The Library
Estimation of structural response using convolutional neural network : application to the Suramadu bridge
Tools
Tools
Tools
Pamuncak, Arya Panji, Salami, Mohammad Reza, Adha, Augusta, Budiono, Bambang and Laory, Irwanda (2021) Estimation of structural response using convolutional neural network : application to the Suramadu bridge. Engineering Computations, 38 (10). pp. 4047-4065. doi:10.1108/EC-12-2020-0695 ISSN 0264-4401.
Research output not available from this repository.
Request-a-Copy directly from author or use local Library Get it For Me service.
Official URL: https://doi.org/10.1108/EC-12-2020-0695
Abstract
Purpose: Structural health monitoring (SHM) has gained significant attention due to its capability in providing support for efficient and optimal bridge maintenance activities. However, despite the promising potential, the effectiveness of SHM system might be hindered by unprecedented factors that impact the continuity of data collection. This research presents a framework utilising convolutional neural network (CNN) for estimating structural response using environmental variations.
Design/methodology/approach:
The CNN framework is validated using monitoring data from the Suramadu bridge monitoring system. Pre-processing is performed to transform the data into data frames, each containing a sequence of data. The data frames are divided into training, validation and testing sets. Both the training and validation sets are employed to train the CNN models while the testing set is utilised for evaluation by calculating error metrics such as mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE). Comparison with other machine learning approaches is performed to investigate the effectiveness of the CNN framework.
Findings:
The CNN models are able to learn the trend of cable force sensor measurements with the ranges of MAE between 10.23 kN and 19.82 kN, MAPE between 0.434% and 0.536% and RMSE between 13.38 kN and 25.32 kN. In addition, the investigation discovers that the CNN-based model manages to outperform other machine learning models.
Originality/value:
This work investigates, for the first time, how cable stress can be estimated using temperature variations. The study presents the first application of 1-D CNN regressor on data collected from a full-scale bridge. This work also evaluates the comparison between CNN regressor and other techniques, such as artificial neutral network (ANN) and linear regression, in estimating bridge cable stress, which has not been performed previously.
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
| Journal or Publication Title: | Engineering Computations | ||||||
| Publisher: | Emerald Group Publishing Ltd. | ||||||
| ISSN: | 0264-4401 | ||||||
| Official Date: | 7 December 2021 | ||||||
| Dates: |
|
||||||
| Volume: | 38 | ||||||
| Number: | 10 | ||||||
| Page Range: | pp. 4047-4065 | ||||||
| DOI: | 10.1108/EC-12-2020-0695 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Restricted or Subscription Access |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
