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.
Highlights
As one of the key Information Technology (IT) developments in the construction industry, Building Information Modelling (BIM) has been gaining immense growth in its applications, due to its advantages in improving construction efficiency and minimising design error (Keskin et al, 2019)
Ex-post evaluation of BIM implementation has been frequently reported via case studies, for example in Ham et al (2018) and Manning et al (2008) Ex-ante evaluation plays a critical role in project initiation and project success evaluation (European Commission 2001), yet has not been adopted for evaluation of BIM implementation
An ex-ante evaluation method was proposed in this study to assist organisations in estimating the costs and benefits of implementing BIM at different Level of Development (LOD)
Summary
As one of the key Information Technology (IT) developments in the construction industry, Building Information Modelling (BIM) has been gaining immense growth in its applications, due to its advantages in improving construction efficiency and minimising design error (Keskin et al, 2019). This study aims to propose an ex-ante evaluation method for decisionmakers to assess the level at which BIM should be implemented, subject to the utilisation of specific BIM applications. Through using ANNs as a prediction tool, a generic approach is derived to conduct cost-benefit analysis for BIM implementation at organisations. Ii) Dollar value of benefits and costs could vary significantly across different sized projects undertaken by an organization The latter issue is well demonstrated in the three case studies on BIM implementation, with different levels of organisation size and project characteristic, reported by Giel et al (2013) which showed a wide range of ROI values, ranging between 16% and 1654%.
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