## 4 Results
### Baseline Models  
To evaluate the performance of the developed 2.0 and 3.0 fine-tuned GPT-based method, this study selected three base classifiers, namely TF-IDF, CNN, and BERT, for comparison based on the literature [6, 11, 14]. The baseline models play an important role in classification research as they provide essential benchmarks for evaluating the performance of more complex or new models. By implementing existing algorithms or traditional AI approaches, baseline models set a standard against which the effectiveness of novel methods can be measured. The baseline models serve as reference points for assessing improvements and offer insights into the challenges of a specific task. The model's performance underwent assessment through a designated test set. To ensure robust evaluation, distinct training and testing splits were formulated for outcome categories. 90% of the reports were allocated for training purposes, while the remaining 10% were reserved \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Variable** & **Type** & **Feature** \\ \hline Output & Event type & Categorical & Caught-in-between, Cut, Falls, Struck-by, \\ Input & & (6 events) & Trips, Others. \\ (Unstructured) & Narrative details of accidents & Text & Unstructured text data \\  & & (Accident details) & \\ \hline  & Date & Categorical & \\  & Date & (4 seasons) & Spring, Summer, Fall, Winter \\  & Time & Categorical & Dawn, Morning, Daytime, \\  & & (5 windows) & Afternoon, Night \\  & Weather & Categorical & Sunny, Snowy, Rainy, Windy, \\  & Temperature & Numerical & \(\ldots\), \(-3\,^{\circ}\)C, \(-2\,^{\circ}\)C, \(-1\,^{\circ}\)C, \(0\,^{\circ}\)C, \(1\,^{\circ}\)C, \\  & & (Integer) & \(2\,^{\circ}\)C, \(3\,^{\circ}\)C, \(\ldots\) \\ \hline Input (Structured) & Humidity & \
\begin{tabular}{c} Numerical \\ (Percentage in natural \\ number) \\ \end{tabular} & 0\%, 1\%, 2\%, 3\%, \(\ldots\) \\  & Type of construction & Categorical & File drive, Building blocks, \\  & Method of construction & Categorical & Formwork installation, \(\ldots\) \\  & & & Firewall installation, Doka form \\  & & & installation, Gang form dismantling, \(\ldots\) \\  & & & Republic of Korea, Malaysia, USA, \\  & & & Vietnam, \(\ldots\) \\  & Age & Numerical & 20, 21, 22, \(\ldots\), 79 \\  & & & \(0\)–9\%, \(10\)–\(19\)\%, \(20\)–\(29\)\%, \\  & Work progress &
\begin{tabular}{c} Categorical \\ (Ranges of percentage) \\ \end{tabular} & 0–9\%, \(10\)–\(19\)\%, \(20\)–\(29\)\%, \\  & & & \(\ldots\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Variables and features from construction accident reports. Figure 4: A sample text to illustrate the unstructured data (stop words highlighted in red color). for testing the model's predictive capabilities [44; 45]. Additionally, for the optimization of parameters in the GPT model, a validation set was meticulously generated for each outcome. This involved randomly isolating 10% of the training set, providing a dedicated dataset for fine-tuning and enhancing the model's precision in predicting specific accident types. This allocation, termed a validation set, plays a crucial role in refining model performance by facilitating hyperparameter tuning and mitigating overfitting risks [46; 47]. 1. TF-IDF, is a statistical measure that indicates how important a word is within a specific document in a collection of documents [48]. Typically used in information retrieval and text mining [49], TF-IDF provides weightings but does not involve learning on its own. However, it can be integrated with machine learning techniques and has surprisingly demonstrated strong performance in prior research, earning its selection as a benchmark model. Both the stochastic gradient descent (SGD) classifier and support vector machine (SVM) classifier were trained using the weights obtained from the TF-IDF vectorizer, and their accuracy was measured. In the training of the SGD classifier, the performance of four different kernels (radial basis function, linear, poly, sigmoid) was compared.