Artificial intelligence Technique for Pavement Diseases Identification

Hong, Weixing; Wang, Ju; Guo, Dangui; Wang, Xiaodong; Gongzuo, Shi; Xu, Yanliang; Zhang, Chuanyue; Noori, Mohammad; Altabey, Wael A.

doi:10.1145/3568923.3570515

Cited by 7 publications

(2 citation statements)

References 31 publications

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“…Nowadays, more and more use intelligent and digital means to automatically capture various structural safety-related data such as environmental temperature and humidity, expansion joint displacement, etc. through the beam and plate structural health monitoring system, identify and record the development of existing diseases in real time, Discover new diseases and report it, assist in decision-making and judgment, reduce maintenance costs, and improve monitoring efficiency and quality [13][14][15][16][17][18][19][20][21][22][23][24][25][26].…”

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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