Development of An Artificial Neural Network (ANN) Model for Improved Corrosion Management using Fly Ash Geopolymer Concrete (FAGC) in Marine Infrastructure Applications
OPC for marine infrastructure. Optimized mixes demonstrated a significant 65-70% reduction in chloride migration by 56–90 days, validating long-term durability despite higher early-age porosity. An Artificial Neural Network (ANN) model, with a precision of RMSE 0.007899, effectively predicted chlori...
| Main Author: | |
|---|---|
| Format: | Thesis |
| Published: |
Curtin University
2025
|
| Online Access: | http://hdl.handle.net/20.500.11937/98031 |
| _version_ | 1848766351959654400 |
|---|---|
| author | Shahedan, Noor Fifinatasha Binti |
| author_facet | Shahedan, Noor Fifinatasha Binti |
| author_sort | Shahedan, Noor Fifinatasha Binti |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | OPC for marine infrastructure. Optimized mixes demonstrated a significant 65-70% reduction in chloride migration by 56–90 days, validating long-term durability despite higher early-age porosity. An Artificial Neural Network (ANN) model, with a precision of RMSE 0.007899, effectively predicted chloride migration trends, enhancing corrosion management. These findings highlight geopolymer concrete's eco-friendly durability, reduced maintenance costs, and suitability for sustainable marine structures in harsh environments. |
| first_indexed | 2025-11-14T11:49:46Z |
| format | Thesis |
| id | curtin-20.500.11937-98031 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:49:46Z |
| publishDate | 2025 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-980312025-07-04T02:03:11Z Development of An Artificial Neural Network (ANN) Model for Improved Corrosion Management using Fly Ash Geopolymer Concrete (FAGC) in Marine Infrastructure Applications Shahedan, Noor Fifinatasha Binti OPC for marine infrastructure. Optimized mixes demonstrated a significant 65-70% reduction in chloride migration by 56–90 days, validating long-term durability despite higher early-age porosity. An Artificial Neural Network (ANN) model, with a precision of RMSE 0.007899, effectively predicted chloride migration trends, enhancing corrosion management. These findings highlight geopolymer concrete's eco-friendly durability, reduced maintenance costs, and suitability for sustainable marine structures in harsh environments. 2025 Thesis http://hdl.handle.net/20.500.11937/98031 Curtin University fulltext |
| spellingShingle | Shahedan, Noor Fifinatasha Binti Development of An Artificial Neural Network (ANN) Model for Improved Corrosion Management using Fly Ash Geopolymer Concrete (FAGC) in Marine Infrastructure Applications |
| title | Development of An Artificial Neural
Network (ANN) Model for Improved
Corrosion Management using Fly Ash
Geopolymer Concrete (FAGC) in Marine
Infrastructure Applications |
| title_full | Development of An Artificial Neural
Network (ANN) Model for Improved
Corrosion Management using Fly Ash
Geopolymer Concrete (FAGC) in Marine
Infrastructure Applications |
| title_fullStr | Development of An Artificial Neural
Network (ANN) Model for Improved
Corrosion Management using Fly Ash
Geopolymer Concrete (FAGC) in Marine
Infrastructure Applications |
| title_full_unstemmed | Development of An Artificial Neural
Network (ANN) Model for Improved
Corrosion Management using Fly Ash
Geopolymer Concrete (FAGC) in Marine
Infrastructure Applications |
| title_short | Development of An Artificial Neural
Network (ANN) Model for Improved
Corrosion Management using Fly Ash
Geopolymer Concrete (FAGC) in Marine
Infrastructure Applications |
| title_sort | development of an artificial neural
network (ann) model for improved
corrosion management using fly ash
geopolymer concrete (fagc) in marine
infrastructure applications |
| url | http://hdl.handle.net/20.500.11937/98031 |