Data-driven multi-fault detection in pipelines utilizing frequency response function and artificial neural networks by Hussein A. M. Hussein, Sharafiz B. Abdul Rahim, Faizal B. Mustapha, Prajindra S. Krishnan, Nawal Aswan B. Abdul Jalil

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Failure Detection of Laser Welding Seam for Electric Automotive Brake Joints Based on Image Feature Extraction by Diqing Fan, Chenjiang Yu, Ling Sha, Haifeng Zhang, Xintian Liu

“…And the overall missed detection rate is less than 3%, with both the missed detection rate and false detection rate for pit defects being 0%. …”
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A Hybrid Learning Framework for Enhancing Bridge Damage Prediction by Amal Abdulbaqi Maryoosh, Saeid Pashazadeh, Pedram Salehpour

“…Therefore, there is a pressing need for automated and accurate techniques for bridge damage detection. This study aims to propose a novel method for bridge crack detection that leverages a hybrid supervised and unsupervised learning strategy. …”
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A Method for Solid Axle Flaw Inspection Based on Axle End Coupling by Phased Array Ultrasonic Technology by LIU Zhiyong, PENG Chaoyong

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AI-Driven Detection of Alkali-Silica Reaction in Concrete Structures Using Feature-Enhanced Deep Learning Models by Yujie Wu, Mengze Wu, Tianyi Cui, Jiani Lin, Qingke Liao, Jinqiu Shu

“…Concrete’s affordability, adaptability, and resilience make it a cornerstone of construction, yet its vulnerability to degradation, particularly Alkali-Silica Reaction (ASR), poses significant challenges. ASR induces cracking and structural instability, necessitating efficient detection methods to mitigate its impacts. …”
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Fiber Bragg Grating Detection and Nonlinear Thermal Strain Response of Annular Concrete Structures Under Stress Constraints by Chuan Li, Yusi Ran, Jiaqi Li, Qiang Xu, Hao Liu, Xiaorong Wan

“…To investigate the complex thermodynamic response of annular concrete structures under the combined constraints of stress and temperature, a detection model was developed to measure structural strain by combining the structural mechanics model with fiber Bragg grating (FBG) detection technology, while a numerical simulation model was created to reflect the mechanical response of the annular concrete structures. …”
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Physical Simulation Test and Numerical Simulation Study on Force and Deformation of Inclined Shaft Lining Using OFDR Testing Technology by Wenjie Yao, Tingting Luo, Weihao Yang, Zhijiang Yang

“…The findings indicate that: (1) OFDR provides accurate strain measurements consistent with conventional methods, and (2) has the ability to detect cracks that cannot be identified by the human eye. …”
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Structural Health Monitoring by Fiber Optic Sensors by Alfredo Guemes, Luis Eduardo Mujica, Daniel del-Río-Velilla, Antonio Fernandez-Lopez

“…Although a crack creates a significant strain field at its tip, its effect on the strain field becomes nearly negligible only a few centimeters away from the crack, which complicates the task of damage detection. …”
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Detection and Strong Classification of Natural Weld Defects by Magneto-Optical Imaging Under Rotating Magnetic Field Excitation by Yanfeng Li, Xiangdong Gao, Yanxi Zhang

“…The grayscale values and texture features of MO images can reflect the leakage magnetic field characteristics of weld defects. The MO imaging detection system excited by a rotating magnetic field is used to obtain MO images of defects such as non-penetration, pit, subsurface crack, surface crack, and no defect for weld defect diagnosis. …”
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A New Feature Extraction Technique Based on 1D Local Binary Pattern for Gear Fault Detection by Zrar Kh. Abdul, Abdulbasit Al-Talabani, Ayub O. Abdulrahman

“…The extracted features from the original signals are utilized as inputs to a classifier based on k-Nearest Neighbour (k-NN) and Support Vector Machine (SVM) for three classes (normal, break, or crack). The effectiveness of the proposed approach is evaluated for gear fault detection, on the vibration data obtained from the Prognostic Health Monitoring (PHM’09) Data Challenge. …”
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Automatic Detection and Classification of Natural Weld Defects Using Alternating Magneto-Optical Imaging and ResNet50 by Yanfeng Li, Pengyu Gao, Yongbiao Luo, Xianghan Luo, Chunmei Xu, Jiecheng Chen, Yanxi Zhang, Genxiang Lin, Wei Xu

