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 pair...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
2015
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/8144/ http://ir.unimas.my/id/eprint/8144/1/CAC41061X_Lau%20See%20Hung.pdf |
| _version_ | 1848836311125852160 |
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| author | Tay, K.M See, Hung Lau Chee, Khoon Ng |
| author_facet | Tay, K.M See, Hung Lau Chee, Khoon Ng |
| author_sort | Tay, K.M |
| 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 from 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 bond reduction coefficient u is then
reported. The contributions of this paper is two folds; (1) it contributes towards a new monotonicity-preserving
data-driven FIS model in fuzzy modeling and (2) it provides a novel solution for estimating the u even without a
complete physical knowledge of unbonded tendons. |
| first_indexed | 2025-11-15T06:21:45Z |
| format | Article |
| id | unimas-8144 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:21:45Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-81442015-07-02T05:39:02Z http://ir.unimas.my/id/eprint/8144/ Data-driven SIRMs-connected FIS for prediction of external tendon stress Tay, K.M See, Hung Lau Chee, Khoon Ng QA75 Electronic computers. Computer science 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 from 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 bond reduction coefficient u is then reported. The contributions of this paper is two folds; (1) it contributes towards a new monotonicity-preserving data-driven FIS model in fuzzy modeling and (2) it provides a novel solution for estimating the u even without a complete physical knowledge of unbonded tendons. 2015 Article PeerReviewed text en http://ir.unimas.my/id/eprint/8144/1/CAC41061X_Lau%20See%20Hung.pdf Tay, K.M and See, Hung Lau and Chee, Khoon Ng (2015) Data-driven SIRMs-connected FIS for prediction of external tendon stress. Computers and Concrete. |
| spellingShingle | QA75 Electronic computers. Computer science Tay, K.M See, Hung Lau Chee, Khoon Ng 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 | QA75 Electronic computers. Computer science |
| url | http://ir.unimas.my/id/eprint/8144/ http://ir.unimas.my/id/eprint/8144/1/CAC41061X_Lau%20See%20Hung.pdf |