Automated regional seismic damage assessment of buildings using an unmanned aerial vehicle and a convolutional neural network

C Xiong, Q Li, X Lu - Automation in Construction, 2020 - Elsevier
A rapid assessment of the seismic damage to buildings can facilitate improved emergency
response and timely relief in earthquake-prone areas. In this study, an automated building …

Classification of building damage using a novel convolutional neural network based on post-disaster aerial images

Z Hong, H Zhong, H Pan, J Liu, R Zhou, Y Zhang… - Sensors, 2022 - mdpi.com
The accurate and timely identification of the degree of building damage is critical for disaster
emergency response and loss assessment. Although many methods have been proposed …

Improved CNN classification method for groups of buildings damaged by earthquake, based on high resolution remote sensing images

H Ma, Y Liu, Y Ren, D Wang, L Yu, J Yu - Remote Sensing, 2020 - mdpi.com
Effective extraction of disaster information of buildings from remote sensing images is of
great importance to supporting disaster relief and casualty reduction. In high-resolution …

Transferability of convolutional neural network models for identifying damaged buildings due to earthquake

W Yang, X Zhang, P Luo - Remote Sensing, 2021 - mdpi.com
The collapse of buildings caused by earthquakes can lead to a large loss of life and
property. Rapid assessment of building damage with remote sensing image data can …

A deep learning approach to rapid regional post‐event seismic damage assessment using time‐frequency distributions of ground motions

X Lu, Y Xu, Y Tian, B Cetiner… - … Engineering & Structural …, 2021 - Wiley Online Library
Every year, earthquakes result in severe economic losses and a significant number of
casualties worldwide. In limiting the losses that occur after these extreme events, timely and …

Structural building damage detection with deep learning: Assessment of a state-of-the-art CNN in operational conditions

F Nex, D Duarte, FG Tonolo, N Kerle - Remote sensing, 2019 - mdpi.com
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 …

A framework of structural damage detection for civil structures using a combined multi-scale convolutional neural network and echo state network

Y He, L Zhang, Z Chen, CY Li - Engineering with Computers, 2023 - Springer
Structural health monitoring (SHM) has become a notable method to ensure structural
safety, yet the ability of existing damage detection techniques need improvements on …

Post‐earthquake damage recognition and condition assessment of bridges using UAV integrated with deep learning approach

XW Ye, SY Ma, ZX Liu, Y Ding, ZX Li… - Structural Control and …, 2022 - Wiley Online Library
Rapid and accurate assessment of the damage to bridge structures after an earthquake can
provide a basis for decision‐making regarding post‐earthquake emergency work. However …

Real‐time crack assessment using deep neural networks with wall‐climbing unmanned aerial system

S Jiang, J Zhang - Computer‐Aided Civil and Infrastructure …, 2020 - Wiley Online Library
Crack information provides important evidence of structural degradation and safety in civil
structures. Existing inspection methods are inefficient and difficult to rapidly deploy. A real …

Geometric consistency enhanced deep convolutional encoder-decoder for urban seismic damage assessment by UAV images

Y Wang, X Jing, L Cui, C Zhang, Y Xu, J Yuan… - Engineering …, 2023 - Elsevier
Precise and rapid assessment of seismic damage to buildings is critical for urban regions.
To address this challenge, this study proposes QuakeCityNet (QCNet-MN)-a model with …