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The dataset exhibited a significant class imbalance, with fatalities (SOI Level 5) accounting for only 11% of the total incidents. The distribution (Fig. 1) highlights that most incidents fall into moderate to severe categories (SOI Levels 2–4), while fatal incidents (SOI Level 5) represent a smaller yet critical portion of the dataset (11%). The table summarizes the mean, standard deviation, minimum, and maximum values, which help readers understand the distribution and scale of the encoded variables used in model training. To provide a clear understanding of the modelling dataset after encoding (Sect. “Encoding categorical data”), Table 3 presents the descriptive statistics of the 14 explanatory variables and the target (SOI). These exclusions were necessary to ensure data quality and reliability, leaving 203 incidents as the final dataset used for model development and analysis. Section “Results and discussion” presents the results and discussion, covering model validation, performance evaluation, feature importance analysis, and SHAP-based interpretability.
However, the regularization parameter (C) and kernel parameter (gamma) were kept at default values due to the study’s focus on comparative evaluation rather than exhaustive hyperparameter tuning. For this study, the RBF kernel (Radial Basis Function) was used, as it is well-suited for datasets with non-linear relationships. XGB and RF were chosen for their ability to handle non-linear relationships and feature interactions, which are critical in analysing the complex factors contributing to safety incidents. Such imbalance can bias ML models toward majority classes, reducing their ability to predict minority outcomes like fatalities.
- In this context, SVMs establish a hyperplane, a linear decision boundary in lower dimensions or a higher-dimensional analogue, to optimally differentiate between two distinct classes within a dataset37.
- AI makes schedules more predictable by identifying where time will be lost before it lands on the critical path.
- Additionally, the “Date of Incident” revealed temporal patterns, with certain months exhibiting higher risks, providing opportunities for seasonal safety planning.
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- Based on the above research gaps, there is a clear need for approaches that can simultaneously address multiple construction safety outcomes while ensuring interpretability for practical decision-making.
- GB followed closely with a Micro-Average AUC of 0.86, reinforcing the effectiveness of boosting-based techniques in handling complex classification tasks.
Dataset overview and characteristics
Collectively, the feature importance patterns explain the marked improvement in XGB performance when NOI is incorporated, as it captures critical distinctions among incident types that strongly determine severity. Boosting algorithms, for example, iteratively refine the model by focusing on hard-to-classify instances, ensuring improved performance on imbalanced and noisy datasets. To provide a more thorough https://greenhousebali.com/construction-chemistry-materials-and-their.html evaluation, Micro-Average AUC scores were used to compare models, as they aggregate performance across all severity levels. Table 7, which focuses exclusively on SOI Level 5 (fatal incidents), provides deeper insights into the models’ ability to predict the most critical severity class. To ensure a more balanced evaluation, precision, recall, and F1-score were also calculated to provide deeper insight into model performance for SOI (including NOI). In this study, SHAP was used to quantify the relative influence of each input on predicted outcomes and to summarize feature contributions across the dataset, supporting transparent interpretation of the learned models.
- It then flags the patterns that tend to precede project delays and cost overruns.
- The effectiveness of KNN hinges on selecting the appropriate values for K and the distance measure.
- The study categorizes safety incidents according to the Occupational Injury and Illness Classification System (OIICS)22.
- AI is already showing up across a broad range of construction projects.
Artificial Intelligence for the Built Environment
Models like RF and XGB aggregate multiple DTs, each specializing in different regions of the feature space, allowing them to handle complex decision boundaries effectively. The dataset contains non-linear relationships and interactions between explanatory variables (e.g., the combined effect of PPE usage, safety training, and incident location), which are better captured by ensemble methods. These models showed strong predictive capability for high-severity incidents (Severity Level 5), minimizing errors in distinguishing critical safety events.
At the same time, the evidence should be interpreted considering the sample size and regional scope, and would benefit from external validation on larger, multi-region datasets curated under common taxonomies. Future work should explore region-specific variations in accident patterns to improve model applicability in diverse construction environments. Overall, SHAP analysis strengthens the interpretability of the model, providing construction managers and safety officers with clear guidance for implementing effective safety measures.
- This is where the impact compounds, because it’s the work that normally stays hidden until it becomes expensive.
- This method assigns a unique numerical value to each categorical class, allowing ML models to process the data efficiently without increasing dataset dimensionality.
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- However, 47 incidents were excluded due to missing or incomplete data in critical explanatory variables, such as time with employer, time of incident, and specific PPE types.
- One of the truly amazing things about machine learning in construction is that it can look at terabytes of data and figure out project risks before they happen.
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