| Title: |
Predicting degraded lifting capacity of aging tower cranes : a digital twin-driven approach |
| Authors: |
Hussain, M; Ye, Z; Chi, HL; Hsu, SC |
| Contributors: |
Department of Civil and Environmental Engineering; Department of Building and Real Estate |
| Publisher Information: |
Elsevier Ltd |
| Publication Year: |
2024 |
| Collection: |
Hong Kong Polytechnic University: PolyU Institutional Repository (PolyU IR) |
| Subject Terms: |
Tower crane; Digital twin; Lifting capacity; Safety monitoring; IoT system |
| Description: |
202404 bcch ; Accepted Manuscript ; Others ; The Hong Kong Polytechnic University Presidential Ph.D. Fellowship Scheme (PPPFS) ; Published ; Green (AAM) |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
https://hdl.handle.net/10397/105166; 59; 102310; a2668; 48037 |
| DOI: |
10.1016/j.aei.2023.102310 |
| Availability: |
https://hdl.handle.net/10397/105166; https://doi.org/10.1016/j.aei.2023.102310 |
| Rights: |
© 2023 Elsevier Ltd. All rights reserved. ; © 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ ; The following publication Hussain, M., Ye, Z., Chi, H.-L., & Hsu, S.-C. (2024). Predicting degraded lifting capacity of aging tower cranes: A digital twin-driven approach. Advanced Engineering Informatics, 59, 102310 is available at https://doi.org/10.1016/j.aei.2023.102310. |
| Accession Number: |
edsbas.3D6574D2 |
| Database: |
BASE |