<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper aims to present a deflection prediction model of Near Surface Mounted (NSM) Reinforce Concrete (RC) beams using the Fuzzy Logic Expert System (FLES) with different types of membership functions (MF). The absence of a complete theoretical deflection prediction model of NSM-strengthened RC beams persuades this research to develop an Artificial Intelligence (AI) based prediction model using FLES. The proposed model uses triangular and trapezoidal MF to predict the deflection behavior of six NSM-strengthened RC beams. The research variables are strengthening materials and NSM bar length. In this study, two inputs (applied load and variable length) were used to predict two outputs (deflection of two types of strengthened RC beams). The relative error of predicted values was within 5% and the suitability of fit was close to 1.0 which affirms the efficacy of the FLES. Besides, a tiny difference was detected using triangular and trapezoidal MF for the prediction model.</p>
</abstract>

This paper aims to present a deflection prediction model of Near Surface Mounted (NSM) Reinforce Concrete (RC) beams using the Fuzzy Logic Expert System (FLES) with different types of membership functions (MF). The absence of a complete theoretical deflection prediction model of NSM-strengthened RC beams persuades this research to develop an Artificial Intelligence (AI) based prediction model using FLES. The proposed model uses triangular and trapezoidal MF to predict the deflection behavior of six NSM-strengthened RC beams. The research variables are strengthening materials and NSM bar length. In this study, two inputs (applied load and variable length) were used to predict two outputs (deflection of two types of strengthened RC beams). The relative error of predicted values was within 5% and the suitability of fit was close to 1.0 which affirms the efficacy of the FLES. Besides, a tiny difference was detected using triangular and trapezoidal MF for the prediction model.

Prediction of Deflection Behavior of NSM Strengthened Reinforced Concrete Beam Using Fuzzy Logic

Department of Nuclear Science & Engineering

Department of Civil Engineering, Faculty of Engineering

Selected Scientific Papers - Journal of Civil Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This paper aims to present a deflection prediction model of Near Surface Mounted (NSM) Reinforce Concrete (RC) beams using the Fuzzy Logic Expert System (FLES)...

Prediction of Deflection Behavior of NSM Strengthened Reinforced Concrete Beam Using Fuzzy Logic

Online veröffentlicht: 14. Jan. 2023

Seitenbereich: 1 - 10

DOI: https://doi.org/10.2478/sspjce-2022-0008

Schlüsselwörter
Artificial Intelligence, fuzzy logic, CFRP, prediction model

© 2022 K. M. Darain et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Prediction of Deflection Behavior of NSM Strengthened Reinforced Concrete Beam Using Fuzzy Logic

Online veröffentlicht: 14. Jan. 2023

Seitenbereich: 1 - 10

DOI: https://doi.org/10.2478/sspjce-2022-0008

SchlüsselwörterArtificial Intelligence, fuzzy logic, CFRP, prediction model

© 2022 K. M. Darain et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Artificial Intelligence, fuzzy logic, CFRP, prediction model