Prediction of the impact force on reinforced concrete beams from a drop weight
It is always a challenge to efficiently and accurately estimate the force on structures from falling objects. This study aims to predict the maximum impact force on reinforced concrete beams subjected to drop-weight impact using artificial neural network. A new empirical model including a comprehens...
| Main Authors: | , |
|---|---|
| Format: | Journal Article |
| Published: |
Multi-Science Publishing
2016
|
| Online Access: | http://hdl.handle.net/20.500.11937/45590 |
| _version_ | 1848757328503898112 |
|---|---|
| author | Pham, Thong Hao, H. |
| author_facet | Pham, Thong Hao, H. |
| author_sort | Pham, Thong |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | It is always a challenge to efficiently and accurately estimate the force on structures from falling objects. This study aims to predict the maximum impact force on reinforced concrete beams subjected to drop-weight impact using artificial neural network. A new empirical model including a comprehensive version and a simplified version is proposed to estimate the maximum impact force. The model was verified against a database collected from the literature including 67 reinforced concrete beams tested under drop-weight impacts. The database covers the concrete strengths ranging from 23 to 47 MPa, the projectile mass from 150 to 500 kg, and the impact velocity up to 9.3 m/s. The prediction of the comprehensive version of the proposed model fits the experimental results very well with an average absolute error of 11.6%. The simplified version of the proposed model is established for easy estimation, with the average error of 23.2% in prediction of the maximum impact force. |
| first_indexed | 2025-11-14T09:26:21Z |
| format | Journal Article |
| id | curtin-20.500.11937-45590 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:26:21Z |
| publishDate | 2016 |
| publisher | Multi-Science Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-455902017-09-13T15:36:20Z Prediction of the impact force on reinforced concrete beams from a drop weight Pham, Thong Hao, H. It is always a challenge to efficiently and accurately estimate the force on structures from falling objects. This study aims to predict the maximum impact force on reinforced concrete beams subjected to drop-weight impact using artificial neural network. A new empirical model including a comprehensive version and a simplified version is proposed to estimate the maximum impact force. The model was verified against a database collected from the literature including 67 reinforced concrete beams tested under drop-weight impacts. The database covers the concrete strengths ranging from 23 to 47 MPa, the projectile mass from 150 to 500 kg, and the impact velocity up to 9.3 m/s. The prediction of the comprehensive version of the proposed model fits the experimental results very well with an average absolute error of 11.6%. The simplified version of the proposed model is established for easy estimation, with the average error of 23.2% in prediction of the maximum impact force. 2016 Journal Article http://hdl.handle.net/20.500.11937/45590 10.1177/1369433216649384 Multi-Science Publishing restricted |
| spellingShingle | Pham, Thong Hao, H. Prediction of the impact force on reinforced concrete beams from a drop weight |
| title | Prediction of the impact force on reinforced concrete beams from a drop weight |
| title_full | Prediction of the impact force on reinforced concrete beams from a drop weight |
| title_fullStr | Prediction of the impact force on reinforced concrete beams from a drop weight |
| title_full_unstemmed | Prediction of the impact force on reinforced concrete beams from a drop weight |
| title_short | Prediction of the impact force on reinforced concrete beams from a drop weight |
| title_sort | prediction of the impact force on reinforced concrete beams from a drop weight |
| url | http://hdl.handle.net/20.500.11937/45590 |