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Workers' unsafe behavior is the main cause of construction safety accidents, thereby highlighting the critical importance of behavior-based management. To compensate for the limitations of computer vision in tackling knowledge-intensive issues, semantic-based methods have gained increasing attention in the field of construction safety management. Knowledge graph provides an efficient and visualized method for the identification of various unsafe behaviors.

This study proposes an unsafe behavior identification framework by integrating computer vision and knowledge graph–based reasoning. An enhanced ontology model anchors our framework, with image features from YOLOv5, COCO Panoptic Segmentation and DeepSORT integrated into the graph database, culminating in a structured knowledge graph. An inference module is also developed, enabling automated the extraction of unsafe behavior knowledge through rule-based reasoning.

A case application is implemented to demonstrate the feasibility and effectiveness of the proposed method. Results show that the method can identify various unsafe behaviors from images of construction sites and provide mitigation recommendations for safety managers by automated reasoning, thus supporting on-site safety management and safety education.

Existing studies focus on spatial relationships, often neglecting the diversified spatiotemporal information in images. Besides, previous research in construction safety only partially automated knowledge graph construction and reasoning processes. In contrast, this study constructs an enhanced knowledge graph integrating static and dynamic data, coupled with an inference module for fully automated knowledge-based unsafe behavior identification. It can help managers grasp the workers’ behavior dynamics and timely implement measures to correct violations.

This study aims to produce composite pigments, including SHS/ZnAl-LDHs, IDS/ZnAl-LDHs and SNND/ZnAl-LDHs, with improved coloration, enhanced photostability and thermostability and biocompatibility.

The chemical structures of the composite pigments were characterized by X-ray diffraction spectroscopy and Fourier transform infrared spectroscopy. Photostability and thermal stability were assessed using ultraviolet-visible spectroscopy and colorimetry. The coverage of the dyes was determined through black-and-white tile testing, and specific RGB values were used to indicate color expressiveness. Finally, a four-color eyeshadow was formulated, and safety tests were conducted via human patch test and cellular assays to confirm the safety and reliability of the samples.

The experimental results demonstrate an enhancement in the photo and thermal stability of the SHS/ZnAl-LDHs, IDS/ZnAl-LDHs and SNND/ZnAl-LDHs composites, along with their superior performance in terms of covering power and color saturation. These composite pigments also exhibit high safety, making them well-suited for cosmetic applications.

The composite pigments based on hydrotalcite can be used in the cosmetic industry without causing any harm to the environment and human health.

The addition of hydrotalcite enables better application of pigments in cosmetics.