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Damage Monitoring and Defect Identification Based on Deep/Machine Learning
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Damage Monitoring and Defect Identification Based on Deep/Machine Learning

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (20 May 2024) | Viewed by 34250

Special Issue Editor

Prof. Dr. Zengshun Chen

E-Mail Website
Guest Editor
School of Civil Engineering, Chongqing University, Chongqing 400044, China
Interests: high-performance concrete; structural analysis; intelligent detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the final barrier for humankind, civil structures constantly confront hazards, such as winds, earthquakes, floods, and even manmade machinery or vehicles. Excessive loading, fatigue, undesired vibrations, deformations, collapses, and previous losses also remind us that structural safety is never a one-size-fits-all task. For these reasons, structural health monitoring, damage detection, risk forecast, and reliability assessment bear paramount socioeconomic importance.

On the cusp of the digital era, several AI techniques, particlualry data-driven optimization, deep/machine learning, and reduced-order modeling, have made breakthroughs in many applications. The interdisciplinary integration of civil engineering and data science has already shown great potential. Applying AI to structural safety and damage monitoring is also one of the hottest topics in civil/wind/earthquake/environmental/structural engineering.

This Special Issue is dedicated to highlighting the state-of-the-art advances and latest applications of data-driven/AI techniques in structural safety and relevant fields. We welcome high-quality and original work addressing, but not limited to, the following topics:

  • Advances in data-driven theories and algorithms that show potential in civil applications;
  • Applications of data-driven theories and algorithms in civil problems concerning structural safety and health monitoring;
  • Methods, regardless of whether they are numerical, experimental, field, or analytical in nature, for structure safety and health monitoring;
  • Case studies of damage detection, risky forecast, design optimization, and reliability assessment using computer-aided techniques;
  • All other interdisciplinary efforts solving civil engineering problems with data/computer methods.

Dr. Zengshun Chen
Guest Editor

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Keywords

  • structure safety
  • damage detection
  • artificial intelligence
  • health monitoring
  • machine learning

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Related Special Issue

Published Papers (17 papers)

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Research

17 pages, 25328 KiB  
Article
Comparison of Residual Network and Other Classical Models for Classification of Interlayer Distresses in Pavement
by Wenlong Cai, Mingjie Li, Guanglai Jin, Qilin Liu and Congde Lu
Appl. Sci. 2024, 14(15), 6568; https://doi.org/10.3390/app14156568 - 27 Jul 2024
Viewed by 732
Abstract
Many automatic classification methods published can identify the main hidden distress types of highways, but they cannot meet the precise needs of operation and maintenance. The classification of interlayer distresses based on ground penetrating radar (GPR) images is very important to improve maintenance [...] Read more.
Many automatic classification methods published can identify the main hidden distress types of highways, but they cannot meet the precise needs of operation and maintenance. The classification of interlayer distresses based on ground penetrating radar (GPR) images is very important to improve maintenance efficiency and reduce cost. However, among models of different complexities, which models are suitable for the interlayer distress data needs further verification. Firstly, to cover enough of the variable range of distress samples, the interlayer distress dataset collected containing 32,038 samples was subcategorized into three types: interlayer debonding, interlayer water seepage, and interlayer loosening. Secondly, residual networks (ResNets) that render easier to build shallower or deeper networks (ResNet-4, ResNet-6, ResNet-8, ResNet-10, ResNet-14, ResNet-18, ResNet-34, and ResNet-50) and five classical network models (DenseNet-121, EfficientNet B0, SqueezeNet1_0, MobileNet V2, and VGG-19) were evaluated by training and validation loss, test accuracy, and model complexity. The experimental results show that all models have high test accuracy with little difference, but ResNet-4, ResNet-6, SqueezeNet1_0, and ResNet-8 exhibit no overfitting which means they have good generalization performance. Full article
(This article belongs to the Special Issue Damage Monitoring and Defect Identification Based on Deep/Machine Learning)
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27 pages, 24713 KiB  
Article
Performance Evaluation of Convolutional Neural Network Models for Classification of Highway Hidden Distresses with GPR B-Scan Images
by Guanglai Jin, Qilin Liu, Wenlong Cai, Mingjie Li and Congde Lu
Appl. Sci. 2024, 14(10), 4226; https://doi.org/10.3390/app14104226 - 16 May 2024
Cited by 1 | Viewed by 856
Abstract
Despite the considerable advancements in automated identification methods of highway hidden distress with ground-penetrating radar (GPR) images, there still exist challenges in realizing automated identification of highway hidden distress owing to the quantity, variability, and reliability of the distress samples and diversity of [...] Read more.
