Computers and Concrete

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

  • Lau, See Hung (Department of Civil Engineering, Faculty of Engineering, Universiti Malaysia Sarawak) ;
  • Ng, Chee Khoon (Department of Civil Engineering, Faculty of Engineering, Universiti Malaysia Sarawak) ;
  • Tay, Kai Meng (Department of Electronic Engineering, Faculty of Engineering, Universiti Malaysia Sarawak)
  • Received : 2014.01.06
  • Accepted : 2014.11.03
  • Published : 2015.01.25
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Abstract

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, ${\Delta}f_{ps}$, 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 ${\Delta}f_{ps}$ even without a complete physical knowledge of unbonded tendons.

Keywords

Acknowledgement

Supported by : Ministry of Education (MOE)

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