Abstract

The construction industry suffers from workplace accidents, including injuries and fatalities, which represent a significant economic and social burden for employers, workers, and society as a whole.The existing research on construction accidents heavily relies on expert evaluations,which often suffer from issues such as low efficiency, insufficient intelligence, and subjectivity.However, expert opinions provided in construction accident reports offer a valuable source of knowledge that can be extracted and utilized to enhance safety management.Today this valuable resource can be mined as the advent of artificial intelligence has opened up significant opportunities to advance construction site safety. Ontology represents an attractive representation scheme.Though ontology has been used in construction safety to solve the problem of information heterogeneity using formal conceptual specifications, the establishment and development of ontologies that utilize construction accident reports are currently in an early stage of development and require further improvements. Moreover, research on the exploration of incorporating deep learning methodologies into construction safety ontologies for predicting construction incidents is relatively limited.This paper describes a novel approach to improving the performance of accident prediction models by incorporating ontology into a deep learning model.A domain word discovery algorithm, based on mutual information and adjacency entropy, is used to analyze the causes of accidents mentioned in construction reports. This analysis is then combined with technical specifications and the literature in the field of construction safety to build an ontology encompassing unsafe factors related to construction accidents.By employing TransH model, the reports are transformed into conceptual vectors using the constructed ontology. Building on this foundation, we propose a TextCNN model that incorporates the ontology specifically designed for construction accidents. We compared the performance of the model against five traditional machine learning models, namely Naive Bayes, support vector machine, logistic regression,random forest, and multilayer perceptron, using three different data sets:One-Hot encoding, word vector, and conceptual vectors. The results indicate that the TextCNN model integrated with the ontology outperformed the other models in terms of performance achieving an impressive accuracy rate of 88% and AUC value of 0.92.

Citations

Authors (6)