Despite the considerable advancements in automated identification methods of highway hidden distress with ground-penetrating radar (GPR) images, there still exist challenges in realizing automated identification of highway hidden distress owing to the quantity, variability, and reliability of the distress samples and diversity of classification models. Firstly, the dataset collected contains 31,640 samples categorized into four categories: interlayer debonding, interlayer loosening, interlayer water seepage, and structural loosening from 1500 km highway, for obtaining larger enough samples and covering the variable range of distress samples. Secondly, the distresses were labeled by experienced experts, and the labels were verified with drilled cores to ensure their reliability. Lastly, 18 exemplary convolutional neural network (CNN) models from 8 different architectures were evaluated using evaluation metrics such as precision, recall, and f1-score. Further, confusion matrix and Grad-CAM techniques were utilized to analyze these models. The experimental results show that VGG13 performed most prominently and stably, while the lightweight network SqueezeNet1_1 performed particularly well with a batch size of 64. Furthermore, this study indicates that models with fewer layers can achieve comparable or better performance than deeper models. Full article
(This article belongs to the Special Issue Damage Monitoring and Defect Identification Based on Deep/Machine Learning)
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13 pages, 2031 KiB  
Article
Research on Defect Detection Method for Composite Materials Based on Deep Learning Networks
by Jing Cheng, Wen Tan, Yuhao Yuan, Zirui Zhao and Yuxiang Cheng
Appl. Sci. 2024, 14(10), 4161; https://doi.org/10.3390/app14104161 - 14 May 2024
Viewed by 1338
Abstract
Compared to traditional industrial materials, composites have higher durability and compressive strength. However, some components may have flaws due to the manufacturing process. Traditional defect detection methods have low accuracy and cannot adapt to complex shooting environments. Aiming to address the issues of [...] Read more.
Compared to traditional industrial materials, composites have higher durability and compressive strength. However, some components may have flaws due to the manufacturing process. Traditional defect detection methods have low accuracy and cannot adapt to complex shooting environments. Aiming to address the issues of high computational requirements in traditional detection models and the lack of lightweight detection capabilities, the Ghost module is used instead of convolutional arithmetic to construct a lightweight model. To reduce the computational complexity of the feature extraction module, we have incorporated an improved Efficient Channel Attention mechanism to improve the model’s feature extraction capabilities. A rapid defect classification method is implemented to determine whether there are defects in the image or not by comparing the performance and running speed of models such as AlexNet, VGGNet, and ResNet. And ablation experiments are conducted for each model. The results show that the Ghost module model, which incorporates the improved Efficient Channel Attention mechanism, has a significant optimization effect on the convolutional neural network model. It can achieve a high accuracy rate when constructing lightweight models. It improves the running speed of the model, making it more efficient to use and deploy. Full article
(This article belongs to the Special Issue Damage Monitoring and Defect Identification Based on Deep/Machine Learning)
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17 pages, 16540 KiB  
Article
Accurate Classification of Tunnel Lining Cracks Using Lightweight ShuffleNetV2-1.0-SE Model with DCGAN-Based Data Augmentation and Transfer Learning
by Ningyu Zhao, Yi Song, Ailin Yang, Kangping Lv, Haifei Jiang and Chao Dong
Appl. Sci. 2024, 14(10), 4142; https://doi.org/10.3390/app14104142 - 13 May 2024
Viewed by 1110
Abstract
Cracks in tunnel lining surfaces directly threaten structural integrity; therefore, regular inspection of cracks is essential. Lightweight convolutional neural networks (LCNNs) have recently offered a promising alternative to conventional manual inspection. However, the effectiveness of LCNNs is still adversely affected by the lack [...] Read more.
