## 1 Introduction  
Uncertainty is inherent to infrastructure projects, arising from factors such as changing site conditions, fluctuating material costs, and unforeseen technical challenges [1, 2]. To manage these uncertainties, contingency funds are typically allocated [3]. Traditional methods of contingency estimation often rely on subjective judgment, such as expert interviews or surveys, or simulation techniques, such as Monte Carlo simulations [3, 4, 5]. However, reliance on the above subjective judgments is often criticized for its inconsistencies and potential bias [6]. Similarly, simulation-based methods depend on underlying assumptions that may not fully capture the complexity of real-world projects. Infrastructure projects, in particular, entail elevated levels of risk and unpredictability due to their large scale, complexity, and extended timelines [7, 8]. These characteristics make determining an appropriate contingency allocation especially challenging. Therefore, the development of predictive methods capable of addressing the uncertainty and complexity of infrastructure projects is essential. Conversely, ML algorithms offer a promising alternative by leveraging data to identify complex, non-linear relations among project variables [9]. ML enables practitioners to anticipate and quantify potential uncertainties more effectively. Research by [PERSON] et al. [10] demonstrated that ML models outperformed traditional estimation techniques, such as regression-based models, in terms of accuracy. ML also offers a more reliable approach to accounting for uncertainties and improves financial planning in construction projects. Several studies have explored the potential of using ML to estimate cost contingencies [1; 11]. However, previous studies have predominantly utilized ANNs or linear regression models and often focused on project risk factors rather than the distinct characteristics of individual projects [5]. This highlights a research gap in evaluating diverse ML models to identify those offering superior predictive performance. Incorporating project-specific attributes, such as scale and project duration, would allow these models to better capture the inherent uncertainties that should be accommodated in contingency estimates. In addition to technical challenges, project complexity plays a critical role in cost outcomes and affects the accuracy of cost estimation [12; 13; 14]. The evidence suggests that complexity level has a substantial effect on cost performance [15]. Moreover, prior research has also found that complexity affects the accuracy of cost estimation [16]. Thus, a measure of project complexity is necessary to accurately reflect a project's multifaceted nature when applying ML algorithms to infrastructure projects. A complexity index, which integrates factors such as project scale and capital investment, offers a more comprehensive measure of project risk and the potential of unforeseen challenges during project execution [17]. However, relatively few studies have incorporated a complexity index to capture the multifaceted nature of infrastructure project environments [1]. This study attempts to integrate a complexity index to improve the accuracy and robustness of cost contingency predictions. This approach is particularly valuable in contexts of significant project complexity, such as large-scale infrastructure projects. Moreover, human cognitive biases also affect the confidence of project cost estimation. A natural aversion to uncertainty often makes project planners reluctant to fully trust point estimates, as project outcomes are inherently unpredictable [18]. Furthermore, reliance on a single-value prediction that does not account for a range of potential outcomes can result in underestimating risks [19]. Formatting cost estimates as interval ranges rather than fixed points mitigates these issues by explicitly acknowledging uncertainty. Confidence intervals, grounded in statistical sampling principles, provide a robust framework for capturing and communicating uncertainty [20].