Forecasting Variations and Trends in Water-Main Breaks- A Case Study of Taipei City
碩士 === 國立雲林科技大學 === 營建與物業管理研究所 === 97 === Deterioration of water mains causes serious problems in major urban communities worldwide. This study presents the findings of a recent study conducted on pipe-break data in an effort to identify and categorize the key factors that contribute to the deterior...
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ndltd-TW-097YUNT55120062016-04-29T04:19:02Z http://ndltd.ncl.edu.tw/handle/80698737817944516577 Forecasting Variations and Trends in Water-Main Breaks- A Case Study of Taipei City 自來水分配管網破損趨勢與變動之分析-以台北市售水轄區為例 Chih-Hung Chang 張志鴻 碩士 國立雲林科技大學 營建與物業管理研究所 97 Deterioration of water mains causes serious problems in major urban communities worldwide. This study presents the findings of a recent study conducted on pipe-break data in an effort to identify and categorize the key factors that contribute to the deterioration of water mains and examine the applicability of existing deterioration models for water mains in representing actual field conditions.The data used in this paper were collected from a municipality that has a large water distribution network in Taiwan. The effective planning of water distribution system renewal requires accurate quantification of the structural deterioration of water mains. As typical water distribution systems comprise hundreds and even thousands of buried pipes, direct inspection of all of them is often prohibitively expensive. Identifying water main breakage patterns over time is an effective and inexpensive alternative to measure the structural deterioration of a water distribution system. Environmental and operational conditions exert stresses on the pipe. Pipe breakage occurs when these stresses exceed its structural resiliency. While the structural deterioration of the pipe is generally considered to be a steady, monotonous process, some of the environmental and operational stresses could be time-dependent, steady or transient. These stresses result in “noisy” breakage data sets that reduce the accuracy of establishing the underlying deterioration (ageing) patterns, especially in small data sets. If the cause of these random stresses can be identified and attributed to a measurable phenomenon (e.g., temperatures, precipitation, etc.), their “noisy” effect can be neutralised to obtain a more accurate pipe deterioration pattern. A method is presented to analyse how breakage patterns in water mains are affected by time-dependent factors. The method is versatile enough to consider any number of underlying causes but the solution becomes more complex as the number of causes increases. Finding the “true” deterioration rates of buried water mains will inevitably lead to a more accurate prediction of their useful life, which in turn will lead to a more efficient allocation of resources for water main rehabilitation and renewal. Ying-Hua Huang 黃盈樺 2009 學位論文 ; thesis 128 zh-TW |
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碩士 === 國立雲林科技大學 === 營建與物業管理研究所 === 97 === Deterioration of water mains causes serious problems in major urban communities worldwide. This study presents the findings of a recent study conducted on pipe-break data in an effort to identify and categorize the key factors that contribute to the deterioration of water mains and examine the applicability of existing deterioration models for water mains in representing actual field conditions.The data used in this paper were collected from a municipality that has a large water distribution network in Taiwan.
The effective planning of water distribution system renewal requires accurate quantification of the structural deterioration of water mains. As typical water distribution systems comprise hundreds and even thousands of buried pipes, direct inspection of all of them is often prohibitively expensive. Identifying water main breakage patterns over time is an effective and inexpensive alternative to measure the structural deterioration of a water distribution system.
Environmental and operational conditions exert stresses on the pipe. Pipe breakage occurs when these stresses exceed its structural resiliency. While the structural deterioration of the pipe is generally considered to be a steady, monotonous process, some of the environmental and operational stresses could be time-dependent, steady or transient. These stresses result in “noisy” breakage data sets that reduce the accuracy of establishing the underlying deterioration (ageing) patterns, especially in small data sets. If the cause of these random stresses can be identified and attributed to a measurable phenomenon (e.g., temperatures, precipitation, etc.), their “noisy” effect can be neutralised to obtain a more accurate pipe deterioration pattern.
A method is presented to analyse how breakage patterns in water mains are affected by time-dependent factors. The method is versatile enough to consider any number of underlying causes but the solution becomes more complex as the number of causes increases. Finding the “true” deterioration rates of buried water mains will inevitably lead to a more accurate prediction of their useful life, which in turn will lead to a more efficient allocation of resources for water main rehabilitation and renewal.
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author2 |
Ying-Hua Huang |
author_facet |
Ying-Hua Huang Chih-Hung Chang 張志鴻 |
author |
Chih-Hung Chang 張志鴻 |
spellingShingle |
Chih-Hung Chang 張志鴻 Forecasting Variations and Trends in Water-Main Breaks- A Case Study of Taipei City |
author_sort |
Chih-Hung Chang |
title |
Forecasting Variations and Trends in Water-Main Breaks- A Case Study of Taipei City |
title_short |
Forecasting Variations and Trends in Water-Main Breaks- A Case Study of Taipei City |
title_full |
Forecasting Variations and Trends in Water-Main Breaks- A Case Study of Taipei City |
title_fullStr |
Forecasting Variations and Trends in Water-Main Breaks- A Case Study of Taipei City |
title_full_unstemmed |
Forecasting Variations and Trends in Water-Main Breaks- A Case Study of Taipei City |
title_sort |
forecasting variations and trends in water-main breaks- a case study of taipei city |
publishDate |
2009 |
url |
http://ndltd.ncl.edu.tw/handle/80698737817944516577 |
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