Cracks in tunnel lining surfaces directly threaten structural integrity; therefore, regular inspection of cracks is essential. Lightweight convolutional neural networks (LCNNs) have recently offered a promising alternative to conventional manual inspection. However, the effectiveness of LCNNs is still adversely affected by the lack of sufficient crack images, which limits the potential detection performance. In this paper, transfer learning was used to optimize deep convolutional generative adversarial networks (DCGANs) for crack image synthesis to significantly improve the accuracy of LCNNs. In addition, an improved LCNN model named ShuffleNetV2-1.0-SE was proposed, incorporating the squeeze–excitation (SE) attention mechanism into ShuffleNetV2-1.0 and realizing highly accurate classification results while maintaining lightness. The results show that the DCGAN-based data enhancement method can significantly improve the classification accuracy of ShuffleNetV2-1.0-SE for tunnel lining cracks. ShuffleNetV2-1.0-SE achieves an accuracy of 98.14% on the enhanced dataset, which is superior to multiple advanced LCNN models. Full article
(This article belongs to the Special Issue Damage Monitoring and Defect Identification Based on Deep/Machine Learning)
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16 pages, 14838 KiB  
Article
A Lightweight Method for Detecting Sewer Defects Based on Improved YOLOv5
by Xing Zhang, Jiawei Zhang, Lei Tian, Xiang Liu and Shuohong Wang
Appl. Sci. 2023, 13(15), 8986; https://doi.org/10.3390/app13158986 - 5 Aug 2023
Cited by 2 | Viewed by 1324
Abstract
In response to the issues of the existing sewer defect detection models, which are not applicable to small computing platforms due to their complex structure and large computational volume, as well as the low detection accuracy, a lightweight detection model based on YOLOv5, [...] Read more.
In response to the issues of the existing sewer defect detection models, which are not applicable to small computing platforms due to their complex structure and large computational volume, as well as the low detection accuracy, a lightweight detection model based on YOLOv5, named YOLOv5-GBC, is proposed. Firstly, to address the computational redundancy problem of the traditional convolutional approach, GhostNet, which is composed of Ghost modules, is used to replace the original backbone network. Secondly, aiming at the problem of low detection accuracy of small defects, more detailed spatial information is introduced by fusing shallow features in the neck network, and weighted feature fusion is used to improve the feature fusion efficiency. Finally, to improve the sensitivity of the model to key feature information, the coordinate attention mechanism is introduced into the Ghost module and replaced the traditional convolution approach in the neck network. Experimental results show that compared with the YOLOv5 model, the model size and floating point of operations (FLOPs) of YOLOv5-GBC are reduced by 74.01% and 74.78%, respectively; the mean average precision (MAP) and recall are improved by 0.88% and 1.51%, respectively; the detection speed is increased by 63.64%; and the model size and computational volume are significantly reduced under the premise of ensuring the detection accuracy, which can effectively meet the needs of sewer defect detection on small computing platforms. Full article
(This article belongs to the Special Issue Damage Monitoring and Defect Identification Based on Deep/Machine Learning)
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12 pages, 2196 KiB  
Article
Non-Destructive Damage Evaluation Based on Static Response for Beam-like Structures Considering Shear Deformation
by Xiangwei Meng, Feng Xiao, Yu Yan, Gang S. Chen and Yanlong Ma
Appl. Sci. 2023, 13(14), 8219; https://doi.org/10.3390/app13148219 - 15 Jul 2023
Cited by 4 | Viewed by 1331
Abstract
Shear deformation plays an important role in certain structures, and neglecting shear deformation can affect the accuracy of structural response. This paper proposes a non-destructive damage evaluation method that considers shear deformation, based on static response, for identifying corrosion in beam-like structures. The [...] Read more.
