Computer vision‐based techniques were developed to overcome the limitations of visual
inspection by trained human resources and to detect structural damage in images remotely …

Current methods of practice for inspection of civil infrastructure typically involve visual
assessments conducted manually by trained inspectors. For post-earthquake structural …

Deep learning‐based structural damage detection methods overcome the limitation of
inferior adaptability caused by extensively varying real‐world situations (eg, lighting and …

Many bridge structures, one of the most critical components in transportation infrastructure
systems, exhibit signs of deteriorations and are approaching or beyond the initial design …

With the growing number of aging infrastructure across the world, there is a high demand for
a more effective inspection method to assess its conditions. Routine assessment of structural …

Visual damage inspection of steel frames by eyes alone is time-consuming and
cumbersome; therefore, it produces inconsistent results. Existing computer vision-based …

A number of image processing techniques (IPTs) have been implemented for detecting civil
infrastructure defects to partially replace human‐conducted onsite inspections. These IPTs …

Timely assessment of damages induced to buildings due to an earthquake is critical for
ensuring life safety, mitigating financial losses, and expediting the rehabilitation process as …

Reinforced concrete bridge substructures are one of the most important road components,
requiring routine maintenance for road safety. These structures initially require visual …

Remotely sensed data can provide the basis for timely and efficient building damage maps
that are of fundamental importance to support the response activities following disaster …