Abstract
This special issue is the second of two, reflecting recent advances in infrastructure condition assessment, deterioration modeling, and optimal maintenance, repair, and reconstruction. The first special issue September 2006 featured methodological advances that are taking place in light of practical considerations. In this second issue, the need to improve upon the management of infrastructure facilities and systems by explicitly addressing practical matters is again a recurring theme. In addition, the papers in this issue remind us that much technological and methodological research is also needed to help the practicing community effectively and efficiently address its pressing demands. Cascante et al. develop a fuzzy-logic based methodology for assessing the condition of hydraulic channel sidewalls, based on nondestructive testing and expert knowledge. Collecting data with nondestructive methods reduces cost, maintains serviceability, and preserves historically significant structures. Incorporating expert knowledge is often a cost-efficient means of assessing infrastructure condition. Therefore, capturing this knowledge in a rigorous, consistent, and valid manner has value to both the research and practicing communities. The authors support the utility of their method by showing the reasonableness of its sensitivity to the maintenance action applied in a realistic setting. Prozzi and Hong develop a multivariate deterioration model that addresses multiple pavement condition measures relevant to the management of infrastructure facilities. Their rigorous statistical approach simultaneously models the deterioration mechanisms corresponding to the different measures, while capturing the correlation among these measures. The authors also present an extensive case study, exploiting data collected from in-service facilities to demonstrate the importance of considering the multivariate nature of deterioration. Numerous pavement deterioration models have been developed and estimated using field data over the years. These models attempt to capture different characteristics of the data, represent deterioration behavior through alternative model structures, and
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