## 2 Literature Review
### Machine Learning Applications in Contingency Estimation

Table 1: ML algorithms and their theoretical underpinnings and strengths. More recently, [PERSON] et al. [5] employed Bayesian belief networks to estimate the probability of cost overruns, focusing on cost overrun-related risks. While a promising estimate can be provided by their Bayesian model, the extensive subjective input and requirement for prior probability data may constrain the model's applicability. [PERSON] et al. [10] compared ANNs with regression-based models for contingency estimation, focusing on contractors' approaches to managing cost-related risks. Their results indicate that ANNs provide superior predictive performance. However, the study primarily focused on the influence of steel reinforcement prices, reflecting only a narrow subset of the factors affecting cost contingency. Furthermore, the datasets used by [PERSON] et al. [5] and [PERSON] et al. [10] were limited, with only 12 and 30 projects, respectively. Similarly, [PERSON] et al. [11] developed a linear regression model to estimate contingency based on cost overrun risks, but this model also lacks diverse project features as input variables and depends heavily on subjective input data. In summary, despite the increasing interest in ML for cost contingency estimation, the existing studies are limited by their reliance on narrow project-specific characteristics, subjective risk inputs, and a restricted selection of ML algorithms, primarily ANNs and linear regression models [10; 11; 1]. Expanding the use of ML algorithms to include diverse project-specific characteristics and testing a broader range of algorithms could significantly advance the contingency estimation literature. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Research** & **Algorithm** & **Input Variables** & **Target** & **Advantages/Disadvantages** \\ \hline \multirow{8}{*}{[PERSON] et al. (2014) [1]} & \multirow{8}{*}{ANN} & \multirow{8}{*}{\begin{tabular}{c} Number of bidders, \\ project letting year, project \\ duration, and contract \\ value \\ \end{tabular} } & \multirow{8}{*}{Predicted contingency} & \multirow{8}{*}{\
\begin{tabular}{c} The ANN model \\ demonstrated promising \\ accuracy in predicting cost \\ contingency. However, the \\ input variables are limited, \\ and the lack of consideration \\ for project complexity \\ factors restricts the model’s \\ generalizability to \\ infrastructure projects. \\ \end{tabular} } \\ \hline [PERSON] et al. (2021) [5] & Fuzzy Bayesian belief networks &
\begin{tabular}{c} Cost overrun-related risks, \\ including 27 independent \\ risks and 14 dependent \\ risks \\ \end{tabular} & Probability of cost overrun & It predicted the probability of cost overrun. However, limited project cases (12 cases) used for prediction model, computational complexity, and reliance on subjective data constrain its application. \\ \hline \multirow{8}{*}{[PERSON] et al. (2022) [10]} & \multirow{8}{*}{ANN} & Sensitivity analysis ratio of steel reinforcement prices, ratio of the direct cost of steel reinforcement cost of steel reinforcement to the total project cost, probability distribution types for risk variables (triangular and normal), and contractor trend in dealing with risk (gambler, neutral, and conservative) & This study revealed promising predictive accuracy, however, the limited number of project cases (30 cases) and the focus on the influence of steel reinforcement prices on cost contingency, constrain its broader applicability. \\ \hline \multirow{8}{*}{[PERSON] et al. (2024) [11]} & \multirow{8}{*}{Linear regression} & Cost overrun-related factors, such as inaccurate cost estimate, design changes, scope changes, and variation orders & Predicted contingency amount & This study revealed promising predictive performance for linear regression. However, it lacks diverse project features as input variables and relies on subjective input data. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Previous studies of cost contingency prediction using machine learning.