Ontology-based text convolution neural network (TextCNN) for prediction of construction accidents

Shi, Donghui; Li, Zhigang; Zurada, Jozef; Manikas, Andrew; Guan, Jian; Weichbroth, Pawel

doi:10.1007/s10115-023-02036-9

Cited by 3 publications

(1 citation statement)

References 61 publications

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“…Combined with AI, knowledge graphs could identify previously unnoticed patterns and relationships in the data, leading to a more comprehensive understanding of the factors contributing to accidents. The work of Shi et al (2024) [67] recently used ontologies for analyzing accidents in the construction industry where they used a domain word discovery algorithm to build a construction safety ontology and combined this ontology with a Text Convolutional Neural Network (TextCNN). Ontologies and knowledge graphs, while both crucial in the organization and representation of knowledge, have distinct differences and applications.…”

Section: Discussionmentioning

confidence: 99%

Explainable Artificial Intelligence to Support Work Safety in Forestry: Insights from Two Large Datasets, Open Challenges, and Future Work

Hoenigsberger,

Saranti,

Jalali

et al. 2024

Applied Sciences

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Forestry work, which is considered one of the most demanding and dangerous professions in the world, is claiming more and more lives. In a country as small as Austria, more than 50 forestry workers are killed in accidents every year, and the number is increasing rapidly. This serves as a catalyst for us to implement more stringent measures for workplace safety in order to achieve the sustainability objective of SDG 3, which focuses on health and well-being. This study contributes to the analysis of occupational accidents and focuses on two large real-world datasets from both the Austrian Federal Forests (ÖBf) and the Austrian Workers’ Compensation Board (AUVA). Decision trees, random forests, and fully connected neural networks are used for the analysis. By exploring different interpretation methods, this study sheds light on the decision-making processes ranging from basic association to causal inference and emphasizes the importance of causal inference in providing actionable insights for accident prevention. This paper contributes to the topic of explainable AI, specifically in its application to occupational safety in forestry. As a result, it introduces novel aspects to decision support systems in this application domain.

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Section: Discussionmentioning

confidence: 99%

Explainable Artificial Intelligence to Support Work Safety in Forestry: Insights from Two Large Datasets, Open Challenges, and Future Work

Hoenigsberger,

Saranti,

Jalali

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

Potato disease detection and prevention using multimodal AI and large language model

Zhu,

Shi,

Guo

et al. 2025

Computers and Electronics in Agriculture

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Adaptive information retrieval for enhanced building safety management leveraging BIM

Jiang,

Zhang,

Shi

et al. 2024

ECAM

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PurposeThis paper introduces a novel method to improve building safety management by leveraging building information modeling (BIM) and adaptive information retrieval techniques. The integration aims to overcome the limitations of traditional safety management methods in connecting construction processes with risk management efficiently.Design/methodology/approachThe proposed method involves developing industry foundation classes (IFC) ontologies and integrating them with a safety document ontology to form a comprehensive BIM-based safety context framework. Custom reasoning rules and an inference engine are constructed to enable automatic context-aware safety information retrieval. The methodology is demonstrated through an adaptive information retrieval system using job hazard analysis (JHA) documents.FindingsThe implementation of the BIM-based adaptive information retrieval system shows significant improvements in identifying and managing construction risks. By mapping job-specific risks to corresponding safety measures, the system enhances risk detection and management tailored to particular construction tasks. The results indicate a marked improvement in the precision and accuracy of safety assessments and recommendations, aligning them closely with planned construction activities and conditions.Originality/valueThis paper offers an innovative approach to construction safety management through the development of a BIM-facilitated context-aware information retrieval system. This approach provides a more intelligent and automated framework for identifying and managing risks in construction projects. By focusing on specific job steps and related risks, the system enhances the effectiveness and accuracy of safety measures, contributing to better overall building safety management.

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Ontology-based text convolution neural network (TextCNN) for prediction of construction accidents

Cited by 3 publications

References 61 publications

Explainable Artificial Intelligence to Support Work Safety in Forestry: Insights from Two Large Datasets, Open Challenges, and Future Work

Explainable Artificial Intelligence to Support Work Safety in Forestry: Insights from Two Large Datasets, Open Challenges, and Future Work

Potato disease detection and prevention using multimodal AI and large language model

Adaptive information retrieval for enhanced building safety management leveraging BIM

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