Green BIM Adoption Framework for Existing Buildings

Green BIM is a standardized approach and an integrated process where BIM design software and BIM-based sustainability software are used to perform comprehensive sustainability analysis of buildings, through the use of enriched building data to optimize building performances [p.25]. It is a digital model-based approach that involves generating and managing coordinated and consistent building data over the lifecycle to accomplish desired sustainability goals. Green BIM integrates sustainable design principles with BIM tools to achieve improved building performances and environmental impacts. The concept of Green BIM is based primarily on the convergence of sustainable buildings and BIM, focusing on integrated design processes, environmentally sustainable design principles, and optimization of green building certification credits.

While enriched BIM data is naturally generated to a good extent in the design and construction process of a new building, it was observed that existing data poses a challenge for the implementation of Green BIM in existing buildings. With the aim of overcoming this challenge, a study focusing on the adoption of Green Building Information Modelling (BIM) for existing buildings was carried out. This is a synopsis of an article published in the journal Intelligent Buildings International based on this study.

The research aims to identify the challenges of generating BIM data from existing building information and to recognize the solutions to overcome them. It also compares the different challenges encountered when applying Green BIM in existing contexts, particularly in buildings that were not constructed using BIM during the design and construction stages. The study provides insights into the practical challenges that arise in these existing conditions and offers a conceptual framework for implementing Green BIM techniques in existing buildings.

A multiple case study involving two existing buildings was conducted to achieve the study aim. The first case is a 13-year-old building, and the second case is 2 years old. Both buildings are three-story educational buildings within a university. The purpose of the case studies was to identify the challenges of implementing Green BIM for existing buildings. The methodology involved the practical implementation of Green BIM techniques for the selected cases. The analysis, comparing one building with new conditions and the other with old conditions, enabled the identification of different challenges when applying Green BIM to existing buildings. The study utilized Autodesk Revit as the modelling tool and Green Building Studio (GBS) for simulations.

The study revealed several challenges for Green BIM for existing buildings, such as errors in drawings, complexity, and excessive time consumption for modelling. It also identified potential solutions to overcome these challenges. Consequently, a framework for the successful implementation of Green BIM in existing buildings was developed based on these research findings. This framework benefits existing buildings in Sri Lanka by providing a structured approach to address the practical challenges of implementing Green BIM in the context of existing buildings.

The framework offers potential solutions to overcome identified challenges. By addressing these challenges, the framework aims to facilitate the successful adoption of Green BIM technology, leading to improved sustainability, energy efficiency, and environmental performance of existing buildings in Sri Lanka. Additionally, the framework serves as a guide for researchers and industry practitioners interested in the development of Green BIM for existing building contexts, contributing to the advancement of sustainable building practices in the country.

Citation to Original Article

Rathnasiri, P., & Jayasena, S. (2022). Green building information modelling technology adoption for existing buildings in Sri Lanka. Facilities management perspective. Intelligent Buildings International, 14(1), 23–44. https://doi.org/10.1080/17508975.2019.1632782

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