Cee 249 Seminar 1

Dr. James Alexander Goulet
Data-driven structural condition assessment for resilient and sustainable cities


Dr. James Alexander Goulet
Data-driven structural condition assessment for resilient and sustainable cities


Abstract

Despite the
technology available for monitoring structures in hazard-prone and aging
cities, data interpretation solutions for generating added value from
structural health monitoring (SHM) cannot yet justify costs. In this
seminar, we will explore affordable and scalable solutions
for post-earthquake rapid structural condition assessment and anomaly
detection during service conditions. Achieving this requires a change in
the way we use data; rather than seeking to infer exact structural
conditions through complex physics-based models, we need to search
for patterns and relationships in the characteristic responses of
a population of structures. In the first part of this seminar, we
will look at a data-driven methodology for post-earthquake rapid
structural condition assessment. This approach progressively learns
the relationship between frequency shifts and damage states as
a small number of buildings are inspected after an earthquake. We
then use this information to predict the safety state of uninspected
but monitored buildings. In the second part, we will look at a
probabilistic framework for forecasting the occurrence of anomalies
from trends identified in SHM-based structural
performance indicators. Here, anomalies do not necessarily refer to
discrete damage events, but more generally to any gradual or
sudden deterioration or malfunction occurring during the service life
of a structure. The methodologies presented will be used to identify
the challenges associated with a broader research roadmap to achieve
resilient and sustainable cities.