Enhancing Construction Site Safety: Natural Language Processing for Hazards Identification and Prevention

Abstract

Construction sites are well known for the inherent risks that negatively impact the safety and well-being of workers. Identifying and minimising these hazards is critical for preventing accidents and creating a safe working environment. Traditional techniques of hazards identification in construction rely on visual assessments and professional expertise, which can be time-consuming and subjective. The goal of this research is to identify traits that indicate potential dangers in the construction industry by extracting meaningful information from accident narratives. This will be achieved through the application of a rule-based iteration approach, using the Natural Language Toolkit (NLTK) for keyword extraction and text tokenization. It is a branch of artificial intelligence and computational linguistics concerned with the interaction of computers and human language. The research methodology involves the utilization of NLTK and the application of a rule-based iteration approach to extract hazards from construction-related accident narratives. The proposed approach includes gathering accident narratives, pre-processing data, and textual analysis with NLP tool for information extraction and training the algorithm with identified attributes. The textual analysis eventually leads to the extraction of significant sources of dangers that cause accidents. The study contributes to the developing subject of construction safety management by utilizing the capabilities of NLP to enhance hazard detection, resulting in safer construction practices and lower occupational hazards. The findings emphasise the accuracy with which NLP approaches detect dangers, allowing construction professionals to proactively decrease risks and enhance overall safety on construction sites.

Keywords

• keyword extraction
• nlp
• risk
• safety
• text mining