Deep learning-based automatic volumetric damage quantification using depth camera
GH Beckman, D Polyzois, YJ Cha - Automation in Construction, 2019 - Elsevier
A depth camera or 3-dimensional scanner was used as a sensor for traditional methods to
quantify the identified concrete spalling damage in terms of volume. However, to quantify the …
quantify the identified concrete spalling damage in terms of volume. However, to quantify the …
Computer vision-based concrete crack detection using U-net fully convolutional networks
For the first time, U-Net is adopted to detect the concrete cracks in the present study. Focal
loss function is selected as the evaluation function, and the Adam algorithm is applied for …
loss function is selected as the evaluation function, and the Adam algorithm is applied for …
A research on an improved Unet-based concrete crack detection algorithm
L Zhang, J Shen, B Zhu - Structural Health Monitoring, 2021 - journals.sagepub.com
Crack is an important indicator for evaluating the damage level of concrete structures.
However, traditional crack detection algorithms have complex implementation and weak …
However, traditional crack detection algorithms have complex implementation and weak …
Multi-sensor and decision-level fusion-based structural damage detection using a one-dimensional convolutional neural network
S Teng, G Chen, Z Liu, L Cheng, X Sun - Sensors, 2021 - mdpi.com
This paper presents a novel approach to substantially improve the detection accuracy of
structural damage via a one-dimensional convolutional neural network (1-D CNN) and a …
structural damage via a one-dimensional convolutional neural network (1-D CNN) and a …
Postdisaster image‐based damage detection and repair cost estimation of reinforced concrete buildings using dual convolutional neural networks
Reinforced concrete (RC) buildings are commonly used around the world. With recent
earthquakes worldwide, rapid structural damage inspection and repair cost evaluation are …
earthquakes worldwide, rapid structural damage inspection and repair cost evaluation are …
A review of the research and application of deep learning-based computer vision in structural damage detection
Z Lingxin, S Junkai, Z Baijie - Earthquake engineering and engineering …, 2022 - Springer
Damage detection is a key procedure in maintenance throughout structures' life cycles and
post-disaster loss assessment. Due to the complex types of structural damages and the low …
post-disaster loss assessment. Due to the complex types of structural damages and the low …
Crack detection using fusion features‐based broad learning system and image processing
Deep learning has been widely applied to vision‐based structural damage detection, but its
computational demand is high. To avoid this computational burden, a novel crack detection …
computational demand is high. To avoid this computational burden, a novel crack detection …
Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm
With the rapid increase of ageing infrastructures worldwide, effective and robust inspection
techniques are highly demanding to evaluate structural conditions and residual lifetime. The …
techniques are highly demanding to evaluate structural conditions and residual lifetime. The …
Image-based reinforced concrete component mechanical damage recognition and structural safety rapid assessment using deep learning with frequency information
Safety assessment of post-event damaged structures is vital and significant because it
directly affects life security, structural repair, and economic loss, especially in earthquakes …
directly affects life security, structural repair, and economic loss, especially in earthquakes …
Deep learning–based autonomous concrete crack evaluation through hybrid image scanning
This article proposes a deep learning–based autonomous concrete crack detection
technique using hybrid images. The hybrid images combining vision and infrared …
technique using hybrid images. The hybrid images combining vision and infrared …