首页|Reports from University of Perugia Advance Knowledge in Machine Learning (A Doma in Adaptation Approach To Damage Classification With an Application To Bridge Mo nitoring)
Reports from University of Perugia Advance Knowledge in Machine Learning (A Doma in Adaptation Approach To Damage Classification With an Application To Bridge Mo nitoring)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Machine Learning are pre sented in a new report. According to news originating from Perugia, Italy, by Ne wsRx correspondents, research stated, "Data -driven machine-learning algorithms generally suffer from a lack of labelled health -state data, mainly those referr ing to damage conditions. To address such an issue, population -based structural health monitoring seeks to enrich the original dataset by transferring knowledg e from a population of monitored structures." Funders for this research include Ministry of Education, Universities and Resear ch (MIUR), FABRE Consortium, Engineering & Physical Sciences Resea rch Council (EPSRC), University of Perugia, Italy via the funded projects "Math4 Bridges"and "AIDMIX"in the internal research program fund. Our news journalists obtained a quote from the research from the University of P erugia, "Within this context, this paper presents a transfer learning approach, based on domain adaptation, to leverage information from completelylabelled brid ge structure data to accurately predict new instances of an unknown target domai n. Since intrinsic structural differences may cause distribution shifts, domain adaptation attempts to minimise the distance between the domains and to learn a mapping within a shared feature space. Specifically, the methodology involves th e long-term acquisition of natural frequencies from several structural scenarios . Such damage-sensitive features are then aligned via domain adaptation so that a machine-learning algorithm can effectively utilise the labelled source domain data and generalise well to the unlabelled target-domain data. The described pro cedure is applied to two case studies, including the Z24 and the S101 benchmark bridges and their finite element models, respectively."
PerugiaItalyEuropeCyborgsEmergin g TechnologiesMachine LearningUniversity of Perugia
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