Deep Transfer Learning for Image‐Based Structural Damage Recognition

By Yuqing Gao, Khalid M. Mosalam. Comput. Aided Civ. Infrastructure Eng., 2018

699 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/f3b6935b279bc81ef4005a37fad1608228b30617

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

Citations in the corpus

An Information-Theoretic Approach to Transferability in Task Transfer Learning (2019, methodology): “For example, deep convolutional neural networks trained for the ImageNet classiﬁcation task have been used as the source network in transfer learning for target tasks with fewer labeled data [2], such as medical image analysis [3] and structural damage recognition in buildings [4].”
A Review of Deep Transfer Learning and Recent Advancements (2022, background): “[40] 2018 Deep Transfer Learning for ImageBased Structural Damage Recognition Image no Freezing CNN layers yes VGG-Net Civil engineering”
Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring (2019, methodology): “11 [161] for structural component recognition results).”
Image‐based post‐disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization (2018, background): “…Gabbouj, & Inman, 2017), pavement crack detection (Vetrivel, Gerke, Kerle, Nex, & Vossel-man, 2018; Zhang et al., 2017), structural damage recognition through transfer learning (Gao & Mosalam, 2018), and structural damage detection with feature extracted from deep learning (Lin, Nie, & Ma, 2017).”
Transfer learning: a friendly introduction (2022, background): “Though recommendation application did a lot in e-commerce, it has been facing many data sparsity problems, leading to poor recommendations [60].”
Machine learning paradigm for structural health monitoring (2020, methodology): “For example, Gao and Mosalam 54 proposed a transfer learning method using a pretrained model for the structural damage classification task.”
The promise of implementing machine learning in earthquake engineering: A state-of-the-art review (2020, background): “Gao and Mosalam (2018) have also constructed an image data-base called ‘‘Structural ImageNet,’’ from which two DL technologies such as transfer learning (TL) and visual geometry group (VGGNet) were applied to recognize structural damage caused by earthquakes and other natural hazards.”
Automatic pixel‐level multiple damage detection of concrete structure using fully convolutional network (2019, methodology): “When training the FCN, a strategy of model-based transfer learning (Gao & Mosalam, 2018; Pan & Yang, 2010), rather than training the model from scratch, is adopted to accelerate and optimize the learning eﬃciency of the model.”
Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning (2021, methodology): “Transfer learning was implemented in image-based structural recognition to perform component type identification, spalling condition check, damage level evaluation, and damage type determination [42].”