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 1 publication

(1 citation statement)

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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%