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Building information modeling (BIM) is an emerging technique in the construction industry. It is regarded as an effective approach for green building development; however, its effectiveness has not been sufficiently investigated from a lifecycle perspective. To bridge this research gap, this study investigates BIM application value in different phases of a green building through a convolutional neural network (CNN) method. To begin with, an assessment framework was developed with the consideration of balancing the estimation accuracy and the data size. Then, the validity of the developed model was verified from both theoretical and practical perspectives. Finally, the effectiveness of BIM was tested using the proposed framework. Results showed that the overall score of the tested project was four in the five-point Likert scale, with an average relative error less than 1%. From a value-based perspective, it is revealed that the application value of BIM represented a descending order throughout the lifecycle of the tested project. In addition, it is found that the functional value obtained the highest score, whereas social value was at the bottom. The findings of this study can help decision makers to detect the weaknesses of BIM implementation during green building development.

Assessing the Effectiveness of Building Information Modeling in Developing Green Buildings from a Lifecycle Perspective