Predicting project success in construction using an evolutionary Gaussian process inference model

Abstract

There are many factors that affect the success of the implementation process of a project. The importance of each of these factors varies according to the different phases of the project lifecycle, which makes it very difficult to predict the final result of a project. In practice, foreseeing the result of a project is based on the judgment of those in management, which is grounded in their experience. This study aimed to build an Evolutionary Gaussian Process Inference Model (EGPIM), using a Gaussian process, along with Bayesian inference and particle swarm optimization, which helps to optimize the hyper-parameters required for making Gaussian process predictions. With this model at its core, this study can efficiently extract expert knowledge and experience from case studies and historical data to determine relationships between factors which significantly influence the outcome of a project so that its success may be predicted. Historical cases were ordered as a time series based on the Continuous Assessment of Project Performance (CAPP) research results. The model was trained using the EGPIM and these cases to predict the success of a project. This model proved quite accurate at predicting the success of a project and had outstanding performance in time-series applications.