedures when evaluating end-product performance. At the Maintenance Quality Assurance Peer Exchange held at Madison, Wisconsin, in October 2004, participants expressed interest in exploring how statistical tools might be more effectively applied in MQA programs. The purpose of this paper is to provide maintenance practitioners with knowledge of how to understand and use statistics in MQA programs. Literature pertaining to recent efforts in this area was reviewed and synthesized. In addition, hazardous debris data from Wisconsin and level of service data from North Carolina were analyzed to demonstrate how an agency could apply traditional statistical methods such as analysis of variance, confidence limits, means comparison, data stratification, and sample size determination to an MQA program.

Ranking Procedure Based on Statistical Hypothesis Testing

A key element in a highway maintenance management system is a means to establish an ordered list of those sections most in need of repair. Developing such a list requires a method of measuring and analyzing existing conditions to judge which system components are in greatest need of treatment. For highway pavement, for example, the challenge is first to survey the system to determine the range of condition, usually according to level of service or other suitable measure, and then to create an ordered ranking of individual segments of the system by need for repair. Toward this end, basic tools of statistical analysis have been used to develop a procedure that produces a logically ordered sequence of highway segments. The procedure can also be used to provide a graphical display of statistical results, which serves as a visual snapshot of the condition of the highway system. The procedure performs the equivalent of multiple hypothesis tests to establish the rankings, but it stops short of providing an optimal sequence of repair strategies based on economic considerations.