Shear deformation plays an important role in certain structures, and neglecting shear deformation can affect the accuracy of structural response. This paper proposes a non-destructive damage evaluation method that considers shear deformation, based on static response, for identifying corrosion in beam-like structures. The influence of shear deformation on nodal displacement for simply supported beams with different cross-sections was analyzed. The results indicate that even small errors yield inaccurate identification results when neglecting shear deformation. To solve this problem, analytical displacements of the structure were determined based on the Timoshenko beam theory, and the objective function was established. Additionally, the damage identification results were obtained by minimizing the objective function using the interior point method. Several progressively complex examples were used to demonstrate the effectiveness of the proposed method in identifying damage in beam-like structures. Full article
(This article belongs to the Special Issue Damage Monitoring and Defect Identification Based on Deep/Machine Learning)
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19 pages, 3623 KiB  
Article
Classification of Unbalanced and Bowed Rotors under Uncertainty Using Wavelet Time Scattering, LSTM, and SVM
by Nima Rezazadeh, Mario de Oliveira, Donato Perfetto, Alessandro De Luca and Francesco Caputo
Appl. Sci. 2023, 13(12), 6861; https://doi.org/10.3390/app13126861 - 6 Jun 2023
Cited by 10 | Viewed by 1960
Abstract
A growing interest in intelligent fault detection may sometimes lead to practical issues when existing malfunctions reveal analogous indications and the number of observations is limited. This article addresses the classification problem of two identical malfunctions, i.e., unbalancing and shaft bow in rotary [...] Read more.
A growing interest in intelligent fault detection may sometimes lead to practical issues when existing malfunctions reveal analogous indications and the number of observations is limited. This article addresses the classification problem of two identical malfunctions, i.e., unbalancing and shaft bow in rotary machines, where only 56 observations were utilized for the training. The faulty systems are modeled in ABAQUS/CAE; a data set for each fault is created by simulation under various physical and operational conditions employing the uncertainty concept. The wavelet time scattering (WTS) technique extracts low-variance presentations from signals. With respect to the classification procedure of the faulted rotor systems, two models are examined with the extracted features from WTS as the input. Initially, a long short-term memory (LSTM) network is trained and tested, and then, the capability of a support vector machine (SVM) model is inquired. Ultimately, the classification models are trained and tested using the raw time series data and the extracted features to compare the effectiveness of the suggested methods, i.e., WTS. The employed approach for feature extraction demonstrated remarkable effectiveness in addressing a potential hurdle in identifying faults in rotating systems: the ability to differentiate between unbalanced and bowed rotors, irrespective of the classification model utilized. Full article
(This article belongs to the Special Issue Damage Monitoring and Defect Identification Based on Deep/Machine Learning)
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20 pages, 5151 KiB  
Article
Framework for Identification and Prediction of Corrosion Degradation in a Steel Column through Machine Learning and Bayesian Updating
by Simone Castelli and Andrea Belleri
Appl. Sci. 2023, 13(7), 4646; https://doi.org/10.3390/app13074646 - 6 Apr 2023
Cited by 1 | Viewed by 1510
Abstract
In recent years, structural health monitoring, starting from accelerometric data, is a method which has become widely adopted. Among the available techniques, machine learning is one of the most innovative and promising, supported by the continuously increasing computational capacity of current computers. The [...] Read more.
In recent years, structural health monitoring, starting from accelerometric data, is a method which has become widely adopted. Among the available techniques, machine learning is one of the most innovative and promising, supported by the continuously increasing computational capacity of current computers. The present work investigates the potential benefits of a framework based on supervised learning suitable for quantifying the corroded thickness of a structural system, herein uniformly applied to a reference steel column. The envisaged framework follows a hybrid approach where the training data are generated from a parametric and stochastic finite element model. The learning activity is performed by a support vector machine with Bayesian optimization of the hyperparameters, in which a penalty matrix is introduced to minimize the probability of missed alarms. Then, the estimated structural health conditions are used to update an exponential degradation model with random coefficients suitable for providing a prediction of the remaining useful life of the simulated corroded column. The results obtained show the potentiality of the proposed framework and its possible future extension for different types of damage and structural types. Full article
(This article belongs to the Special Issue Damage Monitoring and Defect Identification Based on Deep/Machine Learning)
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19 pages, 5464 KiB  
Article
Automatic Detection Method of Sewer Pipe Defects Using Deep Learning Techniques
by Jiawei Zhang, Xiang Liu, Xing Zhang, Zhenghao Xi and Shuohong Wang
Appl. Sci. 2023, 13(7), 4589; https://doi.org/10.3390/app13074589 - 4 Apr 2023
Cited by 8 | Viewed by 4232
Abstract
Regular inspection of sewer pipes can detect serious defects in time, which is significant to ensure the healthy operation of sewer systems and urban safety. Currently, the widely used closed-circuit television (CCTV) inspection system relies mainly on manual assessment, which is labor intensive [...] Read more.
