Image‐based post‐disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization

By Xiao Liang. Comput. Aided Civ. Infrastructure Eng., 2018

379 citations recorded by the source corpus.

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 service life. Therefore, structural health inspections of these bridges are becoming critically important, especially after extreme events. To enhance the efficiency of such an inspection, in recent years, autonomous damage detection based on computer vision has become a research hotspot. This article proposes a three‐level image‐based approach for post‐disaster inspection of the reinforced concrete bridge using deep learning with novel training strategies. The convolutional neural network for image classification, object detection, and semantic segmentation are, respectively, proposed to conduct system‐level failure classification, component‐level bridge column detection, and local damage‐level damage localization. To enable efficient training and prediction using a small data set, the model robustness is a crucial aspect to be taken into account, generally through its hyperparameters’ selection. This article, based on Bayesian optimization, proposes a principled manner of such selection, with which very promising results (well over 90% accuracies) and robustness are observed on all three‐level deep learning models.

Subfields: Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/eab7943ae386f3ef99e06660af68cc98da68fe17

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In Civil and Structural Engineering

Citations in the corpus

Efficient training of physics‐informed neural networks via importance sampling (2021, background): “Examples include, but are not limited to, structural damage detection and image recognition (e.g., Azimi & Pekcan, 2019; Cha et al., 2017; Koziarski & Cyganek, 2017; Liang, 2019; Lin et al., 2017; Rafiei & Adeli, 2018, 2017; Zeinali & Story, 2017; X. Zhang et al., 2019), estimation of concrete compr”
Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring (2019, methodology): “Liang [162] applied the Faster R-CNN algorithm to detect and localize bridge components by drawing bounding boxes around them automatically.”
Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review (2020, background): “Similarly, a study conducted by [76] proposed a threelevel image-based approach for post-disaster monitoring of reinforced concrete bridges using image classification, object detection and semantic segmentation, respectively to assess failure of the overall system, detect the structural element (Dec”
A systematic review of convolutional neural network-based structural condition assessment techniques (2021, methodology): “765 Liang (2018) Global and component- level damage assessment VGG16, R-CNN, and fully deep CNN through semantic segmentation with Bayesian optimization Cracked and un-cracked images of reinforced concrete bridges Kim et al. (2018) Component-level damage assessment R-CNN-based TL (ImageNet…”