P2.51ARC Asset Intelligence: Maximising Operational Effectiveness for the Digital Era
Project Number
P2.51ARCRound
Round 5Date
October 2019 - September 2022Research Team
Chair, Project Steering Group
Professor Keith Hampson
BEng(Civil)(Hons) MBA PhD
FIEAust FAIM FAICD
k.hampson@sbenrc.com.au
Chief Investigator
Professor Xiangyu Wang
Project Leader
xiangyu.wang@curtin.edu.au
Project Leader
Professor Peng Wu
BSc MSc PhD
Curtin University
Peng.Wu@curtin.edu.au
Documents for Downloading
Publications
Journal article: Xu, S.; Wang, J.; Wu, P.; Shou, W.; Wang, X.; Chen, M. (April 2021). Vision Based Pavement Marking Detection and Condition Assessment – A Case Study Appl. Sci. 2021, 11, 3152. https://doi.org/10.3390/app11073152. The conference, ICCCBE/CIB W78 2020, was held on 18-20 August 2020 and in the form of virtual joint conference.
Last Updated: 2024-08-20 11:42:46
October 2019 – September 2022
The primary aim of this project is to develop a structured, integrated and shared water asset management platform through advanced sensing, digital modelling and computational intelligence so as to maximise the planning and operational effectiveness of water utilities in maintenance, repair and rehabilitation. The research intends to address an important gap of isolated water data management by providing relational information, interoperability, and real-time capability. The expected outcome will be an innovative digital water asset management platform that is a single source of truth for centralised storage, access, analysis of structured water asset data to reduce the cost and time of information storage, retrieval and decision making.
Objectives
The project will develop:
- To holistically map the lifecycle asset information requirements of water assets, including not only the spatial structure of the asset, but also lifecycle information on inspection, performance and maintenance.
- To develop an innovative and practical approach that can help transfer digital water asset data from one platform to another without the need to creating separate mapping for every model, platform and stakeholder.
- To collect and integrate real-time water asset performance information into the digital platform
- To develop a data-driven performance prediction model and a non-dominated sorting genetic algorithm (NSGA) for many-objective performance optimisation of water assets.
Industry Outcomes
- The primary aim of this project is to develop a structured, integrated and shared water asset management platform through advanced sensing, digital modelling and computational intelligence so as to maximise the planning and operational effectiveness of water utilities in maintenance, repair and rehabilitation.
- The research intends to address an important gap of isolated water data management by providing relational information, interoperability, and real-time capability.
- The expected outcome will be an innovative digital water asset management platform that is a single source of truth for centralised storage, access, analysis of structured water asset data to reduce the cost and time of information storage, retrieval and decision making.
