A Structural Health Monitoring Technique for the Analysis of Big Data of Bridges

Hong, Weixing; Li, Jiawei; Mao, Mingjie; Noori, Mohammad; Altabey, Wael A.

doi:10.1007/978-981-19-5783-3_5

Cited by 10 publications

(2 citation statements)

References 51 publications

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“…In recent years, the construction of beam structures has developed rapidly, and the health monitoring of beam structures has become a research hotspot at home and abroad. Reasonable configuration of sensors is the premise to ensure the quality of beam structure health monitoring, and it is very important to obtain accurate and real-time information on the health status of beam structures and realize the monitoring and evaluation of beam structures [1][2][3][4][5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Effective Technique for Structures Damage Detection Based on the Structural Frequency Maps

Altabey,

Noori,

et al. 2023

Proceedings of the 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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This research studies a novel damage detection framework for beam structure systems using displacement sensors. First, a finite element model is established under the impact load. Based on this model, an accumulative damage model is introduced to model the beam damage, and the damage case is considered. Both static and transient displacement are collected from the beam sensors installing a long beam structure. A system transfer function (TF) is proposed and applied to the "open loop" beam system. Results show these approaches perform great promises when damage evolves in beam structure.

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Section: Introductionmentioning

confidence: 99%

Effective Technique for Structures Damage Detection Based on the Structural Frequency Maps

Altabey,

Noori,

et al. 2023

Proceedings of the 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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“…Damages such as cracks, and corrosion on the pipeline surface during pipeline operation will adversely affect environment and pipeline safety. Rapid and accurate detection of pipeline surface damages is an effective guarantee for maintaining the health and safety of pipeline surfaces [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Crack, Location and Area Identification for a Pipeline by the Convolutional Neural Network Based on Crack Contour Network Method

Altabey,

Noori,

2023

Proceedings of the 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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In this work, the Convolutional Neural Network (CNN) algorithm is introduced in pipeline surface cracks monitoring-based image processing method for improving the efficiency and accuracy of crack type, location, and area identification. The method is used to extract the cracks area called the CNN based on crack contour network (CCN-CNNs) method from locate and extract the crack shape. CCN-CNNs is provides the accuracy rate (P%), recall rate (R%), and F-score (F%) index to assess the algorithm in the problem while identifying the cracks, and then according to the maximum F-score, we computes the crack corresponding contour area. In this work the pipeline crake images datasets are provided using an inspection drone with high definition camera. To the best of the authors' knowledge, the methodology presented in this paper for pipeline crack identification is an original contribution to the literature. This work introduces an efficient approach that also significantly reduces the time for crack type, location, and area identification of pipelines, the accuracy rate (P%), recall rate (R%), and Fscore (F%) are recorded 91.8% ,86.1%, and 84.6% respectively. 3357 COMPDYN 2023 9 th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering M. Papadrakakis, M. Fragiadakis (eds.

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Intelligent Monitoring and Vision Based Vibration Measurement on Bridges

Hong,

Jia,

Silik

et al. 2024

Lecture Notes in Civil Engineering

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A Structural Health Monitoring Technique for the Analysis of Big Data of Bridges

Cited by 10 publications

References 51 publications

Effective Technique for Structures Damage Detection Based on the Structural Frequency Maps

Effective Technique for Structures Damage Detection Based on the Structural Frequency Maps

Deep Learning-Based Crack, Location and Area Identification for a Pipeline by the Convolutional Neural Network Based on Crack Contour Network Method

Intelligent Monitoring and Vision Based Vibration Measurement on Bridges

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