# Artificial Neural Network Model to Predict Final Construction Contract Duration
## 2 Literature Review
### Regression and ANN Models  
Several studies utilized regression and ANN models to estimate the _FCCD_. For example, [PERSON] and [PERSON] [23] developed a regression model based on cross-validation. The model parameters were project type, sector, contractor selection, and the 93 Australian building project model. [PERSON] and [PERSON] [24] developed a regression model to predict the building project duration based on 51 historical data. The model parameters were the area of the building, estimated duration, and estimated cost. The model did not consider the project sectors, and the model cannot be used for different types of projects, such as electrical or mechanical projects. The artificial neural network method proved more advanced and performed better than the regression model [25]. The ANN model developed by [26] was to forecast the duration of building projects. The input data included the number of floors, foundation type, activities, contractor class and client class, and floor area. The mean absolute error was 25.9%. Moreover, [PERSON] et al. [27] established an ANN model for predicting the building project. The parameters were the type of clients, construction quality, project location, the total height of the project, client coordination with contractor staff, contract type (unit price contract/lump sum), contractor selection method, and quality of project documentation. The maximum error of the model was 20%.  
The previous models required specific information, which varies from one project to another, such as the model developed by [PERSON] et al. [14], [PERSON] [2], [PERSON] et al. [15], or contract data that should be available through the construction stage, such as [PERSON] et al. [23]. Although the CBR method has proven its effectiveness in predicting the duration of the contract, it requires the availability of a previously completedproject similar to the one required in terms of characteristics and operational conditions, which may be difficult to provide. In terms of the regression and ANN models, the above model was utilized for building project duration and cannot be generalized to other projects. Therefore, there is a need to develop a predictive model using the ANN model, which is used for different projects and is based on common and available data. This paper contributed to the development of a model to predict _FCCD_ in the early stages based on a few common node criteria (_CC_, _CD_, and sector). Those parameters are characterized as being few and common for any contract. However, the ANN models need extensive data to provide a reliable and adequate forecast model. In this paper, the available data were relatively small (135), making it challenging to deal with them using an ANN model. To address the issue, the relatively small data issue was solved using the two methods that regard standardization (Zavadskas and Turskis' logarithmic) and augmentation data by utilizing the method introduced by [28], which was then utilized in developing the ANN model. The input data tested consists of contract cost, contract duration, and project sectors. This study presented a reasonable-accuracy prediction model based on KSA project data. The analysis approach used to create the model can also be applied to projects from other areas of the globe.