Adaptive-neuro-fuzzy-inference-system model for prediction of ultimate load of rectangular concrete-filled steel tubular columns
This study is devoted to the development of an Adaptive-Neuro-Fuzzy-Inference-System (ANFIS) model for the prediction of ultimate load of rectangular concrete-filled steel tubular structural members. The learning process of the model is performed by conducting a combination of backpropagation gradie...
Lưu vào:
| Tác giả chính: | , , , |
|---|---|
| Định dạng: | Bài trích |
| Ngôn ngữ: | English |
| Nhà xuất bản: |
IOS Press
2022
|
| Chủ đề: | |
| Truy cập trực tuyến: | https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs201628 https://dlib.phenikaa-uni.edu.vn/handle/PNK/5927 https://doi.org/10.3233/jifs-201628 |
| Từ khóa: |
Thêm từ khóa
Không có từ khóa, Hãy là người đầu tiên đánh dấu biểu ghi này!
|
| id |
oai:localhost:PNK-5927 |
|---|---|
| record_format |
dspace |
| spelling |
oai:localhost:PNK-59272022-08-17T05:54:56Z Adaptive-neuro-fuzzy-inference-system model for prediction of ultimate load of rectangular concrete-filled steel tubular columns T.H. Duong T.-T. Le S.X. Nguyen M.V. Le Concrete-filled steel tubular members Adaptive-neuro-fuzzy-inference-system This study is devoted to the development of an Adaptive-Neuro-Fuzzy-Inference-System (ANFIS) model for the prediction of ultimate load of rectangular concrete-filled steel tubular structural members. The learning process of the model is performed by conducting a combination of backpropagation gradient descent and least-squares techniques. The performance of the model is examined by several quality metrics such as coefficient of determination (R2), Root-Mean-Squared-Error (RMSE), Mean-Absolute-Error (MAE), Index of Agreement (IA) and Slope of linear regression. Monte Carlo random sampling technique is employed to propagate input variations to the output response. Moreover, the performance of ANFIS is also compared with other machine learning models including Artificial Neural Network (ANN), Support Vector Machine (SVM), Gaussian Process Regression (GPR) and Ensemble. Results show that the ANFIS model yields higher prediction performance than other machine learning models, for both training and testing data points and regarding all quality metrics. For instance, using training data points, the ANFIS model exhibits a RMSE of 0.0283 compared to 0.0342, 0.0588, 0.0291, and 0.0464 using ANN, Ensemble, GPR, and SVM, respectively (the corresponding gain values are+17.3%,+51.9%,+2.8%, and+39.0%, respectively). On the other hand, using testing data points, the ANFIS model exhibits a RMSE of 0.0276 compared to 0.0393, 0.0987, 0.0403, and 0.0460 using ANN, Ensemble, GPR, and SVM, respectively (the corresponding values of gain are+29.8%,+72.1%,+31.5%, and+40.0%, respectively). The same observation can be made for other quality metrics. It can be concluded that the ANFIS model outperforms other models for both training and testing datasets. The ANFIS model is also compared with existing works in the past, showing its improvement in prediction results. Finally, sensitivity analysis is performed to determine the degree of effect of the input parameters on the ultimate load. 2022-07-13T01:59:57Z 2022-07-13T01:59:57Z 2022 Bài trích https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs201628 https://dlib.phenikaa-uni.edu.vn/handle/PNK/5927 https://doi.org/10.3233/jifs-201628 en IOS Press |
| institution |
Digital Phenikaa |
| collection |
Digital Phenikaa |
| language |
English |
| topic |
Concrete-filled steel tubular members Adaptive-neuro-fuzzy-inference-system |
| spellingShingle |
Concrete-filled steel tubular members Adaptive-neuro-fuzzy-inference-system T.H. Duong T.-T. Le S.X. Nguyen M.V. Le Adaptive-neuro-fuzzy-inference-system model for prediction of ultimate load of rectangular concrete-filled steel tubular columns |
| description |
This study is devoted to the development of an Adaptive-Neuro-Fuzzy-Inference-System (ANFIS) model for the prediction of ultimate load of rectangular concrete-filled steel tubular structural members. The learning process of the model is performed by conducting a combination of backpropagation gradient descent and least-squares techniques. The performance of the model is examined by several quality metrics such as coefficient of determination (R2), Root-Mean-Squared-Error (RMSE), Mean-Absolute-Error (MAE), Index of Agreement (IA) and Slope of linear regression. Monte Carlo random sampling technique is employed to propagate input variations to the output response. Moreover, the performance of ANFIS is also compared with other machine learning models including Artificial Neural Network (ANN), Support Vector Machine (SVM), Gaussian Process Regression (GPR) and Ensemble. Results show that the ANFIS model yields higher prediction performance than other machine learning models, for both training and testing data points and regarding all quality metrics. For instance, using training data points, the ANFIS model exhibits a RMSE of 0.0283 compared to 0.0342, 0.0588, 0.0291, and 0.0464 using ANN, Ensemble, GPR, and SVM, respectively (the corresponding gain values are+17.3%,+51.9%,+2.8%, and+39.0%, respectively). On the other hand, using testing data points, the ANFIS model exhibits a RMSE of 0.0276 compared to 0.0393, 0.0987, 0.0403, and 0.0460 using ANN, Ensemble, GPR, and SVM, respectively (the corresponding values of gain are+29.8%,+72.1%,+31.5%, and+40.0%, respectively). The same observation can be made for other quality metrics. It can be concluded that the ANFIS model outperforms other models for both training and testing datasets. The ANFIS model is also compared with existing works in the past, showing its improvement in prediction results. Finally, sensitivity analysis is performed to determine the degree of effect of the input parameters on the ultimate load. |
| format |
Bài trích |
| author |
T.H. Duong T.-T. Le S.X. Nguyen M.V. Le |
| author_facet |
T.H. Duong T.-T. Le S.X. Nguyen M.V. Le |
| author_sort |
T.H. Duong |
| title |
Adaptive-neuro-fuzzy-inference-system model for prediction of ultimate load of rectangular concrete-filled steel tubular columns |
| title_short |
Adaptive-neuro-fuzzy-inference-system model for prediction of ultimate load of rectangular concrete-filled steel tubular columns |
| title_full |
Adaptive-neuro-fuzzy-inference-system model for prediction of ultimate load of rectangular concrete-filled steel tubular columns |
| title_fullStr |
Adaptive-neuro-fuzzy-inference-system model for prediction of ultimate load of rectangular concrete-filled steel tubular columns |
| title_full_unstemmed |
Adaptive-neuro-fuzzy-inference-system model for prediction of ultimate load of rectangular concrete-filled steel tubular columns |
| title_sort |
adaptive-neuro-fuzzy-inference-system model for prediction of ultimate load of rectangular concrete-filled steel tubular columns |
| publisher |
IOS Press |
| publishDate |
2022 |
| url |
https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs201628 https://dlib.phenikaa-uni.edu.vn/handle/PNK/5927 https://doi.org/10.3233/jifs-201628 |
| _version_ |
1751856288919191552 |
| score |
8.887929 |