Identification of fastening compliance for intelligent work-at-height safety belts based on multimodal time-series analysis

Vision-Based Progress Compliance and Worker Health Monitoring in Concrete Pouring and Vibration

This paper proposes a vision-based framework named concrete progress and concrete health monitoring (CPCHM) for automated compliance supervision and health risk assessment during concrete pouring and vibration operations. The framework integrates YOLOv8-pose for worker posture estimation and YOLOv8-detection for equipment identification, achieving average accuracies of 92.8% and 99.5%, respectively. By fusing posture and equipment features, a support vector machine classifier distinguishes between pouring and vibration operations with 90.8% accuracy and an F1-score of 90.3%. Further, a spatiotemporal graph convolutional network is employed to model elbow joint dynamics and assess musculoskeletal risks, reaching a behavioral classification accuracy of 89.6%. To address occlusion and multiworker collaboration, CPCHM introduces a distance-based operator identification method and an adaptive region-of-interest inference mechanism, maintaining stable keypoint tracking and continuous elbow-angle estimation even under partial visibility. The framework is embedded on a Jetson nano edge device, which automatically triggers an acoustic buzzer alert when pouring durations exceed 90 min or vibration times fall outside the 5–15 s standard. CPCHM provides a compact, sensor-free, and scalable solution for integrating progress compliance monitoring and ergonomic health assessment, enabling intelligent and real-time supervision in dynamic concrete construction environments.

YOLO-NAS based Intelligent Workplace Safety Monitoring System for Real-Time Personal Protective Equipment Compliance Detection

Workplaces in construction and industry suffer from a significant number of workplace accidents because of a lack of safety mechanisms like helmets and high-visibility vests. In order to solve this issue, an automatic detection system for detecting the presence of the worker's helmet & safety vest using a deep learning model is developed. You Only Look Once – Neural Architecture Search (YOLO-NAS) algorithm was chosen to be used in the model because of its fast and high-quality detection process. A dataset with images containing healthy workers wearing helmets & safety vests (with some workers appearing in different safety vests) is collected and used as the training set. The resulting model is then saved for future usage in making predictions with new images. Then, this model is connected to Streamlit, which provides a convenient Web-based user interface through which a user can provide an image as an input to the model. The trained neural network is applied to the input image, and all detected objects that correspond to helmet & safety vest are put in bounding boxes on the input image. Moreover, each of the bounding boxes is labeled with the name of the object and confidence score of the detection. Thus, a clear visualization of the safety equipment worn by the worker is provided. Overall, the developed detection system significantly decreases the human intervention needed for visual verification of the safety helmet or vest. The described system is a user-friendly and cost-effective way of evaluating workplace safety compliance.

An Integrated Safety Management Framework for Working at Height Using Smart PPE, Artificial Intelligence and Risk Assessment

The development of an AI and Internet of Things (IoT) enabled Smart Safety Harness System for enhancing working at height safety in industrial applications with significant potential for application in construction, manufacturing, petrochemical, power, mining, and infrastructure industries.

Intelligent discrimination method for the closed state of relay contacts in high-speed rail trains

To ensure the operational safety of high-speed trains, accurate assessment of relay contact closure is critical. This paper proposes a physical- data fusion approach for intelligent monitoring and diagnosis to overcome the inefficiency and safety risks of manual inspection. A wireless embedded system with a 16 × 16 flexible pressure sensor array is developed to capture real-time pressure distribution during contact closure. A feature extraction method combining spatiotemporal processing and a damped oscillation model is used to derive multidimensional features from raw pressure data. A 3D convolutional neural network and a support vector machine with handcrafted features are constructed for contact status classification. Experimental results show that the 3DCNN achieves 99.5% accuracy and the SVM achieves 94.8%, demonstrating the effectiveness of both models in distinguishing good from poor contact conditions. This study provides a new technical pathway for efficient and intelligent relay maintenance in high-speed rail applications.

Vibration and time based feature engineering for comprehensive gear health

Field Measurement and FE-Augmented LSTM-RF Prediction for Low-Frequency Industrial Vibrations on High-Speed Railway Bridges

The structural integrity and operational safety of High-Speed Railway (HSR) bridges are increasingly threatened by low-frequency industrial vibrations. As traditional numerical methods are inadequate for real-time monitoring, this study proposes a novel FE-Augmented intelligent prediction framework to evaluate and forecast the structural response of HSR bridges subjected to ambient industrial excitations. Field measurements conducted on an HSR bridge near a stone-processing plant revealed a severe 1.5 Hz global resonance, with lateral displacements exceeding safety limits by a factor of 4.4 even without train loads. To overcome data scarcity for machine learning, a 3D coupled bridge-soil finite element model was developed to augment the vibration dataset. Subsequently, a hybrid deep learning framework integrating Long Short-Term Memory (LSTM) networks and Random Forest (RF) was established. By aligning the LSTM input time-window with the physical wave propagation delay, the predictive model gains physical interpretability. Furthermore, ablation studies confirmed that integrating spatial waveguide information with hybrid features minimizes prediction errors. Validation demonstrates that by fusing LSTM-extracted deep temporal features with handcrafted statistical indicators, the proposed LSTM-RF framework overcomes the amplitude clipping effect common in standard neural networks. It accurately captures complex spatial interference and beat phenomena under multi-machine operations, achieving a high Coefficient of Determination (R 2 = 0.965) and a low Normalized RMSE of 2.43% under extreme load scenarios. The trained hybrid model accelerates prediction inference to the millisecond scale, representing over six orders of magnitude improvement in computational efficiency compared to conventional 3D FEM. This framework provides an efficient, real-time early-warning methodology for safeguarding HSR infrastructure against complex environmental vibrations.