Forecasting the net costs to organisations of building information modelling (BIM) implementation at different levels of development (LOD)

Abstract

Numerous frameworks and tools have been proposed in the literature to assess the performance of BIM implementation in the Architecture, Engineering and Construction (AEC). However, there is yet a lack of ex-ante evaluation methods that forecast BIM implementation costs. This study aims to propose an ex-ante evaluation method to forecast the net costs of BIM implementation at different Level of Development (LOD). The proposed method is expected to assist decision makers to find the most cost-saving LOD when investing resources for implementing BIM, from an organisational perspective. The proposed method relies on an Artificial Neural Network (ANN) for each type of implementation costs and benefits. The findings suggest that decision makers need to evaluate an organisation's competency and their implemented BIM applications when choosing the BIM implementation level of BIM. Furthermore, the results show that a higher BIM implementation level does not often secure more benefits. Over 30 features were included in the ANNs with results indicating the possibility of expanding the feature set to obtain more accurate results.