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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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Main Authors: See, Hung Lau, Chee, Khoon Ng, Kai, Meng Tay
Format: E-Article
Language:English
Published: Techno Press 2015
Subjects:
T Technology (General)
Online Access:http://ir.unimas.my/id/eprint/14852/1/NO%2034%20Data-driven%20SIRMs-connected%20FIS%20for%20prediction%20of%20external%20tendon%20stress%20-%20abstrak.pdf
http://ir.unimas.my/id/eprint/14852/
http://www.scopus.com/inward/record.url?eid=2-s2.0-84930792804&partnerID=40&md5=4bb1dcd7fe813bee1436929bff64f4f3
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spelling my.unimas.ir.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 E-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
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
See, Hung Lau
Chee, Khoon Ng
Kai, Meng Tay
Data-driven SIRMs-connected FIS for prediction of external tendon stress
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.
format E-Article
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
title 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_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_sort data-driven sirms-connected fis for prediction of external tendon stress
publisher Techno Press
publishDate 2015
url http://ir.unimas.my/id/eprint/14852/1/NO%2034%20Data-driven%20SIRMs-connected%20FIS%20for%20prediction%20of%20external%20tendon%20stress%20-%20abstrak.pdf
http://ir.unimas.my/id/eprint/14852/
http://www.scopus.com/inward/record.url?eid=2-s2.0-84930792804&partnerID=40&md5=4bb1dcd7fe813bee1436929bff64f4f3
_version_ 1644512013761642496
score 13.211869

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