Data

Improved Management of Australian Port Infrastructure by Development of Predictive Ageing Simulation: data

Monash University
Assoc Prof Nick Birbilis (Associated with) Dr Frank Collins (Aggregated by) Dr R Zou (Associated with)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=1959.1/470439&rft.title=Improved Management of Australian Port Infrastructure by Development of Predictive Ageing Simulation: data&rft.identifier=1959.1/470439&rft.publisher=Monash University&rft.description=The Monash dataset generated from this ARC Linkage Project contains newly derived data from the raw data collected from port authorities Australia wide. The project receives funding from two port authorities and Architecture, Engineering, Consulting, Operations and Management (AECOM), previously Maunsell. It is led by Frank Collins from Monash University who is working with co-investigators: Nick Birbilis (Monash University); Tom Molyneaux, David Law, Jennifer Fernandes (significant partners from Royal Melbourne Institute of Technology) and Marita Berndt, Frederic Blin, Miles Dacre from AECOM. The aim of the research is to be able to create predictive models for port authorities to manage the service life of these structures through various stages and conditions. Data has been collected from condition records, surveys and non-destructive testing supplied by industry partners and analysis of literature provides material properties and structural properties so that the source data contains properties of steel and concrete and types of iron oxide rust that evolve from the corrosion as well as data for loss of bond, crack prediction and micro crack prediction. Most previous models have been one-dimensional but this dataset is analysed using a three-dimensional numerical model created using finite element analysis with Advanced Tool for Engineering Nonlinear Analysis (ATENA) software by Roger Zou of Monash University. Eventually a website will give access to a database to visualise corrosion prediction in order to better manage in-service durability. Analysis of the dataset has created for the first time a three-dimensional model which enables an asset manager to visualise an existing port structure. It will be possible to predict different types of deterioration due to aggressive marine conditions causing corrosion, according to geographic location, weather and environmental conditions and to compare the performance of the different remediation options in order to maintain maximum service life by reducing deterioration. Users of the web site will be able to see a snap shot of the existing condition, which can be 'aged' in real time so that it will show, for example, likely conditions 20 years in future. &rft.creator=Dr Frank Collins&rft.date=2012&rft.coverage=AU&rft_subject=Construction Materials&rft_subject=ENGINEERING&rft_subject=CIVIL ENGINEERING&rft_subject=Civil Engineering not elsewhere classified&rft_subject=090305 &rft_subject=Maritime Engineering not elsewhere classified&rft_subject=MARITIME ENGINEERING&rft_subject=090505 &rft_subject=Corrosion&rft_subject=Predictive modelling &rft_subject=Reinforced concrete&rft_subject=3-D damage&rft_subject=Maritime structures&rft.type=dataset&rft.language=English Access the data
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All rights reserved. Newly derived data belongs to Frank Collins and the partner organisations.

Access to the data is restricted to the project investigators. The partners may be willing to release some data after negotiation. Refer to the "Point of contact" to discuss terms and conditions.

Full description

The Monash dataset generated from this ARC Linkage Project contains newly derived data from the raw data collected from port authorities Australia wide. The project receives funding from two port authorities and Architecture, Engineering, Consulting, Operations and Management (AECOM), previously Maunsell. It is led by Frank Collins from Monash University who is working with co-investigators: Nick Birbilis (Monash University); Tom Molyneaux, David Law, Jennifer Fernandes (significant partners from Royal Melbourne Institute of Technology) and Marita Berndt, Frederic Blin, Miles Dacre from AECOM. The aim of the research is to be able to create predictive models for port authorities to manage the service life of these structures through various stages and conditions. Data has been collected from condition records, surveys and non-destructive testing supplied by industry partners and analysis of literature provides material properties and structural properties so that the source data contains properties of steel and concrete and types of iron oxide rust that evolve from the corrosion as well as data for loss of bond, crack prediction and micro crack prediction. Most previous models have been one-dimensional but this dataset is analysed using a three-dimensional numerical model created using finite element analysis with Advanced Tool for Engineering Nonlinear Analysis (ATENA) software by Roger Zou of Monash University. Eventually a website will give access to a database to visualise corrosion prediction in order to better manage in-service durability.

Notes

Equations (pdf); spreadsheets containing raw data (loss of bond data; crack prediction and micro crack prediction; properties of steel and concrete; types of rust, iron oxide) (xlsx). analysed literature review an (docx; pdf).

Significance statement

Analysis of the dataset has created for the first time a three-dimensional model which enables an asset manager to visualise an existing port structure. It will be possible to predict different types of deterioration due to aggressive marine conditions causing corrosion, according to geographic location, weather and environmental conditions and to compare the performance of the different remediation options in order to maintain maximum service life by reducing deterioration. Users of the web site will be able to see a snap shot of the existing condition, which can be 'aged' in real time so that it will show, for example, likely conditions 20 years in future.

Data time period: 1980 to 2010

This dataset is part of a larger collection

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iso31661: AU

Subjects
090305 | 090505 | 3-D damage | Civil Engineering | Civil Engineering Not Elsewhere Classified | Construction Materials | Corrosion | Engineering | Maritime Engineering | Maritime Engineering Not Elsewhere Classified | Maritime structures | Predictive modelling | Reinforced concrete |

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Identifiers
ACN 633 798 857