Predicting construction cost and schedule success using artificial neural networks ensemble and support vector machines classification models

It is commonly perceived that how well the planning is performed during the early stage will have significant impact on final project outcome. This paper outlines the development of artificial neural networks ensemble and support vector machines classification models to predict project cost and schedule success, using status of early planning as the model inputs. Through industry survey, early planning and project performance information from a total of 92 building projects is collected. The results show that early planning status can be effectively used to predict project success and the proposed artificial intelligence models produce satisfactory prediction results.

► Project success is predicted using AI techniques (ANNs-ensemble and SVMs).
► Status of early planning is model input and project success is model output.
► Data form 92 building construction projects are used to develop and test models.
► Better early planning leads to project cost and schedule success.
► AI techniques are applicable for non-linear data in this case.