{"links":{"self":"http://dataportal.arc.gov.au/NCGP/API/grants/DE260101471"},"data":{"type":"grant-details","id":"DE260101471","attributes":{"code":"DE260101471","administering-organisation":"Griffith University","announcement-administering-organisation":"Griffith University","scheme-name":"Discovery Early Career Researcher Award","grant-status":"Active","funding-commencement-year":2026,"years-funded":3,"project-start-date":"2026-01-01","anticipated-end-date":"2028-12-31","grant-summary":"Spatiotemporally Correlated Population-based Structural Health Monitoring. This project aims to advance infrastructure health monitoring by using sensor data from a structural population, focusing on the collective behavior of similar structures to enhance the accuracy and reliability of condition assessments. The project will generate new knowledge by developing novel transfer learning methods across structures and assessing structural conditions without monitoring systems. Expected outcomes include improved safety and lifespan of structures and establishing a new standard for infrastructure monitoring. This research should provide significant benefits, such as enhanced safety, extended lifespan, and reduced maintenance costs, leading to more efficient resource allocation in infrastructure management.","funding-current":520959.00,"funding-at-announcement":516778,"investigators-current":[{"title":"Dr","firstName":"Xiaoyou","familyName":"WANG","roleName":"Discovery Early Career Researcher Award","roleCode":"DECRA","isFellowship":true,"orcidIdentifier":"0000-0002-4588-2460 "}],"investigators-at-announcement":[{"title":"Dr","firstName":"Xiaoyou","familyName":"WANG","roleName":"Discovery Early Career Researcher Award","roleCode":"DECRA","isFellowship":true,"orcidIdentifier":"0000-0002-4588-2460 "}],"organisations-current":[{"organisationName":"Griffith University","roleName":"Administering Organisation","state":"QLD"}],"organisations-at-announcement":[{"organisationName":"Griffith University","roleName":"Administering Organisation","state":"QLD"}],"field-of-research":[{"isPrimary":true,"code":"4005","name":"Civil Engineering","type":"FOR20"},{"isPrimary":false,"code":"400510","name":"Structural Engineering","type":"FOR20"}],"socio-economic-objective":[{"code":"120101","name":"Civil Building Management and Services","type":"SEO20"},{"code":"280110","name":"Expanding Knowledge In Engineering","type":"SEO20"}],"international-collaboration":["Hong Kong (SAR of China)"],"lief-register":[],"achievement-summary":null,"national-interest-test-statement":"This project will revolutionize how we monitor and maintain infrastructure by developing a population-based Structural Health Monitoring (SHM) system. Traditionally, SHM focuses on individual structures, but this project considers the collective behavior of similar structures, enhancing the accuracy and reliability of condition assessments. This addresses a critical research gap in effectively managing long-service structures, which often lack SHM systems. By leveraging measurement data and effective knowledge transfer methods, the condition of structures without SHM systems can be predicted, reducing the need for frequent inspections and extending their lifespan. Economically, the project reduces maintenance costs and inspection frequencies, leading to significant savings. Socially, it ensures the safety and reliability of infrastructure, minimizing disruptions for building occupants and communities. Environmentally, the project aligns with Australia's goal of transitioning to a net-zero future by reducing energy consumption associated with extensive sensor installations. By demonstrating the effectiveness of the developed methodologies, the project aims to set a new standard in SHM practices, both in Australia and internationally, enhancing the safety and longevity of infrastructure systems worldwide."}}}