An artificial neural network approach in service life prediction of building components in Malaysia based on local environment and building service load
The degradation of building and its components are influenced by whole set of factors such as environmental degradation agents, quality of material, protective treatment, design of building, quality of work and maintenance. Selection of suitable materials for the building components can prolong the...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2007
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| Subjects: | |
| Online Access: | http://eprints.utm.my/2788/ http://eprints.utm.my/2788/1/SHTapsir2007_artificial_neural_network_approach_in_service.pdf |
| _version_ | 1848890432020283392 |
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| author | Tapsir, Siti Hamisah Mohd. Yatim, Jamaludin Usman, Fathoni |
| author_facet | Tapsir, Siti Hamisah Mohd. Yatim, Jamaludin Usman, Fathoni |
| author_sort | Tapsir, Siti Hamisah |
| building | UTeM Institutional Repository |
| collection | Online Access |
| description | The degradation of building and its components are influenced by whole set of factors such as environmental degradation agents, quality of material, protective treatment, design of building, quality of work and maintenance. Selection of suitable materials for the building components can prolong the service life of particular building components and in certain cases require less maintenance and replacement activity. Emphasis on material characterisations at the design stage is limited because most of the time great emphasis is given on delivering with lowest initial building cost rather than lowest life cycle cost. In this study, an artificial neural network is used to predict the service life of building materials with the basis study on deterioration of building components affected by its surrounding environment and factors that accelerate its aging process. The advantages of artificial neural network is employing as a prediction tool. The back-propagation learning algorithm is used as learning model. The environment load factors, workmanship, design, usage and level of maintenance are used as input variables in training process of the neural network model. The results are encouraging and potentially useful for further application of the service life prediction |
| first_indexed | 2025-11-15T20:41:58Z |
| format | Conference or Workshop Item |
| id | utm-2788 |
| institution | Universiti Teknologi Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:41:58Z |
| publishDate | 2007 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utm-27882010-06-01T03:05:09Z http://eprints.utm.my/2788/ An artificial neural network approach in service life prediction of building components in Malaysia based on local environment and building service load Tapsir, Siti Hamisah Mohd. Yatim, Jamaludin Usman, Fathoni TA Engineering (General). Civil engineering (General) The degradation of building and its components are influenced by whole set of factors such as environmental degradation agents, quality of material, protective treatment, design of building, quality of work and maintenance. Selection of suitable materials for the building components can prolong the service life of particular building components and in certain cases require less maintenance and replacement activity. Emphasis on material characterisations at the design stage is limited because most of the time great emphasis is given on delivering with lowest initial building cost rather than lowest life cycle cost. In this study, an artificial neural network is used to predict the service life of building materials with the basis study on deterioration of building components affected by its surrounding environment and factors that accelerate its aging process. The advantages of artificial neural network is employing as a prediction tool. The back-propagation learning algorithm is used as learning model. The environment load factors, workmanship, design, usage and level of maintenance are used as input variables in training process of the neural network model. The results are encouraging and potentially useful for further application of the service life prediction 2007 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/2788/1/SHTapsir2007_artificial_neural_network_approach_in_service.pdf Tapsir, Siti Hamisah and Mohd. Yatim, Jamaludin and Usman, Fathoni (2007) An artificial neural network approach in service life prediction of building components in Malaysia based on local environment and building service load. In: Fourth International Conference on Construction in the 21st Century (CITC-IV) “Accelerating Innovation in Engineering, Management and Technologyâ€, July 11-13, 2007, Gold Coast, Australia. (In Press) |
| spellingShingle | TA Engineering (General). Civil engineering (General) Tapsir, Siti Hamisah Mohd. Yatim, Jamaludin Usman, Fathoni An artificial neural network approach in service life prediction of building components in Malaysia based on local environment and building service load |
| title | An artificial neural network approach in service life prediction of building components in Malaysia based on local environment and building service load
|
| title_full | An artificial neural network approach in service life prediction of building components in Malaysia based on local environment and building service load
|
| title_fullStr | An artificial neural network approach in service life prediction of building components in Malaysia based on local environment and building service load
|
| title_full_unstemmed | An artificial neural network approach in service life prediction of building components in Malaysia based on local environment and building service load
|
| title_short | An artificial neural network approach in service life prediction of building components in Malaysia based on local environment and building service load
|
| title_sort | artificial neural network approach in service life prediction of building components in malaysia based on local environment and building service load |
| topic | TA Engineering (General). Civil engineering (General) |
| url | http://eprints.utm.my/2788/ http://eprints.utm.my/2788/1/SHTapsir2007_artificial_neural_network_approach_in_service.pdf |