A damage detection model for unbonded post-tensioning tendons based on relative strain variation in multi-strand anchors

2014 ◽  
Author(s):  
A. B. M. Abdullah ◽  
Jennifer A. Rice ◽  
H. R. Hamilton
Keyword(s):  
Damage Detection ◽  
Strain Variation ◽  
Detection Model ◽  
Relative Strain ◽  
Post Tensioning

Wire Breakage Detection Using Relative Strain Variation in Unbonded Posttensioning Anchors

2015 ◽  
Vol 20 (1) ◽  
pp. 04014056 ◽  
Author(s):  
A. B. M. Abdullah ◽  
Jennifer A. Rice ◽  
H. R. Hamilton
Keyword(s):  
Strain Variation ◽  
Wire Breakage ◽  
Relative Strain

scholarly journals Deep Learning-Based Thermal Image Analysis for Pavement Defect Detection and Classification Considering Complex Pavement Conditions

2021 ◽  
Vol 14 (1) ◽  
pp. 106
Author(s):  
Cheng Chen ◽  
Sindhu Chandra ◽  
Yufan Han ◽  
Hyungjoon Seo
Keyword(s):  
Deep Learning ◽  
Damage Detection ◽  
Defect Detection ◽  
Input Data ◽  
Thermal Image ◽  
Detection Accuracy ◽  
Detection Model ◽  
Fused Image ◽  
Data Source ◽  
Pavement Damage

Automatic damage detection using deep learning warrants an extensive data source that captures complex pavement conditions. This paper proposes a thermal-RGB fusion image-based pavement damage detection model, wherein the fused RGB-thermal image is formed through multi-source sensor information to achieve fast and accurate defect detection including complex pavement conditions. The proposed method uses pre-trained EfficientNet B4 as the backbone architecture and generates an argument dataset (containing non-uniform illumination, camera noise, and scales of thermal images too) to achieve high pavement damage detection accuracy. This paper tests separately the performance of different input data (RGB, thermal, MSX, and fused image) to test the influence of input data and network on the detection results. The results proved that the fused image’s damage detection accuracy can be as high as 98.34% and by using the dataset after augmentation, the detection model deems to be more stable to achieve 98.35% precision, 98.34% recall, and 98.34% F1-score.

scholarly journals Crack Detection in Folded Plates With Back-Propagated Artificial Neural Network

2021 ◽  
Author(s):  
Can Gonenli ◽  
Oguzhan Das ◽  
Duygu Bagci Das
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Detection ◽  
Crack Detection ◽  
Structural Integrity ◽  
Engineering Structures ◽  
Detection Techniques ◽  
Detection Model ◽  
Artificial Neural ◽  
Destructive Methods

Abstract Engineering structures may face various damages such as crack, delamination, and fatigue in several circumstances. Localizing such damages becomes essential to understand the health of the structures since they may not be able to operate anymore. Among the damage detection techniques, non-destructive methods are considerably more preferred than destructive methods since damage can be located without affecting the structural integrity. However, these methods have several drawbacks in terms of detecting abilities, time consumption, cost, and hardware or software requirements. Employing artificial intelligence techniques could overcome such issues and could provide a powerful damage detection model if the technique is utilized correctly. In this study, the crack localization in flat and folded plate structures has been conducted by employing a Back-propagated Artificial Neural Network (BPANN). For this purpose, cracks with 18 different dimensions have been modeled in flat and four different folded structures by utilizing the Finite Element Method. The dataset required to perform the crack localization procedure includes the first ten natural frequencies of all structures as input variables. As output variables, the dataset contains a total of 500 crack locations for five structures. It is concluded that the BPANN can localize all cracks with an average accuracy of 95.12%.

scholarly journals A cost-effective neural network–based damage detection procedure for cylindrical equipment

2019 ◽  
Vol 11 (7) ◽  
pp. 168781401986694 ◽  
Author(s):  
R Karami-Mohammadi ◽  
M Mirtaheri ◽  
M Salkhordeh ◽  
MA Hariri-Ardebili
Keyword(s):  
Neural Networks ◽  
Damage Detection ◽  
Damage Index ◽  
Principal Component ◽  
Numerical Data ◽  
Cost Effective ◽  
Voltage Transformer ◽  
Detection Model ◽  
The Neural Networks ◽  
Parametric Analyses

This article presents a vibration-based technique for damage detection in the cylindrical equipment. First, a damage index based on the residual frequency responses is defined. This technique uses the principal component analysis for data reduction by eliminating the components that have the minimum contribution to the damage index. Then, the principal components are fed into neural networks to identify the changes in the damage pattern. Furthermore, the efficiency of this technique in the field condition is investigated by adding different noise levels to the output data. This study aims at proposing a cost-effective damage detection model using only one sensor. Therefore, the optimal location of the sensor is also discussed. A case study of capacitive voltage transformer is used for validation of finite element models. The neural networks are trained using numerical data and tested with experimental one. Several parametric analyses are performed to investigate the sensitivity of the model.

