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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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 |
_version_ |
1725992841084141568 |