Regular inspection of sewer pipes can detect serious defects in time, which is significant to ensure the healthy operation of sewer systems and urban safety. Currently, the widely used closed-circuit television (CCTV) inspection system relies mainly on manual assessment, which is labor intensive and inefficient. Therefore, it is urgent to develop an efficient and accurate automatic defect detection method. In this paper, an improved method based on YOLOv4 is proposed for the detection of sewer defects. A significant improvement of this method is using the spatial pyramid pooling (SPP) module to expand the receptive field and improve the ability of the model to fuse context features in different receptive fields. Meanwhile, the influence of three bounding box loss functions on model performance are compared based on their processing speed and detection accuracy, and the effectiveness of the combination of DIoU loss function and SPP module is verified. In addition, to address the lack of datasets for sewer defect detection, a dataset that contains 2700 images and 4 types of defects was created, which provides useful help for the application of computer vision techniques in this field. Experimental results show that, compared with the YOLOv4 model, the mean average precision (mAP) of the improved model for sewer defect detection are improved by 4.6%, the mAP can reach 92.3% and the recall can reach 89.0%. The improved model can effectively improve the detection and classification accuracy of sewer defects, and has significant advantages compared with other methods. Full article
(This article belongs to the Special Issue Damage Monitoring and Defect Identification Based on Deep/Machine Learning)
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17 pages, 4070 KiB  
Article
STrans-YOLOX: Fusing Swin Transformer and YOLOX for Automatic Pavement Crack Detection
by Hui Luo, Jiamin Li, Lianming Cai and Mingquan Wu
Appl. Sci. 2023, 13(3), 1999; https://doi.org/10.3390/app13031999 - 3 Feb 2023
Cited by 12 | Viewed by 2723
Abstract
Automatic pavement crack detection is crucial for reducing road maintenance costs and ensuring transportation safety. Although convolutional neural networks (CNNs) have been widely used in automatic pavement crack detection, they cannot adequately model the long-range dependencies between pixels and easily lose edge detail [...] Read more.
Automatic pavement crack detection is crucial for reducing road maintenance costs and ensuring transportation safety. Although convolutional neural networks (CNNs) have been widely used in automatic pavement crack detection, they cannot adequately model the long-range dependencies between pixels and easily lose edge detail information in complex scenes. Moreover, irregular crack shapes also make the detection task challenging. To address these issues, an automatic pavement crack detection architecture named STrans-YOLOX is proposed. Specifically, the architecture first exploits the CNN backbone to extract feature information, preserving the local modeling ability of the CNN. Then, Swin Transformer is introduced to enhance the long-range dependencies through a self-attention mechanism by supplying each pixel with global features. A new global attention guidance module (GAGM) is used to ensure effective information propagation in the feature pyramid network (FPN) by using high-level semantic information to guide the low-level spatial information, thereby enhancing the multi-class and multi-scale features of cracks. During the post-processing stage, we utilize α-IoU-NMS to achieve the accurate suppression of the detection boxes in the case of occlusion and overlapping objects by introducing an adjustable power parameter. The experiments demonstrate that the proposed STrans-YOLOX achieves 63.37% mAP and surpasses the state-of-the-art models on the challenging pavement crack dataset. Full article
(This article belongs to the Special Issue Damage Monitoring and Defect Identification Based on Deep/Machine Learning)
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24 pages, 8673 KiB  
Article
Experimental Study on the Seismic Performance of Shear Walls with Different Coal Gangue Replacement Rates
by Shixin Wang, Haiqing Liu, Yue Wang, Yizhi Qiao, Liang Wang, Jie Bai, Tim K. T. Tse, Cruz Y. Li and Yunfei Fu
Appl. Sci. 2022, 12(20), 10622; https://doi.org/10.3390/app122010622 - 20 Oct 2022
Cited by 3 | Viewed by 1504
Abstract
To replace conventional concrete with coal gangue concrete in the construction industry, lateral cyclic loading tests were applied to three shear walls with different coal gangue replacement rates in this study, in which the replacement rate of coal gangue was 0%, 50%, and [...] Read more.
