Скачать или смотреть Sujith Mangalathu: Analytics-Driven Models to Support the Design and Service-Life Management of...

Sujith Mangalathu, UCLA
May 1, 2020
Title: Analytics-Driven Models to Support the Design and Service-Life Management of Infrastructure Systems

CompSust Open Graduate Seminar (COGS)
http://www.compsust.net/cogs.php

Abstract:
The rapid increase in computational power, coupled with the improvements in numerical modeling and availability of performance data provides a unique opportunity to enhance the resilience and sustainability of spatially distributed infrastructure systems. This talk introduces the development of analytics-driven models to support the design and service-life management of infrastructure systems. Machine learning methods are used to perform rapid post-hazard-event assessments of the damage and functional state of impacted infrastructure systems and components. The analytics-driven framework is presented as a viable alternative to the traditional approach, which relies heavily on visual inspections and subjective estimates of the state of an infrastructure system. The machine learning methods presented in this talk are used to (1) develop bridge-specific fragility curves for more than 24,000 concrete box-girder California bridges, and (2) generate real-time estimates of the spatial distribution and severity of building damage following an earthquake.

Bio:
Sujith Mangalathu is a research scientist in Equifax in the field of adaptive machine learning and graph analysis for risk assessment. He obtained his Ph.D. from Georgia Institute of Technology, in 2017. He also spent two years as a post-doctoral research scholar at University of California, Los Angeles. His research is focused on the application of machine learning techniques for the risk assessment of structural and infrastructure systems with a special emphasis on bridge systems. He is also working on the performance-based grouping of structures for regional risk assessment and statistical decisions that support post-earthquake emergency response. He strives to develop innovative methods for regional risk assessment of structural systems using advanced statistical and machine learning techniques.