## 5 Discussion  
Cost contingency is a financial buffer that is allocated to address unforeseen expenses in a project and plays a pivotal role in risk management. The accurate estimation of cost contingency early in a project is essential for effective cost control and overall project success. Despite its importance, little research has explored the relationship between infrastructure-specific project characteristics and cost contingencies. Furthermore, studies that employ and compare multiple ML algorithms for contingency predictions using real-life datasets are sparse, and even fewer attempt interval estimates. Moreover, the influence of project complexity--a key determinant of cost performance--remains underexplored. This study seeks to close these research gaps by investigating how project characteristics and complexity contribute to both point and interval contingency estimates by leveraging multiple ML algorithms to improve prediction accuracy and reliability. For point estimations, ANNs demonstrated the strongest predictive performance of the seven ML algorithms evaluated. An ANN's ability to model complex, non-linear relationships likely accounts for its superior ability to capture the intricate associations between project characteristics and cost contingency, particularly in complex infrastructure projects. These findings align with the existing research on the topic. For example, [PERSON] (2016) reported that ANNs outperformed linear regression and SVM in predicting costs for infrastructure projects, especially highway projects. Similarly, [PERSON] et al. (2016) found that ANNs surpassed four other ML algorithms (linear regression, SVM, decision trees, and random forests) in predictive accuracy. In this study, XGBoost ranked second in predictive performance. [PERSON] (2016) previously concluded that XGBoost generally exhibits superior accuracy compared to other commonly used ML algorithms. These results suggest that both ANNs and XGBoost are well-suited for modeling contingency predictions and represent robust and reliable tools for academics and professionals addressing cost uncertainties in infrastructure projects. Furthermore, a complexity index was introduced and integrated into both the point and interval contingency estimations. The complexity index captured key project factors to approximate project complexity through cost and duration. This method balances the trade-off between data availability and the need to encapsulate essential attributes of complexity, as the significance of project complexity in cost estimation has been emphasized in prior studies. [PERSON] et al. (2016) demonstrated that increased complexity in supply chain decision-making reduces cost estimation accuracy. Similarly, [PERSON] et al. (2018) underscored the impact of project complexity on infrastructure cost estimations. In this study, integrating the complexity index significantly improved the ANN's predictive performance, emphasizing the importance of incorporating a measure of complexity to improve infrastructure project cost estimation accuracy ([PERSON] et al., 2016; [PERSON] _et al._, 2018). Feature importance analysis confirmed that the complexity index was a key variable affecting cost, ranking it as the second most influential factor and validating its importance for model robustness and predictive reliability. This study also explored interval estimates for contingency predictions through two techniques: Monte Carlo dropout and bootstrapping. Both approaches offered the ability to operate without prior knowledge of the predictive distribution [38] and exhibited promising predictive performance. The results of these tests revealed an intriguing pattern. While bootstrapping offered superior overall accuracy, Monte Carlo dropout provided narrower prediction intervals, delivering a higher degree of precision in the resulting estimations. [PERSON] et al.