Vibration‐based structural state identification by a 1‐dimensional convolutional neural network

Zhang, Youqi; Miyamori, Yasunori; Mikami, Shuichi; Saito, Takehiko

doi:10.1111/mice.12447

Cited by 219 publications

(102 citation statements)

References 56 publications

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“…At present, deep learning methods are popularly applied for various applications. For example, damage identification on structures with images (Cha, Choi, & Büyüköztürk, 2017;Gao, Kong, & Mosalam, 2019;Gao & Mosalam, 2018;Li, Zhao, & Zhou, 2019;Ni, Zhang, & Chen, 2019;Wu et al, 2019;Yang et al, 2018), with sensor measurements (Huang, Beck, & Li, 2019;Y. Zhang, Miyamori, Mikami, & Saito, 2019), concrete property estimation (Rafiei, Khushefati, Demirboga, & Adeli, 2017), and vehicle type detection in real traffic data (Molina-Cabello, Luque-Baena, López-Rubio, & Thurnhofer-Hemsi, 2018).…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural networks for pavement roughness assessment using calibration‐free vehicle dynamics

Jeong

Ditzler

2020

Computer aided Civil Eng

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Road roughness is a measure of how uncomfortable a ride is, and provides an important indicator for the needs of roadway maintenance or repavement, which is closely tied to the state and federal budget prioritization. As such, accurate and timely monitoring of deteriorating road conditions and following maintenance are essential to improve the overall ride quality on the road. Various technologies, including vehiclemounted laser profiling systems, have been developed and adopted for road roughness (e.g., IRI-International Roughness Index) measurement; however, their high cost limits their use. While recent advances in smartphone technologies allow us to use their embedded accelerometers for road roughness monitoring, the complicated process of necessary vehicle calibration hinders the widespread use of the technology in the actual practices. In this work, a deep learning IRI estimation method is proposed with the goal of using anonymous (i.e., calibration-free) vehicles and their responses measured by smartphones as road roughness sensors. A state-of-the-art deep learning algorithm (i.e., CNN-convolutional neural network) and multimetric vehicle dynamics data (i.e., accelerometer, gyroscope), possibly measured by drivers' smartphones, are employed for the purpose. Optimized CNN architecture and data configuration have been investigated to achieve the best performance. The efficacy of the proposed method has been numerically validated using real road IRI information (i.e., Speedway, Tucson, AZ), real driving speed profiles, and four different types of vehicle data with associated uncertainties.

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Section: Introductionmentioning

confidence: 99%

Convolutional neural networks for pavement roughness assessment using calibration‐free vehicle dynamics

Jeong

Ditzler

2020

Computer aided Civil Eng

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“…Besides, the computational cost for real-time image processing is expensive [ 31 ], while to the best knowledge of the author, there are few researches on the vehicle detection and classification based on DL using one-dimensional signal data. To inspire the methodology, one-dimensional CNN has already been utilized in damage detection based on vibration data [ 32 ].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Based Vehicle Detection and Classification Methodology Using Strain Sensors under Bridge Deck

Zhang

Dong

et al. 2020

Sensors

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Vehicle detection and classification have become important tasks for traffic monitoring, transportation management and pavement evaluation. Nowadays there are sensors to detect and classify the vehicles on road. However, on one hand, most sensors rely on direct contact measurement to detect the vehicles, which have to interrupt the traffic. On the other hand, complex road scenes produce much noise to consider when to process the signals. In this paper, a data-driven methodology for the detection and classification of vehicles using strain data is proposed. The sensors are well arranged under the bridge deck without traffic interruption. Next, a cascade pre-processing method is applied for vehicle detection to eliminate in-situ noise. Then, a neural network model is trained to identify the close-range following vehicles and separate them by Non-Maximum Suppression. Finally, a deep convolutional neural network is designed and trained to identify the vehicle types based on the axle group. The methodology was applied in a long-span bridge. Three strain sensors were installed beneath the bridge deck for a week. High robustness and accuracy were obtained by these algorithms. The methodology proposed in this paper is an adaptive and promising method for vehicle detection and classification under complex noise. It would serve as a supplement to current transportation systems and provide reliable data for management and decision-making.

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“…Avci et al [6] addressed the loss of connection stiffness of a steel frame structure via a novel structural health monitoring (SHM) method using 1DCNN and wireless sensors networks. Zhang et al [7] developed a 1DCNN method for VSHM of bridge structures and successfully tested on both a simplified laboratory model and a real steel bridge. Ince [8] demonstrated that the 1DCNN architecture was highly effective in real-time monitoring motor conditions because their model took only 1.0 ms per classification, and the experimental accuracy result was more than 97%.…”

Section: Introductionmentioning

confidence: 99%

Structural damage detection using hybrid deep learning algorithm

Hưng

Hung

Pham

et al. 2020

STCE

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Timely monitoring the large-scale civil structure is a tedious task demanding expert experience and significant economic resources. Towards a smart monitoring system, this study proposes a hybrid deep learning algorithm aiming for structural damage detection tasks, which not only reduces required resources, including computational complexity, data storage but also has the capability to deal with different damage levels. The technique combines the ability to capture local connectivity of Convolution Neural Network and the well-known performance in accounting for long-term dependencies of Long-Short Term Memory network, into a single end-to-end architecture using directly raw acceleration time-series without requiring any signal preprocessing step. The proposed approach is applied to a series of experimentally measured vibration data from a three-story frame and successful in providing accurate damage identification results. Furthermore, parametric studies are carried out to demonstrate the robustness of this hybrid deep learning method when facing data corrupted by random noises, which is unavoidable in reality. Keywords: structural damage detection; deep learning algorithm; vibration; sensor; signal processing.

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Vibration‐based structural state identification by a 1‐dimensional convolutional neural network

Cited by 219 publications

References 56 publications

Convolutional neural networks for pavement roughness assessment using calibration‐free vehicle dynamics

Convolutional neural networks for pavement roughness assessment using calibration‐free vehicle dynamics

Deep Learning Based Vehicle Detection and Classification Methodology Using Strain Sensors under Bridge Deck

Structural damage detection using hybrid deep learning algorithm

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