A neural model for predicting the time performance of traditional general contract (TGC) project - Mohamad Zin, Rosli; Tan, Caren Cai Loon (Faculty of Civil Engineering, Universiti Teknologi Malaysia, 2008)

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Several studies had shown that many project managers are facing difficulties in predicting the time performance of Traditional General Contract (TGC) projects because there are many factors that affect TGC project success. This study presents the development of a model that can be used to predict the time performance of TGC project. Through literature research, fortyfour success factors affecting TGC project have been established. The degree of importance for these factors was determined through questionnaire survey. The outcome of the survey formed a basis for the development of the time performance prediction model using Artificial Neural Network technique. The best model was found to be a multi-layer back-propagation neural network consists of eight input nodes, five hidden nodes and three output nodes. The model was tested by using data from nine new projects. The results show that the mean error for this prediction model is relatively low. The developed model enables all parties involved in TGC projects to predict and ensure that their project is on time