Data-driven SIRMs-connected FIS for prediction of external tendon stress

• 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

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.