Effects of variability in experimental database on machine-learning-based prediction of ultimate load of circular concrete-filled steel tubes
Q1
Lưu vào:
| Tác giả chính: | |
|---|---|
| Đồng tác giả: | |
| Định dạng: | Bài Báo |
| Ngôn ngữ: | English |
| Nhà xuất bản: |
Measurement
2021
|
| Chủ đề: | |
| Truy cập trực tuyến: | https://www.sciencedirect.com/science/article/abs/pii/S0263224121002165?via%3Dihub#! https://dlib.phenikaa-uni.edu.vn/handle/PNK/1772 https://doi.org/10.1016/j.measurement.2021.109198 |
| Từ khóa: |
Thêm từ khóa
Không có từ khóa, Hãy là người đầu tiên đánh dấu biểu ghi này!
|
| id |
oai:localhost:PNK-1772 |
|---|---|
| record_format |
dspace |
| spelling |
oai:localhost:PNK-17722022-08-17T05:54:41Z Effects of variability in experimental database on machine-learning-based prediction of ultimate load of circular concrete-filled steel tubes Ho, Nang Xuan Le, Tien-Thinh Variability propagation Regression machine-learning models Circular concrete-filled steel tubes Support vector machine Q1 This study investigates the performance and robustness of regression machine-learning models in the presence of variability in the experimental database. The main objective of this work is to predict the ultimate load of circular concrete-filled steel tubes. The simulations were designed by combining size of the learning dataset, random realizations and prediction models. The variability (i.e. probability density function of each variable) is propagated to the output response through the regression machine-learning models. Results show that such variability must be considered when training and testing regression machine-learning models. The performance and robustness of the prediction models are presented and discussed. Based on the most robust and efficient model, a prediction equation is proposed for practical use. After conducting a comparison investigation, the performance of the proposed equation is found superior to one of current models. Finally, the proposed equation is implemented in Excel and appended to this paper. 2021-06-15T04:43:04Z 2021-06-15T04:43:04Z 2021 Article Working Paper https://www.sciencedirect.com/science/article/abs/pii/S0263224121002165?via%3Dihub#! https://dlib.phenikaa-uni.edu.vn/handle/PNK/1772 https://doi.org/10.1016/j.measurement.2021.109198 en Measurement |
| institution |
Digital Phenikaa |
| collection |
Digital Phenikaa |
| language |
English |
| topic |
Variability propagation Regression machine-learning models Circular concrete-filled steel tubes Support vector machine |
| spellingShingle |
Variability propagation Regression machine-learning models Circular concrete-filled steel tubes Support vector machine Ho, Nang Xuan Effects of variability in experimental database on machine-learning-based prediction of ultimate load of circular concrete-filled steel tubes |
| description |
Q1 |
| author2 |
Le, Tien-Thinh |
| author_facet |
Le, Tien-Thinh Ho, Nang Xuan |
| format |
Article |
| author |
Ho, Nang Xuan |
| author_sort |
Ho, Nang Xuan |
| title |
Effects of variability in experimental database on machine-learning-based prediction of ultimate load of circular concrete-filled steel tubes |
| title_short |
Effects of variability in experimental database on machine-learning-based prediction of ultimate load of circular concrete-filled steel tubes |
| title_full |
Effects of variability in experimental database on machine-learning-based prediction of ultimate load of circular concrete-filled steel tubes |
| title_fullStr |
Effects of variability in experimental database on machine-learning-based prediction of ultimate load of circular concrete-filled steel tubes |
| title_full_unstemmed |
Effects of variability in experimental database on machine-learning-based prediction of ultimate load of circular concrete-filled steel tubes |
| title_sort |
effects of variability in experimental database on machine-learning-based prediction of ultimate load of circular concrete-filled steel tubes |
| publisher |
Measurement |
| publishDate |
2021 |
| url |
https://www.sciencedirect.com/science/article/abs/pii/S0263224121002165?via%3Dihub#! https://dlib.phenikaa-uni.edu.vn/handle/PNK/1772 https://doi.org/10.1016/j.measurement.2021.109198 |
| _version_ |
1751856251773386752 |
| score |
8.891145 |