disasters, including accidents, incidents, and near-misses, have been documented in safety reports using unstructured or semi-structured free-text formats. These reports encompass event descriptions, timing information, and location details [11; 12]. However, analyzing accident report data is often laborious and time-consuming, demanding profound understanding of safety to extract meaningful insights [11; 13]. The conventional approach involves the manual classification of accident cases, typically undertaken by safety professionals [11; 14]. This method requires a meticulous review of detailed textual accident reports to categorize accidents based on various accident-related attributes. Furthermore, beyond consuming substantial resources, manual classification is susceptible to human bias and errors, potentially leading to incomplete or inaccurate analyses. The advancements in artificial intelligence (AI) now allow for the automated processing, organization, and handling of free-text data, streamlining this analytical process [15]. Specifically, text rule-based, machine, and deep learning approaches have been showing potential in predicting anticipated danger and enhancing the understanding of accident causation. For instance, [PERSON] et al. [16] developed models aimed at classifying 11 causes of accidents, such as instances of being caught-in-between objects, the collapse of objects, electrocution, exposure to chemical substances, among others, utilizing accident report data and machine learning algorithms. The results of their study demonstrated the average F1 score for 11 causes of accidents was 0.68, showcasing the effectiveness of their proposed ensemble model with optimized weights. Separately, [PERSON] et al. [17] developed machine learning models (e.g., random forest, extreme gradient boosting, and linear support vector machine) to predict four safety outcomes, such as injury severity, injury type, the body part impacted, and accident type. The results showed 84.84% in F1 scores for severity prediction. These studies underscore the significance of leveraging advanced modeling techniques to gain insights into the diverse factors contributing to accidents and improve safety measures. Nevertheless, previous research in this domain has primarily concentrated on the development of traditional machine and deep learning methods. These methods involve the manual extraction of text features, which are subsequently fed into a classifier [11]. These approaches contain several inherent limitations such as limited scalability/generalization, difficulty in analyzing free-from text, high dimensionality, and limited adaptability to new tasks. Although traditional machine/deep learning-based approaches have made significant strides in the natural language processing (NLP) of the construction domain, such limitations must be mitigated by incorporating more generative and powerful AI models [18]. Recently, large language models (LLMs), particularly those built on the transformer architecture, such as the generative pre-trained transformer (GPT), present notable advantages for classification tasks compared to traditional machine learning and deep learning models [19; 20]. The transformer's ability to capture contextual information, employ end-to-end learning without manual feature engineering, utilize attention mechanisms for distinct understanding, and leverage transfer learning for improved performance make it well-suited for tasks that require a comprehension of accident-related text data [21]. Additionally, its adaptability to varied input lengths, multimodal capabilities, and efficient parallelization contribute to its efficacy in handling the complexities of accident classification, marking a significant advancement over more traditional approaches in the field [22]. Despite these potential benefits of LLM, leveraging GPT models is still an explanatory stage in the construction accident domain.