To replace conventional concrete with coal gangue concrete in the construction industry, lateral cyclic loading tests were applied to three shear walls with different coal gangue replacement rates in this study, in which the replacement rate of coal gangue was 0%, 50%, and 100%. The load-displacement hysteretic curves and backbone curves of the shear walls obtained from tests were analyzed to compare the failure process and seismic performance of each shear wall. The results indicate that the stress performance and failure morphology of coal gangue concrete shear walls and conventional concrete shear walls are extremely similar, and the characteristics of the hysteretic and backbone curves are approximately the same. With the increase in the coal gangue replacement rate, the bearing capacity and ductility of the three shear walls gradually decrease, the strength degradation gradually becomes significant, and the energy dissipation capacity becomes worse, but the difference is not obvious, and all of them can meet the requirements of seismic performance. In addition, with the increase in the coal gangue replacement rate, the stiffness degradation gradually slows, so it is feasible to construct a shear wall using coal gangue concrete instead of conventional concrete. Full article
(This article belongs to the Special Issue Damage Monitoring and Defect Identification Based on Deep/Machine Learning)
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26 pages, 8660 KiB  
Article
Numerical and Experimental Study on the Member Performance and Stability Bearing Capacity of Wheel Coupler Formwork Supports
by Qi Chu, Haiqing Liu, Shengyong Xia, Jinfeng Dong, Ming Lei, Tim K. T. Tse, Lingxiao Teng, Cruz Y. Li and Yunfei Fu
Appl. Sci. 2022, 12(20), 10452; https://doi.org/10.3390/app122010452 - 17 Oct 2022
Cited by 3 | Viewed by 1550
Abstract
To evaluate the performance of wheel coupler formwork support components, the bearing capacity of the horizontal bar, the shear capacity of the wheel, the shear capacity of the sleeve, and the stability bearing capacity of the single- and double-layer vertical poles were investigated [...] Read more.
To evaluate the performance of wheel coupler formwork support components, the bearing capacity of the horizontal bar, the shear capacity of the wheel, the shear capacity of the sleeve, and the stability bearing capacity of the single- and double-layer vertical poles were investigated through systematic full-scale tests. The feasibility and correctness of the experiment were verified by comparing the results with those of a finite element analysis. The results demonstrated that the weak point of the horizontal bar was the bearing capacity of the weld at the connection between the socket and the horizontal bar. Preventing buckling failure of the weld at the connection between the horizontal bar and the socket was critical to ensure the bearing capacity of the horizontal bar. Under the action of a shearing force, the wheel underwent buckling failure of the welding seam at the connection between the wheel and the vertical pole. With a decreasing number of connecting horizontal bars on the wheel, the shear capacity of the wheel decreased significantly. The shear failure mode of the sleeve was buckling failure. The connection weld did not undergo buckling failure during the load-bearing process, which was basically meeting the serviceability state. The failure of a single-layer vertical pole was typical with lateral displacement buckling, while the double-layer vertical pole did not undergo buckling with lateral displacement. Full article
(This article belongs to the Special Issue Damage Monitoring and Defect Identification Based on Deep/Machine Learning)
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23 pages, 9595 KiB  
Article
An Efficient Method for Detecting Asphalt Pavement Cracks and Sealed Cracks Based on a Deep Data-Driven Model
by Nan Yang, Yongshang Li and Ronggui Ma
Appl. Sci. 2022, 12(19), 10089; https://doi.org/10.3390/app121910089 - 7 Oct 2022
Cited by 10 | Viewed by 3697
Abstract
Thanks to the development of deep learning, the use of data-driven methods to detect pavement distresses has become an active research field. This research makes four contributions to address the problem of efficiently detecting cracks and sealed cracks in asphalt pavements. First, a [...] Read more.
