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Grant Award View - GA281660
Assessment of Dynamic Pile Driving Using Machine Learning
GA ID:
GA281660
Agency:
Australian Research Council
Approval Date:
19-Jan-2023
Publish Date:
1-Feb-2023
Category:
Humanities, Arts and Social Sciences (HASS) Research
Grant Term:
1-Jun-2023 to 31-May-2026
Original: 19-Jan-2023 to 31-Dec-2025
Value (AUD):
$412,353.00
(GST inclusive where applicable)
Variations:
- GA281660-V1 - Variation to Grant (26-Jun-2024 )
One-off/Ad hoc:
No
Aggregate Grant Award:
No
PBS Program Name:
ARC 22/23 Discovery
Grant Program:
Discovery Projects
Grant Activity:
Assessment of Dynamic Pile Driving Using Machine Learning
Purpose:
This project aims at developing new technology to determine ground properties and foundation capacity in real-time during pile installation by adopting rigorous numerical simulation, laboratory experiments and artificial intelligence-based computational model. Although impact driving is used commonly to install piles on site, there is no technology currently available to interpret collected data accurately and in real-time to provide live feedback and optimise construction processes. This research will provide new machine learning model to assess the ground and foundation characteristics during construction, and will increase certainty in infrastructure investment in Australia particularly for costly transport assets and infrastructure.
GO ID:
GO Title:
Discovery Projects for funding commencing in 2023
Internal Reference ID:
DP23 Round 1
Selection Process:
Targeted or Restricted Competitive
Confidentiality - Contract:
No
Confidentiality - Outputs:
No
Grant Recipient Details
Recipient Name:
University of Technology Sydney
Recipient ABN:
77 257 686 961
Grant Recipient Location
Suburb:
ULTIMO
Town/City:
ULTIMO
Postcode:
2007
State/Territory:
NSW
Country:
AUSTRALIA
Grant Delivery Location
State/Territory:
NSW
Postcode:
2007
Country:
AUSTRALIA