Structural Damage Detection Using Convolutional Neural Networks

Gulgec, Nur Sila; Takáč, Martin; Pakzad, Shamim N.

doi:10.1007/978-3-319-54858-6_33

Cited by 48 publications

(31 citation statements)

References 19 publications

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“…It is reported that they achieved success in finding concrete cracks in realistic situations. In a similar study by Gulgec et al [69] CNNs are used per a Python library Theano with the graphics processing unit (GPU) to classify damaged and undamaged Person-X samples modeled with Finite Element (FE) simulations only. It is reported that high classification accuracy is achieved for the FE data.…”

Section: Vibration-based Structural Damage Detection In Civil Infrastmentioning

confidence: 99%

1-D Convolutional Neural Networks for Signal Processing Applications

Kıranyaz

İnce

Abdeljaber

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

274

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During the last decade, Convolutional Neural Networks (CNNs) have become the de facto standard for various Computer Vision and Machine Learning operations. CNNs are feed-forward Artificial Neural Networks (ANNs) with alternating convolutional and subsampling layers. Deep 2D CNNs with many hidden layers and millions of parameters have the ability to learn complex objects and patterns providing that they can be trained on a massive size visual database with ground-truth labels. With a proper training, this unique ability makes them the primary tool for various engineering applications for 2D signals such as images and video frames. Yet, this may not be a viable option in numerous applications over 1D signals especially when the training data is scarce or application-specific. To address this issue, 1D CNNs have recently been proposed and immediately achieved the state-of-the-art performance levels in several applications such as personalized biomedical data classification and early diagnosis, structural health monitoring, anomaly detection and identification in power electronics and motor-fault detection. Another major advantage is that a real-time and low-cost hardware implementation is feasible due to the simple and compact configuration of 1D CNNs that perform only 1D convolutions (scalar multiplications and additions). This paper presents a comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field. Their state-of-the-art performance is highlighted concluding with their unique properties. The benchmark datasets and the principal 1D CNN software used in those applications are also publically shared in a dedicated website.

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Section: Vibration-based Structural Damage Detection In Civil Infrastmentioning

confidence: 99%

1-D Convolutional Neural Networks for Signal Processing Applications

Kıranyaz

İnce

Abdeljaber

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

274

View full text Add to dashboard Cite

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“…These advantages make deep learning an area of interest in the SHM research community. Several studies adopt DNNs to detect deficiencies (Gulgec, Takáč, & Pakzad, 2017, 2019a; Liang, 2019; Maeda, Sekimoto, Seto, Kashiyama, & Omata, 2018; Rafiei & Adeli, 2018), predict material properties (Nguyen, Kashani, Ngo, & Bordas, 2019; Rafiei, Khushefati, Demirboga, & Adeli, 2017), and perform seismic reliability analysis of transportation networks (Nabian & Meidani, 2018). As a nature of the sensor data, temporal information and long‐term dependencies of the time series are also vital in SHM.…”

Section: Introductionmentioning

confidence: 99%

Structural sensing with deep learning: Strain estimation from acceleration data for fatigue assessment

Gulgec

Takáč

Pakzad

2020

Computer aided Civil Eng

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Many of the civil structures experience significant vibrations and repeated stress cycles during their life span. These conditions are the bases for fatigue analysis to accurately establish the remaining fatigue life of the structures that ideally requires a full-field strain assessment of the structures over years of data collection. Traditional inspection methods collect strain measurements by using strain gauges for a short time span and extrapolate the measurements in time; nevertheless, large-scale deployment of strain gauges is expensive and laborious as more spatial information is desired. This paper introduces a deep learning-based approach to replace this high cost by employing inexpensive data coming from acceleration sensors. The proposed approach utilizes collected acceleration responses as inputs to a multistage deep neural network based on long short-term memory and fully connected layers to estimate the strain responses. The memory requirement of training long acceleration sequences is reduced by proposing a novel training strategy. In the evaluation of the method, a laboratory-scale horizontally curved girder subjected to various loading scenarios is tested.

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“…Gopalakrishnan et al [5] introduced deep migration learning to detect surface cracks of roads made of hot mix asphalt (HMA) and Portland cement concrete (PCC), and observed the best detection effect on the single-layer neural network (NN) after feature training. Based on a convolutional neural network (CNN), Gulgec et al [6] conducted finite-element simulation of cracked gusset plate connections in steel bridges, and differentiated between defected and healthy bridge samples. Wang et al [7] proposed a CNN method based on the sliding window, which uses deep learning frameworks from AlexNet and GoogLeNet to classify bridge damages.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Classification Model for Surface Defects on Cement Concrete Bridges

Zhu

Song

2020

Applied Sciences

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This paper mainly improves the visual geometry group network-16 (VGG-16), which is a classic convolutional neural network (CNN), to classify the surface defects on cement concrete bridges in an accurate manner. Specifically, the number of fully connected layers was reduced by one, and the Softmax classifier was replaced with a Softmax classification layer with seven defect tags. The weight parameters of convolutional and pooling layers were shared in the pre-trained model, and the rectified linear unit (ReLU) function was taken as the activation function. The original images were collected by a road inspection vehicle driving across bridges on national and provincial highways in Jiangxi Province, China. The images on surface defects of cement concrete bridges were selected, and divided into a training set and a test set, and preprocessed through morphology-based weight adaptive denoising. To verify its performance, the improved VGG-16 was compared with traditional shallow neural networks (NNs) like the backpropagation neural network (BPNN), support vector machine (SVM), and deep CNNs like AlexNet, GoogLeNet, and ResNet on the same sample dataset of surface defects on cement concrete bridges. Judging by mean detection accuracy and top-5 accuracy, our model outperformed all the contrastive methods, and accurately differentiated between images with seven classes of defects such as normal, cracks, fracturing, plate fracturing, corner rupturing, edge/corner exfoliation, skeleton exposure, and repairs. The results indicate that our model can effectively extract the multi-layer features from surface defect images, which highlights the edges and textures. The research findings shed important new light on the detection of surface defects and classification of defect images.

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Structural Damage Detection Using Convolutional Neural Networks

Cited by 48 publications

References 19 publications

1-D Convolutional Neural Networks for Signal Processing Applications

1-D Convolutional Neural Networks for Signal Processing Applications

Structural sensing with deep learning: Strain estimation from acceleration data for fatigue assessment

An Intelligent Classification Model for Surface Defects on Cement Concrete Bridges

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