## 2 Literature Review
### RCF and RBF Neural Network Applications  
In the context of investment decisions, the accuracy of forecasts of the cost and schedule of a project is of great importance. In construction projects, techniques such as BIM (Building Information Modeling) and RCF (Reference Class Forecasting) are often employed in order to forecast project costs and schedules [38]. However, it should be noted that BIM can forecast project costs and schedules [39], but that it relies on the subjective judgment of the project manager, which can lead to cognitive bias. Consequently, [PERSON] [38] uses the external perspective--also known as the RBF technique--which was demonstrated to possess a high degree of accuracy in the forecasted results of project costs. [PERSON] [40] provides an illustrative example of RCF in practice; that approach was utilized to forecast the costs associated with the Edinburgh Tram. [PERSON] and [PERSON] [41] conducted an empirical evaluation of the method by applying it to a dataset comprising twenty-four real-world projects originating from the construction industry. Their findings indicated that the RCF technique is more accurate than traditional forecasting methods and provides more precise forecasts. Consequently, RCF techniques are now being employed in a multitude of contexts, including planning, project management, cost estimation, and strategic management [42].  
RBF (Radial Basis Function) neural network is a frequently used model in artificial neural networks [43]. [PERSON] and [PERSON] [44] were the first to try to introduce the principle of RBF function in the design of artificial neural networks, which eventually constituted the RBF neural network. A RBF neural network is composed of an input layer, a hidden layer, and an output layer. The activation function of the hidden layer uses the radial basis function, which weights the input by radial symmetry with the center in the input space to obtain the output of the hidden layer neurons [45]. The radial basis function is known for its strong nonlinear fitting ability and its ability to capture complex nonlinear relationships. As a result, the RBF neural network is highly adaptable when it comes to dealing with nonlinear problems. Although Monte Carlo simulation and fuzzy logic also offer significant advantages when dealing with complex and uncertain problems, they each have some drawbacks. Monte Carlo simulation requires a large number of random samples for simulation and is computationally slow [35]. Fuzzy logic, on the other hand, lacks adaptive and self-learning capabilities, which means that they may not adapt well to environmental changes or the emergence of new data [36]. Meanwhile, RBF neural networks are particularly effective when trained on small sample datasets, as they can achieve better generalization performance with less data. In the event that the quantity of data are considerable in size or the data characteristics are more intricate and diverse, the RBF model may be more susceptible to overfitting as a consequence of the complexity of the data distribution. Additionally, its strong generalization ability means that the performance on the training set tends to translate well to the testing set, allowing for more accurate forecasted results when dealing with unknown data. There have been many scholars applying RBF in different fields, such as construction estimation, schedule forecast, and the daily schedule completion rate of construction projects [46]. For example, [PERSON] et al. [47] used neural networks to help tunnel engineers estimate the productivity of the next cycle, thus improving productivity. [PERSON] and [PERSON] [48] identified the project type, project geographic location, and duration as the key influencing factors and used neural networks to forecast the project overhead costs.