A hybrid theoretical–numerical–experimental framework for robust health monitoring of thin-walled hollow composite members using guided waves by Akshay Prakash Kalgutkar, Shirsendu Sikdar, Sauvik Banerjee, Karl Walton, Rakesh Mishra

“…It focuses on assessing surface abrasion and hairline cracks, two common yet challenging damage types encountered in the field. …”
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Effect of Drying and Wetting Cycles on Deformation Characteristics of Compacted Loess and Constitutive Model by Pengju Qin, Yuqi Liu, Chungang Yang, Qingchen Yan, Yubo Liu, Li Gong, Xingji Zhu

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Enhanced convolutional neural networks for defect detection in fiber-reinforced composites: a hyperparameter optimization approach by Pankaj Beldar, Rasika Chandramore, Sarika Jagtap, Dipali Bendale

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Evaluation of the Magnet Breakaway Force Measurement Accuracy of the NT-800 Sensors for Early Detection of Defects of Their Manufacturing by A. Yu. Kutsepau, A. P. Kren, Y. V. Hnutsenka

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Detection of Defects in Polyethylene and Polyamide Flat Panels Using Airborne Ultrasound-Traditional and Machine Learning Approach by Artur Krolik, Radosław Drelich, Michał Pakuła, Dariusz Mikołajewski, Izabela Rojek

“…This paper presents the use of noncontact ultrasound for the nondestructive detection of defects in two plastic plates made of polyamide (PA6) and polyethylene (PE). …”
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Investigation of Effectiveness of Some Vibration-Based Techniques in Early Detection of Real-Time Fatigue Failure in Gears by Hasan Ozturk, Isa Yesilyurt, Mustafa Sabuncu

“…It has been found that the initiation and progression of fatigue crack cannot be easily detected by conventional time and frequency domain approaches until the fault is significantly developed. …”
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WDNET-YOLO: Enhanced Deep Learning for Structural Timber Defect Detection to Improve Building Safety and Reliability by Xiaoxia Lin, Weihao Gong, Lin Sun, Xiaodong Yang, Chunwei Leng, Yan Li, Zhenyu Niu, Yingzhou Meng, Xinyue Xiao, Junyan Zhang

“…To overcome these challenges, this study proposes WDNET-YOLO: an enhanced deep learning model based on YOLOv8n for high-precision defect detection in structural wood. First, the RepVGG reparameterized backbone utilizes multi-branch training to capture critical defect features (e.g., distributed cracks and dense clusters of knots) across scales. …”
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RHCrackNet: Refined Hierarchical Feature Fusion and Enhancement Network for Pixel-Level Pavement Anomaly Detection by Wenjing Liu, Zhenhua Li, Ji Wang, Qingjie Lu

“…While existing Convolutional Neural Network (CNN) approaches have achieved high performance, their robustness to texture noise is limited, and the completeness of detected pixel-level cracks remains uncertain due to insufficient extraction of contextual information. …”
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Comparative Performance Evaluation of YOLOv5, YOLOv8, and YOLOv11 for Solar Panel Defect Detection by Rahima Khanam, Tahreem Asghar, Muhammad Hussain

“…The reliable operation of photovoltaic (PV) systems is essential for sustainable energy production, yet their efficiency is often compromised by defects such as bird droppings, cracks, and dust accumulation. Automated defect detection is critical for addressing these challenges in large-scale solar farms, where manual inspections are impractical. …”
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Methodology for detecting and assessing the dynamics of defects in engineering structures by processing images from an unmanned aerial vehicle by M.N. Suetin, V.E. Dementiev, A.G. Tashlinskii, R.G. Magdeev

“…An example of the implementation and testing of the methodology for detecting and assessing the dynamics of cracks in metal structures of bridge crossings is given. …”
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Applications of Artificial Intelligence Enhanced Drones in Distress Pavement, Pothole Detection, and Healthcare Monitoring with Service Delivery by Yue Wang, Tian Ye

