## 4 Model Validation  
### Sample Data for Projects  
In this section, we validate the accuracy of the proposed model using historical project datasets derived from the Batselier and Vanhoucke and Martens and Vanhoucke databases. These datasets can be accessed from the official website of the Operations Research and Scheduling (OR&S) research group at the Faculty of Economics and Business Administration of Ghent University ([[https://www.projectmanagement.ugent.be](https://www.projectmanagement.ugent.be)]([https://www.projectmanagement.ugent.be](https://www.projectmanagement.ugent.be)), accessed on 13 August 2024). The collection of project data started in 2008 and encompasses five major industries: construction, IT, education, production, and finance, and the dataset has been updated to 181 empirical projects in 2023 (Table 4). The dataset contains baseline scheduling data, risk analysis data, and project control data. These projects came from a variety of countries and regions, with the most expensive project costing $4,999,958,016 and the least expensive costing only $1210. The duration of the projects ranged from 7 to 1796 days. Information about the 181 projects is detailed in Table 3, which contains information about the project type, the planned duration (PD) of the project, the budget at completion (BAC) of the project, as well as the real cost (RC), and the real duration (RD) of the project. Meanwhile, this dataset is frequently adopted by scholars to perform research, such as when [PERSON] et al. (2016) used the dataset to construct schedules, analyzeschedule risks, and perform project control [50]; and [PERSON] et al. (2018) again used these datasets to conduct a study of scheduling for resource-constrained projects [51].  
Instead, this paper focuses on 122 of these construction projects because construction projects are very complex and often carried out in uncontrolled environments where even small disturbances can magnify their negative impacts.  
To aid the study, 64 projects are chosen from a portfolio of 122 based on RCF technique and two criteria. First, the project has a complete track record that provided all necessary schedule and cost baselines and project control information. Second, after corrective action was taken, the project bounced back to its original stabilized level or bounced back to a higher level of performance than in the period prior to the disruption. From Table 3, it is clear that out of the 64 items selected, the maximum value of BAC is 62,385,600 and the minimum value is 22,704. Meanwhile, the range of PD is from 50 days to 850 days. The RC takes the range of 25,313 to 655,269,930. And the minimum value of RD is 79 days, and the maximum value is 935 days.  
However, due to the limited dataset of the selected project, we expanded the data in order to ensure the accuracy of the experiment. In other words, the number of dataset samples was increased from 64 to 200 through the utilization of interpolation, a process inherent to the RBF neural network. With regard to the expanded sample of 200, we constructed the model by choosing 95% of them as the training set and 5% as the testing set for experimentation.