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Life Cycle Cost Analysis based Optimal Maintenance and Rehabilitation for Underground Infrastructure Management
This study presents a sanitary sewer management decision-making framework incorporating demand forecasting and life cycle cost analysis. The framework provides the asset managers with an alternative approach in sewer management. It is designed to allow asset managers to better allocate limited funds...
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| Published in: | KSCE journal of civil engineering 2006, 10(4), , pp.243-253 |
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| Main Authors: | , , , , |
| Format: | Article |
| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c2128-590dab1fb90d6d1583026a4a6d8d68c6e6a30032227c19186a9a5123901abf9f3 |
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| cites | cdi_FETCH-LOGICAL-c2128-590dab1fb90d6d1583026a4a6d8d68c6e6a30032227c19186a9a5123901abf9f3 |
| container_end_page | 253 |
| container_issue | 4 |
| container_start_page | 243 |
| container_title | KSCE journal of civil engineering |
| container_volume | 10 |
| creator | Chung, Seung-Hyun Hong, Tae-Hoon Han, Seung-Woo Son, Jae-Ho Lee, Sang-Youb |
| description | This study presents a sanitary sewer management decision-making framework incorporating demand forecasting and life cycle cost analysis. The framework provides the asset managers with an alternative approach in sewer management. It is designed to allow asset managers to better allocate limited funds for maintenance and rehabilitation by identifying possible problematic sewers and devising a maintenance plan to prevent costly sewer failures. Sewer demand forecasting model is developed using an artificial neural network. The forecasted sewer demand is then used to identify “critical” areas, where the current hydraulic capacity is less than the forecasted sewer demand. In such areas, an optimal maintenance and rehabilitation strategy is developed through the application of probabilistic dynamic programming in conjunction with Markov chain deterioration modeling. |
| doi_str_mv | 10.1007/BF02830778 |
| format | article |
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In such areas, an optimal maintenance and rehabilitation strategy is developed through the application of probabilistic dynamic programming in conjunction with Markov chain deterioration modeling.</description><subject>Artificial neural networks</subject><subject>Cost analysis</subject><subject>Decision analysis</subject><subject>Decision making</subject><subject>Demand</subject><subject>Dynamic programming</subject><subject>Forecasting</subject><subject>Life cycle</subject><subject>Life cycle analysis</subject><subject>Life cycle costs</subject><subject>Life cycles</subject><subject>Maintenance</subject><subject>Managers</subject><subject>Markov chains</subject><subject>Mathematical models</subject><subject>Neural networks</subject><subject>Rehabilitation</subject><subject>Sanitary sewers</subject><subject>Sewer maintenance</subject><subject>Sewers</subject><subject>Studies</subject><subject>Waste management</subject><subject>토목공학</subject><issn>1226-7988</issn><issn>1976-3808</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><recordid>eNpFkFtLAzEQhRdRULQv_oKACCKs5tJmk8dab4VqobTPYXY3qdE1qUn2of_e1IrOw5zAfDPhnKI4J_iGYFzd3j1iKhiuKnFQnBBZ8ZIJLA7zm1JeVlKI42IQ4zvOxWgl2Oik8DNrNJpsmy53HxMaO-i20UZUQ9Qtmm-S_YQOvYB1STtwjUbgWrTQb1DbziZI1jtkfEAr1-qwDr7P46kzAWIKfZP6oPO2g7X-1C6dFUcGuqgHv3parB4flpPncjZ_mk7Gs7KhhIpyJHELNTF1Vt6SUbZFOQyBt6LlouGaA9uZoLRqiCSCg4QRoUxiArWRhp0WV_u7Lhj10Vjlwf7o2quPoMaL5VRxzqQgGb3Yo5vgv3odk3r3fcgxREUIYWy4wzJ1vaea4GMM2qhNyMmErSJY7eJX__Fn-PL39z5DurXwR7_O7x9wtlgxMmTftcSCkg</recordid><startdate>20060701</startdate><enddate>20060701</enddate><creator>Chung, Seung-Hyun</creator><creator>Hong, Tae-Hoon</creator><creator>Han, Seung-Woo</creator><creator>Son, Jae-Ho</creator><creator>Lee, Sang-Youb</creator><general>대한토목학회</general><general>Springer Nature B.V</general><scope>DBRKI</scope><scope>TDB</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7UA</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FR3</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KR7</scope><scope>L.G</scope><scope>L6V</scope><scope>M7S</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>ACYCR</scope></search><sort><creationdate>20060701</creationdate><title>Life Cycle Cost Analysis based Optimal Maintenance and Rehabilitation for Underground Infrastructure Management</title><author>Chung, Seung-Hyun ; 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| identifier | ISSN: 1226-7988 |
| ispartof | KSCE Journal of Civil Engineering, 2006, 10(4), , pp.243-253 |
| issn | 1226-7988 1976-3808 |
| language | eng |
| recordid | cdi_nrf_kci_oai_kci_go_kr_ARTI_663981 |
| source | Springer Link |
| subjects | Artificial neural networks Cost analysis Decision analysis Decision making Demand Dynamic programming Forecasting Life cycle Life cycle analysis Life cycle costs Life cycles Maintenance Managers Markov chains Mathematical models Neural networks Rehabilitation Sanitary sewers Sewer maintenance Sewers Studies Waste management 토목공학 |
| title | Life Cycle Cost Analysis based Optimal Maintenance and Rehabilitation for Underground Infrastructure Management |
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