Field experiment on a PSC-I bridge for convolutional autoencoder-based damage detection

2020 ◽  
pp. 147592172092626
Author(s):  
Kanghyeok Lee ◽  
Seunghoo Jeong ◽  
Sung-Han Sim ◽  
Do Hyoung Shin
Keyword(s):  
Field Experiment ◽  
Damage Detection ◽  
Detection Method ◽  
Detection Model ◽  
Detection Approach ◽  
Strain Data ◽  
Convolutional Autoencoder ◽  
Single Vehicle

In this study, a field experiment was performed for damage detection on a PSC-I bridge based on a convolutional autoencoder using the damage detection approach proposed in a previous study by the authors. The field experiment measured the acceleration and strain data of the PSC-I bridge while a single vehicle passed the bridge; subsequently, these data were used to train and test the convolutional autoencoder–based damage detection model. The results of the test showed that the convolutional autoencoder–based model could perform accurate and robust damage detection. Furthermore, these findings indicate that the convolutional autoencoder–based damage detection could also perform satisfactorily in practice. The results of this study can form the basis to facilitate the adoption of the convolutional autoencoder–based damage detection method to monitor bridges in practice.

Intelligent-based Structural Damage Detection Model

2010 ◽  
Author(s):  
Eric Wai Ming Lee ◽  
Kin Fung Yu ◽  
Jane W. Z. Lu ◽  
Andrew Y. T. Leung ◽  
Vai Pan Iu ◽  
...  
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Structural Damage Detection ◽  
Detection Model

scholarly journals Study on Improvement of Lightning Damage Detection Model for Wind Turbine Blade

Machines ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 9
Author(s):  
Takuto Matsui ◽  
Kazuo Yamamoto ◽  
Jun Ogata
Keyword(s):  
Damage Detection ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Detection System ◽  
Turbine Blades ◽  
Lightning Strike ◽  
Wind Turbine Blades ◽  
Lightning Strikes ◽  
Detection Model ◽  
Secondary Damage

There have been many reports of damage to wind turbine blades caused by lightning strikes in Japan. In some of these cases, the blades struck by lightning continue to rotate, causing more serious secondary damage. To prevent such accidents, it is a requirement that a lightning detection system is installed on the wind turbine in areas where winter lightning occurs in Japan. This immediately stops the wind turbine if the system detects a lightning strike. Normally, these wind turbines are restarted after confirming soundness of the blade through visual inspection. However, it is often difficult to confirm the soundness of the blade visually for reasons such as bad weather. This process prolongs the time taken to restart, and it is one of the causes that reduces the availability of the wind turbines. In this research, we constructed a damage detection model for wind turbine blades using machine learning based on SCADA system data and, thereby, considered whether the technology automatically confirms the soundness of wind turbine blades.

scholarly journals Effective Crack Damage Detection Using Multilayer Sparse Feature Representation and Incremental Extreme Learning Machine

2019 ◽  
Vol 9 (3) ◽  
pp. 614 ◽  
Author(s):  
Baoxian Wang ◽  
Yiqiang Li ◽  
Weigang Zhao ◽  
Zhaoxi Zhang ◽  
Yufeng Zhang ◽  
...  
Keyword(s):  
Damage Detection ◽  
Extreme Learning Machine ◽  
Crack Detection ◽  
Feature Learning ◽  
Feature Representation ◽  
Fine Tuning ◽  
Detection Model ◽  
Concrete Crack ◽  
Learning Machine ◽  
Sparse Feature Representation

Detecting cracks within reinforced concrete is still a challenging problem, owing to the complex disturbances from the background noise. In this work, we advocate a new concrete crack damage detection model, based upon multilayer sparse feature representation and an incremental extreme learning machine (ELM), which has both favorable feature learning and classification capabilities. Specifically, by cropping and using a sliding window operation and image rotation, a large number of crack and non-crack patches are obtained from the collected concrete images. With the existing image patches, the defect region features can be quickly calculated by the multilayer sparse ELM autoencoder networks. Then, the online incremental ELM classified network is used to recognize the crack defect features. Unlike the commonly-used deep learning-based methods, the presented ELM-based crack detection model can be trained efficiently without tediously fine-tuning the entire-network parameters. Moreover, according to the ELM theory, the proposed crack detector works universally for defect feature extraction and detection. In the experiments, when compared with other recently developed crack detectors, the proposed concrete crack detection model can offer outstanding training efficiency and favorable crack detecting accuracy.

Development of the Deep Learning Based Damage Detection Model for Buildings Utilizing Aerial Photographs of Plural Earthquakes

2020 ◽  
Vol 20 (7) ◽  
pp. 7_177-7_216
Author(s):  
Shohei NAITO ◽  
Hiromitsu TOMOZAWA ◽  
Yuji MORI ◽  
Naokazu MONMA ◽  
Hiromitsu NAKAMURA ◽  
...  
Keyword(s):  
Deep Learning ◽  
Damage Detection ◽  
Aerial Photographs ◽  
Detection Model
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