Prediction of Ultimate Strain and Strength of FRP-Confined Concrete Cylinders Using Soft Computing Methods

Prediction of Ultimate Strain and Strength of FRP-Confined Concrete Cylinders Using Soft Computing Methods

This paper investigates the effectiveness of four different soft computing methods, namely radial basis neural network (RBNN), adaptive neuro fuzzy inference system (ANFIS) with subtractive clustering (ANFIS-SC), ANFIS with fuzzy c-means clustering (ANFIS-FCM) and M5 model tree (M5Tree), for predict...

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Main Authors: Iman Mansouri, Ozgur Kisi, Pedram Sadeghian, Chang-Hwan Lee, Jong Wan Hu
Format: Article
Language:English
Published: MDPI AG 2017-07-01
Series:Applied Sciences
Subjects:
fiber reinforced polymer
concrete
column
confinement
stress
strain
model
Online Access:https://www.mdpi.com/2076-3417/7/8/751
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spelling doaj-587da3ba074a4ca69e369696ce6314262020-11-24T21:23:14ZengMDPI AGApplied Sciences2076-34172017-07-017875110.3390/app7080751app7080751Prediction of Ultimate Strain and Strength of FRP-Confined Concrete Cylinders Using Soft Computing MethodsIman Mansouri0Ozgur Kisi1Pedram Sadeghian2Chang-Hwan Lee3Jong Wan Hu4Department of Civil Engineering, Birjand University of Technology, Birjand 97175-569, IranSchool of Natural Sciences and Engineering, Ilia State University, Tbilisi 0162, GeorgiaDepartment of Civil and Resource Engineering, Dalhousie University, 1360 Barrington Street, Halifax, NS B3H 4R2, CanadaResearch Institute of Structural Engineering & System, DongYang Structural Engineers Co., Ltd., Seoul 05836, KoreaDepartment of Civil and Environmental Engineering, Incheon National University, Incheon 22012, KoreaThis paper investigates the effectiveness of four different soft computing methods, namely radial basis neural network (RBNN), adaptive neuro fuzzy inference system (ANFIS) with subtractive clustering (ANFIS-SC), ANFIS with fuzzy c-means clustering (ANFIS-FCM) and M5 model tree (M5Tree), for predicting the ultimate strength and strain of concrete cylinders confined with fiber-reinforced polymer (FRP) sheets. The models were compared according to the root mean square error (RMSE), mean absolute relative error (MARE) and determination coefficient (R2) criteria. Similar accuracy was obtained by RBNN and ANFIS-FCM, and they provided better estimates in modeling ultimate strength of confined concrete. The ANFIS-SC, however, performed slightly better than the RBNN and ANFIS-FCM in estimating ultimate strain of confined concrete, and M5Tree provided the worst strength and strain estimates. Finally, the effects of strain ratio and the confinement stiffness ratio on strength and strain were investigated, and the confinement stiffness ratio was shown to be more effective.https://www.mdpi.com/2076-3417/7/8/751fiber reinforced polymerconcretecolumnconfinementstressstrainmodel
collection DOAJ
language English
format Article
sources DOAJ
author Iman Mansouri
Ozgur Kisi
Pedram Sadeghian
Chang-Hwan Lee
Jong Wan Hu
spellingShingle Iman Mansouri
Ozgur Kisi
Pedram Sadeghian
Chang-Hwan Lee
Jong Wan Hu
Prediction of Ultimate Strain and Strength of FRP-Confined Concrete Cylinders Using Soft Computing Methods
Applied Sciences
fiber reinforced polymer
concrete
column
confinement
stress
strain
model
author_facet Iman Mansouri
Ozgur Kisi
Pedram Sadeghian
Chang-Hwan Lee
Jong Wan Hu
author_sort Iman Mansouri
title Prediction of Ultimate Strain and Strength of FRP-Confined Concrete Cylinders Using Soft Computing Methods
title_short Prediction of Ultimate Strain and Strength of FRP-Confined Concrete Cylinders Using Soft Computing Methods
title_full Prediction of Ultimate Strain and Strength of FRP-Confined Concrete Cylinders Using Soft Computing Methods
title_fullStr Prediction of Ultimate Strain and Strength of FRP-Confined Concrete Cylinders Using Soft Computing Methods
title_full_unstemmed Prediction of Ultimate Strain and Strength of FRP-Confined Concrete Cylinders Using Soft Computing Methods
title_sort prediction of ultimate strain and strength of frp-confined concrete cylinders using soft computing methods
publisher MDPI AG
series Applied Sciences
issn 2076-3417
publishDate 2017-07-01
description This paper investigates the effectiveness of four different soft computing methods, namely radial basis neural network (RBNN), adaptive neuro fuzzy inference system (ANFIS) with subtractive clustering (ANFIS-SC), ANFIS with fuzzy c-means clustering (ANFIS-FCM) and M5 model tree (M5Tree), for predicting the ultimate strength and strain of concrete cylinders confined with fiber-reinforced polymer (FRP) sheets. The models were compared according to the root mean square error (RMSE), mean absolute relative error (MARE) and determination coefficient (R2) criteria. Similar accuracy was obtained by RBNN and ANFIS-FCM, and they provided better estimates in modeling ultimate strength of confined concrete. The ANFIS-SC, however, performed slightly better than the RBNN and ANFIS-FCM in estimating ultimate strain of confined concrete, and M5Tree provided the worst strength and strain estimates. Finally, the effects of strain ratio and the confinement stiffness ratio on strength and strain were investigated, and the confinement stiffness ratio was shown to be more effective.
topic fiber reinforced polymer
concrete
column
confinement
stress
strain
model
url https://www.mdpi.com/2076-3417/7/8/751
work_keys_str_mv AT imanmansouri predictionofultimatestrainandstrengthoffrpconfinedconcretecylindersusingsoftcomputingmethods
AT ozgurkisi predictionofultimatestrainandstrengthoffrpconfinedconcretecylindersusingsoftcomputingmethods
AT pedramsadeghian predictionofultimatestrainandstrengthoffrpconfinedconcretecylindersusingsoftcomputingmethods
AT changhwanlee predictionofultimatestrainandstrengthoffrpconfinedconcretecylindersusingsoftcomputingmethods
AT jongwanhu predictionofultimatestrainandstrengthoffrpconfinedconcretecylindersusingsoftcomputingmethods
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