Thanks to the development of deep learning, the use of data-driven methods to detect pavement distresses has become an active research field. This research makes four contributions to address the problem of efficiently detecting cracks and sealed cracks in asphalt pavements. First, a dataset of pavement cracks and sealed cracks is created, which consists of 10,400 images obtained by a vehicle equipped with a highway condition monitor, with 202,840 labeled distress instances included in these pavement images. Second, we develop a dense and redundant crack annotation method based on the characteristics of the crack images. Compared with traditional annotation, the method we propose generates more object instances, and the localization is more accurate. Next, to achieve efficient crack detection, a semi-automatic crack annotation method is proposed, which reduces the working time by 80% compared with fully manual annotation. Finally, comparative experiments are conducted on our dataset using 13 currently prevailing object detection algorithms. The results show that dense and redundant annotation is effective; moreover, cracks and sealed cracks can be efficiently and accurately detected using the YOLOv5 series model and YOLOv5s is the most balanced model with an F1-score of 86.79% and an inference time of 14.8ms. The pavement crack and sealed crack dataset created in this study is publicly available. Full article
(This article belongs to the Special Issue Damage Monitoring and Defect Identification Based on Deep/Machine Learning)
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25 pages, 5077 KiB  
Article
Multi-Feature Extraction-Based Defect Recognition of Foundation Pile under Layered Soil Condition Using Convolutional Neural Network
by Chuan-Sheng Wu, Tian-Qi Hao, Ling-Ling Qi, De-Bing Zhuo, Zhen-Yang Feng, Jian-Qiang Zhang and Yang-Xia Peng
Appl. Sci. 2022, 12(19), 9840; https://doi.org/10.3390/app12199840 - 29 Sep 2022
Cited by 3 | Viewed by 1708
Abstract
If the layer of soil surrounding a pile is not taken into account during the engineering detection process, the velocity-time curve might show asymptotic diameter shrinkage or diameter expanding features, which would alter the interpretation of the test findings. In this study, we [...] Read more.
If the layer of soil surrounding a pile is not taken into account during the engineering detection process, the velocity-time curve might show asymptotic diameter shrinkage or diameter expanding features, which would alter the interpretation of the test findings. In this study, we suggest combining multi-feature extraction and a convolutional neural network (CNN) to increase accuracy in pile defect recognition for layered soil conditions and traditional deep learning flaws. First, numerical simulations are run to create velocity–time curves for foundation piles under layered soil conditions. Then, the data are extracted from three dimensions: time domain, frequency domain, and time-frequency domain, respectively, and fused into a set of feature vectors. Finally, a foundation pile defect identification model combining multi-scale features and CNN is established. The findings demonstrate that the CNN model has 97.8% accuracy while the PNN has 28.6% accuracy, demonstrating that the approach is very reliable. Full article
(This article belongs to the Special Issue Damage Monitoring and Defect Identification Based on Deep/Machine Learning)
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26 pages, 6975 KiB  
Article
An Improved Method Based on EEMD-LSTM to Predict Missing Measured Data of Structural Sensors
by Zengshun Chen, Chenfeng Yuan, Haofan Wu, Likai Zhang, Ke Li, Xuanyi Xue and Lei Wu
Appl. Sci. 2022, 12(18), 9027; https://doi.org/10.3390/app12189027 - 8 Sep 2022
Cited by 10 | Viewed by 2218
Abstract
Time history testing using a shaking table is one of the most widely used methods for assessing the dynamic response of structures. In shaking-table experiments and on-site monitoring, acceleration sensors are facing problems of missing data due to the fact of measurement point [...] Read more.
