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Water System Condition and Asset Replacement Prioritization

Water System Condition and Asset Replacement Prioritization

作     者:Frederick Bloetscher Zachary Farmer James Barton Teresa Chapman Paula Fonseca Monica Shaner Frederick Bloetscher;Zachary Farmer;James Barton;Teresa Chapman;Paula Fonseca;Monica Shaner

作者机构:Civil Environmental and Geomatics Engineering Florida Atlantic University Boca Raton Florida USA Boynton Beach FL USA 

出 版 物:《Journal of Water Resource and Protection》 (水资源与保护(英文))

年 卷 期:2023年第15卷第5期

页      面:165-178页

学科分类:080704[工学-流体机械及工程] 080103[工学-流体力学] 08[工学] 0807[工学-动力工程及工程热物理] 0801[工学-力学(可授工学、理学学位)] 

主  题:Water Main Predicted Failure Asset Management Pipe Failure Water Distributions 

摘      要:The goal of asset management is to identify and track the maintenance and replacement of assets that have reached their useful life. For that reason, gathering data and collecting information is a critical step when developing an asset management plan. Such data gathering includes physical and operational properties of the assets as well as collecting and tracking important events during the lifespan of the asset (i.e., pipe breaks, replacement year, maintenance performed, etc.). Critical factors in the asset management plan may be overlooked when there is no data or poor quality data. However, many utilities lack the resources for examining buried infrastructure and lack good quality work order data, so other methods of data collection are needed. The concept for this paper was to develop a means to acquire data on the assets for a condition assessment to identify pipes that were most likely to break and those with the highest consequences for same. Three utilities were used as examples. It was found that for buried infrastructure, much more information was known than anticipated but the actual predictions relied on only a few factors related to pipe type. However, there is a need to track the consequences, in this case breaks, which would indicate a failure. The latter would be useful for predicting future maintenance needs and the most at-risk assets, but is often missing in utility systems as many utilities do not adequately track breaks sufficiently. In this case two utilities were analyzed and predication on a third was developed.

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