Given the devastating losses incurred by past major earthquake events together with the
ever-increasing global seismic exposures due to population growth and urbanization …

A scalar intensity measure (IM) could be insufficient to represent the earthquake intensity
and variety in fragility estimation. Introducing multiple IMs to conventional regression of …

Deep reinforcement learning (DRL) has emerged to be a promising alternative capable of
providing intelligent and proactive sequential decision-support for bridge asset …

Rapid reconnaissance of building damage is critical for disaster response and recovery.
Drones have been utilized to collect aerial images of affected areas in order to assess …

Many methods for seismic risk assessment rely on the selection of a seismic intensity
measure (IM) and the development of models of the seismic demand conditional on the IM …

Contemporary deep learning approaches for post-earthquake damage assessments based
on 2D convolutional neural networks (CNNs) require encoding of ground motion records to …

Traditional methods for seismic damage evaluation require manual extractions of intensity
measures (IMs) to properly represent the record-to-record variation of ground motions …

The seismic vulnerability of reinforced concrete (RC) highway bridges in the vicinity of
landslides is usually evaluated without considering the impact of sliding mass flow-type. The …

Ground motion selection is a crucial step in probabilistic seismic performance assessment of
structural systems. Particularly, identifying ground motion records compatible with a specific …

Intensity measure (IM) selection is a critical step in probabilistic seismic risk assessment
(PSRA). In many past studies, the efficiency of an IM, which quantifies its explanatory power …