Sampling Protocol for Condition Assessment of Selected Assets
This study addresses the development of a sampling protocol for condition assessment of selected assets in the state of Maryland. The proposed sampling protocol targets a desired precision and level of confidence in the estimates of levels of service (LOS) for individual assets at each maintenance shop. This work includes the evaluation of the effect of sample size [i.e., number of 0.8 km (1/2 mi.) roadway sections] on the accuracy of estimates of LOS. The distribution of sample sizes among the various maintenance shops accounts for factors such as roadway functional classification, average annual daily traffic, geographical location, the approximate distribution of assets, and the variability in estimates of LOS for each individual asset. The implementation of the proposed sampling protocol will allow maintenance personnel to make reasonable inferences regarding the condition level of the entire asset population. This information will be useful to prioritize areas of need and determine levels of funding, personnel, and equipment. Three different variations of the proposed sampling protocol are evaluated. These variations are a function of how sample sizes are calculated: Option 1, sample size based on the asset with the largest number of required samples; Option 2, sample size based on the asset with the largest number of required samples from a selected group of assets; and Option 3, sample size based on the average number of required samples for all assets. It was concluded that, for a given confidence level, if a minimum precision is to be met for all assets, Option 1 should be used. However, if the sample size is to be limited and a minimum precision is to be met only for a selected group of assets, then Option 2 would be the best alternative. The framework proposed in this study can be modified so that it can be applied to other geographical regions.
Resource Types: Article
Capabilities: Tools & Technology
Management Processes: Performance Reporting & Communication, Resource Allocation