Real-time monitoring unsafe behaviors of portable multi-position ladder worker using deep learning based on vision data.

INTRODUCTION: Fatal fall from height accidents, especially on construction sites, persist, underscoring the importance of monitoring and managing worker behaviors to enhance safety. Deep learning showed the possibility of substituting the manual work of safety managers. However, applying detection results to determine compliance with safety regulations has limitations. METHOD: This study estimated the actual working height depending on the height of the object detection bounding box by specifying the consistent hinge part as a target marker based on ladder manufacturing regulations. Furthermore, an attempt was made to improve the separation between workers, coworkers, and persons unconnected to ladder activities by applying an optimized loss function alongside an attention mechanism. RESULTS: The experimental results showed that an average precision increased from 87.60% to 90.44%. The performance of the monitoring unsafe behavior of ladder worker following the Korea Occupational Safety and Health Agency (KOSHA) guide was evaluated by 91.40 F1-Score, which accumulated sorted according to the working height. CONCLUSIONS: Experimental results show the feasibility of the real-time automate safety monitoring in ladder work. PRACTICAL APPLICATIONS: By linking the estimated working height and deep learning multi-detection results to established safety regulations, the proposed method shows the potential to automatically monitoring unsafe behaviors in construction site.

Artificial Intelligence-Based Bolt Loosening Diagnosis Using Deep Learning Algorithms for Laser Ultrasonic Wave Propagation Data.

The application of deep learning (DL) algorithms to non-destructive evaluation (NDE) is now becoming one of the most attractive topics in this field. As a contribution to such research, this study aims to investigate the application of DL algorithms for detecting and estimating the looseness in bolted joints using a laser ultrasonic technique. This research was conducted based on a hypothesis regarding the relationship between the true contact area of the bolt head-plate and the guided wave energy lost while the ultrasonic waves pass through it. First, a Q-switched Nd:YAG pulsed laser and an acoustic emission sensor were used as exciting and sensing ultrasonic signals, respectively. Then, a 3D full-field ultrasonic data set was created using an ultrasonic wave propagation imaging (UWPI) process, after which several signal processing techniques were applied to generate the processed data. By using a deep convolutional neural network (DCNN) with a VGG-like architecture based regression model, the estimated error was calculated to compare the performance of a DCNN on different processed data set. The proposed approach was also compared with a K-nearest neighbor, support vector regression, and deep artificial neural network for regression to demonstrate its robustness. Consequently, it was found that the proposed approach shows potential for the incorporation of laser-generated ultrasound and DL algorithms. In addition, the signal processing technique has been shown to have an important impact on the DL performance for automatic looseness estimation.

MFL-Based Local Damage Diagnosis and SVM-Based Damage Type Classification for Wire Rope NDE.

Wire ropes used in various applications such as elevators and cranes to safely carry heavy weights are vulnerable to breakage or cross-sectional loss caused by the external environment. Such damage can pose a serious risk to the safety of the entire structure because damage under tensile force rapidly expands due to concentration of stress. In this study, the magnetic flux leakage (MFL) method was applied to diagnose cuts, corrosion, and compression damage in wire ropes. Magnetic flux signals were measured by scanning damaged wire rope specimens using a multi-channel sensor head and a compact data acquisition system. A series of signal-processing procedures, including the Hilbert transform-based enveloping process, was applied to reduce noise and improve the resolution of signals. The possibility of diagnosing several types of damage was verified using enveloped magnetic flux signals. The characteristics of the MFL signals according to each damage type were then analyzed by comparing the extracted damage indices for each damage type. For automated damage type classification, a support vector machine (SVM)-based classifier was trained using the extracted damage indices. Finally, damage types were automatically classified as cutting and other damages using the trained SVM classifier.