“…Second, the main constraints related to different computer vision techniques are highlighted for detecting distress in the pavement. Then, the possible research solution to some of the distress issues such that detection of pavement texture, cracks or potholes, joint faulting, temperature segregation, and rutting issues are predicted. …”
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Detecting deformation mechanisms of metals from acoustic emission signals through knowledge-driven unsupervised learning by Boyuan Gou, Yan Chen, Songhua Xu, Jun Sun, Turab Lookman, Ekhard K. H. Salje, Xiangdong Ding

“…Abstract Timely detection of deformation mechanisms in metallic structural materials is essential for early-warning alerts on potential damages and fractures. …”
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Ultrasonic phased array flexible coupling detection of defects in high-voltage cable terminal lead seals by Peng Ziping, Zheng Jishi, Zhen Zhiming, Huang Yaosheng, Li Xiaobin, Cai Qiushen

“…To address the ineffective detection of lead seal defects, a non-destructive detection method based on ultrasonic phased array flexible water bag coupling is proposed. …”
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A Transmission Line Defect Detection Method Based on YOLOv7 and Multi-UAV Collaboration Platform by Rong Chang, Peng Xiao, Hongqiang Wan, Songlin Li, Chengjiang Zhou, Fei Li

“…Finally, a complete transmission line defect images dataset is constructed, and the introduction of several defect images such as insulator self-blast and cracked insulators avoids the problem of low application value of single defect detection. …”
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Novel conditional tabular generative adversarial network based image augmentation for railway track fault detection by Ali Raza, Rukhshanda Sehar, Abdul Moiz, Ala Saleh Alluhaidan, Sahar A. El-Rahman, Diaa Salama AbdElminaam

“…Classical methods for fault detection, including manual inspections and simple sensor-based systems, face significant challenges, such as high labour costs, human error, and limited detection accuracy under varying environmental conditions. …”
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Artificial intelligence Defect Detection Robustness inReal-time Non-Destructive Testing of Metal Surfaces by Chaoyu Dong, Jovian Sanjaya Putra, Andrew A. Malcolm

“… Artificial intelligence (AI) is revolutionizing defect detection by employing advanced computational techniques to enhance accuracy and efficiency. …”
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YOLOv10n-Based Defect Detection in Power Insulators: Attention Enhancement and Feature Fusion Optimization by Zhihao Wei, Yan Wei

“…The Concat module in Neck is replaced by a two-way weighted feature fusion mechanism, which constructs a top-down and bottom-up two-way information flow, reduces the loss of cross-layer information, and significantly enhances the model’s ability to detect multi-scale defects, such as insulator cracks and fouling, and especially improves the resolution of details of small targets in long-distance shooting scenarios. …”
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Research on Damage Detection Methods for Concrete Beams Based on Ground Penetrating Radar and Convolutional Neural Networks by Ning Liu, Ya Ge, Xin Bai, Zi Zhang, Yuhao Shangguan, Yan Li

“…The feasibility of this method in the research field of damage detection of concrete structures has been verified.…”
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YOLOv9: A High-Performance Deep Learning Approach for Asphalt Pavement Distresses Detection in Roadway Images by Fahrizal, Siti Nurjanah, Yoan Purbolingga, Dila Marta Putri, Asde Rahmawati, Bastul Wajhi Akramunnas, Muhidin Arifin

“…Further enhancements are necessary to refine longitudinal cracking detection and evaluate the model across a broader spectrum of road conditions.…”
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Technology and application of treating water inrush disasters in underground engineering with the method of physical detection combined with grouting by Huangbin Jiang, Huangbin Jiang, Chu Jiang, Chu Jiang, Di Chen, Di Chen, Xiang Qiu, Xiang Qiu

“…In view of engineering problems such as water gushing in karst geology with relatively high-water flow velocity and water seepage at the joints of the underground diaphragm wall which is concealed, a treatment method for water gushing disasters in karst geology based on physical detection combined with grouting is proposed. The main steps of this method are as follows: Firstly, according to the analysis results of engineering geological investigations, by selecting appropriate physical detection means, accurately locate the hidden danger positions such as underground water gushing channels and cracks. …”
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