Time history testing using a shaking table is one of the most widely used methods for assessing the dynamic response of structures. In shaking-table experiments and on-site monitoring, acceleration sensors are facing problems of missing data due to the fact of measurement point failures, affecting the validity and accuracy of assessing the structural dynamic response. The original measured signals are decomposed by ensemble empirical mode decomposition (EEMD), and the widely used deep neural networks (DNNs), gated recurrent units (GRUs), and long short-term memory networks (LSTMs) are used to predict the subseries of the decomposed original measured signal data to help model and recover the irregular, periodic variations in the measured signal data. The raw acceleration data of a liquefied natural gas (LNG) storage tank in shaking-table experiments were used as an example to compare and discuss the method’s performance for the complementation of missing measured signal data. The results of the measured signal data recovery showed that the hybrid method (EEMD based) proposed in this paper had a higher complementary performance compared with the traditional deep learning methods, while the EEMD-LSTM exhibited the best missing data complementary accuracy among all models. In addition, the effect of the number of prediction steps on the prediction accuracy of the EEMD-LSTM model is also discussed. This study not only provides a method to fuse EEMD and deep learning models to predict measured signal’ missing data but also provides suggestions for the use of EEMD-LSTM models under different conditions. Full article
(This article belongs to the Special Issue Damage Monitoring and Defect Identification Based on Deep/Machine Learning)
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21 pages, 7605 KiB  
Article
Seismic Response of a Large LNG Storage Tank Based on a Shaking Table Test
by Zengshun Chen, Zhengang Xu, Yang Liu, Jun Fu, Huayan Cheng, Likai Zhang and Xuanyi Xue
Appl. Sci. 2022, 12(15), 7663; https://doi.org/10.3390/app12157663 - 29 Jul 2022
Cited by 3 | Viewed by 2003
Abstract
In order to study the dynamic response of the LNG storage tank under the action of seismic load and the seismic isolation effect of the lead-core rubber bearing, this paper establishes the experimental storage tank model with reference to the structural form of [...] Read more.
In order to study the dynamic response of the LNG storage tank under the action of seismic load and the seismic isolation effect of the lead-core rubber bearing, this paper establishes the experimental storage tank model with reference to the structural form of the large-scale LNG storage tank, and the seismic response of the test tank is obtained using a shaking table test. Simplified mechanical models of non-isolated and isolated storage tanks are proposed and the seismic responses of the corresponding storage tanks are calculated using the Newmark-beta method. Under the action of seismic waves with different acceleration peaks, the results show that (a) more excitation directions of the seismic wave can lead to the greater acceleration and displacement response of the tank and (b) the isolation bearing has a damping effect on the acceleration response of the storage tank, but it has an amplifying effect on the displacement of the storage tank. Comparing the results of the simplified model and the shaking table test, it is found that the change trend of the acceleration response of the experimental results and simplified mechanical models is the same. The spectral characteristic curve of them is not large, which verifies the effectiveness of the simplified model. Full article
(This article belongs to the Special Issue Damage Monitoring and Defect Identification Based on Deep/Machine Learning)
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18 pages, 4395 KiB  
Article
Short-Term Passenger Flow Prediction of Urban Rail Transit Based on a Combined Deep Learning Model
by Zhongwei Hou, Zixue Du, Guang Yang and Zhen Yang
Appl. Sci. 2022, 12(15), 7597; https://doi.org/10.3390/app12157597 - 28 Jul 2022
Cited by 10 | Viewed by 2205
Abstract
It is difficult for a single model to simultaneously capture the nonlinear, correlation, and periodicity of data series in the passenger flow prediction of urban rail transit (URT). To better predict the short-term passenger flow of URT, based on the long short-term memory [...] Read more.
It is difficult for a single model to simultaneously capture the nonlinear, correlation, and periodicity of data series in the passenger flow prediction of urban rail transit (URT). To better predict the short-term passenger flow of URT, based on the long short-term memory network (LSTM) model, a deep learning model prediction method combining the time convolution network (TCN) and the long short-term memory network (LSTM) based on machine learning is proposed. The model couples the external factors such as date attributes, weather conditions, and air quality, to improve the overall prediction performance and solve the difficulty of accurate prediction due to the large fluctuation and randomness of short-term passenger flow in rail transit. Using the swiping data and related weather information of some stations of Chongqing Rail Transit Line 3, the TCN-LSTM model is verified by an example, and the prediction results of the single LSTM model are given for comparison. The results show that the TCN-LSTM model can better predict the passenger flow characteristics of different stations at different times. Compared with the single LSTM model, the TCN-LSTM model has better prediction accuracy and data generalization ability. Full article
(This article belongs to the Special Issue Damage Monitoring and Defect Identification Based on Deep/Machine Learning)
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