Data-driven SIRMs-connected FIS for prediction of external tendon stress
This paper presents a novel harmony search (HS)-based data-driven single input rule modules (SIRMs)-connected fuzzy inference system (FIS) for the prediction of stress in externally prestressed tendon. The proposed method attempts to extract causal relationship of a system from an input-output pairs...
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| Format: | Article |
| Language: | English |
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Techno Press
2015
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| Online Access: | http://ir.unimas.my/id/eprint/14852/ http://ir.unimas.my/id/eprint/14852/1/NO%2034%20Data-driven%20SIRMs-connected%20FIS%20for%20prediction%20of%20external%20tendon%20stress%20-%20abstrak.pdf |
| _version_ | 1848837746349572096 |
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| author | See, Hung Lau Chee, Khoon Ng Kai, Meng Tay |
| author_facet | See, Hung Lau Chee, Khoon Ng Kai, Meng Tay |
| author_sort | See, Hung Lau |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | This paper presents a novel harmony search (HS)-based data-driven single input rule modules (SIRMs)-connected fuzzy inference system (FIS) for the prediction of stress in externally prestressed tendon. The proposed method attempts to extract causal relationship of a system from an input-output pairs of data even without knowing the complete physical knowledge of the system. The monotonicity property is then exploited as an additional qualitative information to obtain a meaningful SIRMs-connected FIS model. This method is then validated using results from test data of the literature. Several parameters, such as initial tendon depth to beam ratio; deviators spacing to the initial tendon depth ratio; and distance of a concentrated load from the nearest support to the effective beam span are considered. A computer simulation for estimating the stress increase in externally prestressed tendon, Δfps, is then reported. The contributions of this paper is two folds; (i) it contributes towards a new monotonicity-preserving data-driven FIS model in fuzzy modeling and (ii) it provides a novel solution for estimating the Δfps even without a complete physical knowledge of unbonded tendons. Copyright © 2015 Techno-Press, Ltd. |
| first_indexed | 2025-11-15T06:44:33Z |
| format | Article |
| id | unimas-14852 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:44:33Z |
| publishDate | 2015 |
| publisher | Techno Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-148522017-02-06T07:04:55Z http://ir.unimas.my/id/eprint/14852/ Data-driven SIRMs-connected FIS for prediction of external tendon stress See, Hung Lau Chee, Khoon Ng Kai, Meng Tay T Technology (General) This paper presents a novel harmony search (HS)-based data-driven single input rule modules (SIRMs)-connected fuzzy inference system (FIS) for the prediction of stress in externally prestressed tendon. The proposed method attempts to extract causal relationship of a system from an input-output pairs of data even without knowing the complete physical knowledge of the system. The monotonicity property is then exploited as an additional qualitative information to obtain a meaningful SIRMs-connected FIS model. This method is then validated using results from test data of the literature. Several parameters, such as initial tendon depth to beam ratio; deviators spacing to the initial tendon depth ratio; and distance of a concentrated load from the nearest support to the effective beam span are considered. A computer simulation for estimating the stress increase in externally prestressed tendon, Δfps, is then reported. The contributions of this paper is two folds; (i) it contributes towards a new monotonicity-preserving data-driven FIS model in fuzzy modeling and (ii) it provides a novel solution for estimating the Δfps even without a complete physical knowledge of unbonded tendons. Copyright © 2015 Techno-Press, Ltd. Techno Press 2015 Article PeerReviewed text en http://ir.unimas.my/id/eprint/14852/1/NO%2034%20Data-driven%20SIRMs-connected%20FIS%20for%20prediction%20of%20external%20tendon%20stress%20-%20abstrak.pdf See, Hung Lau and Chee, Khoon Ng and Kai, Meng Tay (2015) Data-driven SIRMs-connected FIS for prediction of external tendon stress. Computers and Concrete, 15 (1). pp. 55-71. ISSN 15988198 http://www.scopus.com/inward/record.url?eid=2-s2.0-84930792804&partnerID=40&md5=4bb1dcd7fe813bee1436929bff64f4f3 |
| spellingShingle | T Technology (General) See, Hung Lau Chee, Khoon Ng Kai, Meng Tay Data-driven SIRMs-connected FIS for prediction of external tendon stress |
| title | Data-driven SIRMs-connected FIS for prediction of external tendon stress |
| title_full | Data-driven SIRMs-connected FIS for prediction of external tendon stress |
| title_fullStr | Data-driven SIRMs-connected FIS for prediction of external tendon stress |
| title_full_unstemmed | Data-driven SIRMs-connected FIS for prediction of external tendon stress |
| title_short | Data-driven SIRMs-connected FIS for prediction of external tendon stress |
| title_sort | data-driven sirms-connected fis for prediction of external tendon stress |
| topic | T Technology (General) |
| url | http://ir.unimas.my/id/eprint/14852/ http://ir.unimas.my/id/eprint/14852/ http://ir.unimas.my/id/eprint/14852/1/NO%2034%20Data-driven%20SIRMs-connected%20FIS%20for%20prediction%20of%20external%20tendon%20stress%20-%20abstrak.pdf |