“…It is difficult to detect and identify natural defects in welded components. …”
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Frequent Glacial Hazard Deformation Detection Based on POT-SBAS InSAR in the Sedongpu Basin in the Himalayan Region by Haoliang Li, Yinghui Yang, Xiujun Dong, Qiang Xu, Pengfei Li, Jingjing Zhao, Qiang Chen, Jyr-Ching Hu

“…The maximum deformation rate of SDP1 was 74 m/a and the slope cumulative deformation exceeded 500 m during the monitoring period from 2014 to 2022, which is still in active motion at present; for SDP2, a cumulative deformation of more than 300 m was also detected over the monitoring period. Glaciers SDP3, SDP4, and SDP5 have been relatively stable until now; however, ice cracks are well developed in SDP4 and SDP5, and ice avalanche events may occur if these ice cracks continue to expand under extreme natural conditions in the future. …”
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Orthogonal differential eddy current testing for internal detection of oil and gas pipeline based on incremental permeability extraction by Yanshuang GONG, Xiaokang WAN, Hongjun WANG, Jiaxing XIN, Guangming CHEN, Jinzhong CHEN, Bingfeng YU, Yilai MA

“…Objective Serving as the primary mode of transporting oil and natural gas, pipelines are prone to corrosion, cracks, and other defects under intricate operational conditions. …”
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Weld-CNN: Advancing non-destructive testing with a hybrid deep learning model for weld defect detection by Ngo Thi Hoa, Tang Ha Minh Quan, Quoc Bao Diep

“…Trained on a comprehensive dataset of 24,407 X-ray images covering four weld defect categories (cracks, porosity, lack of penetration, and no defect), Weld-CNN achieved a test accuracy of up to 99.83%. …”
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Improved railway track faults detection using Mel-frequency cepstral coefficient and constant-Q transform features by Rahman Shafique, Khadija Kanwal, Venkata Chunduri, Gyu Sang Choi, Imran Ashraf

“…Some factors including cracks, rail discontinuity, ballast issues, burn wheels, super-elevation, loose nuts and bolts, and misalignment developed on the railways due to pre-emptive investigations, non-maintenance, and delay in detection pose grave threats and danger to the safe operation of railway transportation. …”
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Lightweight YOLOv8 for tongue teeth marks and fissures detection based on C2f_DCNv3 by Chunyang Jin, Delong Zhang, Xiyuan Cao, Zhidong Zhang, Chenyang Xue, Yanjun Zhang

“…Additionally, the model reduces computational cost by approximately one-third in terms of FLOPS, maintaining high accuracy while greatly decreasing the number of parameters, thus offering a more robust and resource-efficient solution. For tongue crack detection, the mAP increases to 91.34%, with notable improvements in F1 score, precision, and recall. …”
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The Research on Path Planning Method for Detecting Automotive Steering Knuckles Based on Phased Array Ultrasound Point Cloud by Yihao Mao, Jun Tu, Huizhen Wang, Yangfan Zhou, Qiao Wu, Xu Zhang, Xiaochun Song

“…Finally, an automatic detection system was set up to test a steering knuckle specimen with surface circumferential cracks. …”
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Probabilistic prediction of fatigue damage based on linear fracture mechanics by M. Krejsa, L. Koubova, J. Flodr, J. Protivinsky, Q. T. Nguyen

“…Three sizes are important for the characteristics of the propagation of fatigue cracks - the initial size, detectable size and acceptable size. …”
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YOLOv8n-DSDL: A lightweight dual-backbone network with decoupled self-attention for cotton maturity detection by Meiqi Zhong, Linjing Wei, Henghui Mo

“…However, existing models often suffer from low detection accuracy and poor adaptability due to challenges such as varietal diversity, rapid changes in maturity stages, difficulty in small object detection, and environmental interferences like lighting and occlusion. …”
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Artificial intelligence with feature fusion empowered enhanced brain stroke detection and classification for disabled persons using biomedical images by Mohammed Alsieni, Khaled H. Alyoubi

“…Blockage of brain vessels causes an ischemic stroke, while cracks in blood vessels in or around the brain cause a hemorrhagic stroke. …”
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