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Machine Learning for the Improvement of Deep Renovation Building Projects Using As-Built BIM Models

Author

Listed:
  • Sofía Mulero-Palencia

    (CARTIF Technology Centre, Parque Tecnológico de Boecillo, 47151 Boecillo, Spain)

  • Sonia Álvarez-Díaz

    (CARTIF Technology Centre, Parque Tecnológico de Boecillo, 47151 Boecillo, Spain)

  • Manuel Andrés-Chicote

    (CARTIF Technology Centre, Parque Tecnológico de Boecillo, 47151 Boecillo, Spain)

Abstract

In recent years, new technologies, such as Artificial Intelligence, are emerging to improve decision making based on learning. Their use applied to the Architectural, Engineering and Construction (AEC) sector, together with the increased use of Building Information Modeling (BIM) methodology in all phases of a building’s life cycle, is opening up a wide range of opportunities in the sector. At the same time, the need to reduce CO 2 emissions in cities is focusing on the energy renovation of existing buildings, thus tackling one of the main causes of these emissions. This paper shows the potentials, constraints and viable solutions of the use of Machine Learning/Artificial Intelligence approaches at the design stage of deep renovation building projects using As-Built BIM models as input to improve the decision-making process towards the uptake of energy efficiency measures. First, existing databases on buildings pathologies have been studied. Second, a Machine Learning based algorithm has been designed as a prototype diagnosis tool. It determines the critical areas to be solved through deep renovation projects by analysing BIM data according to the Industry Foundation Classes (IFC4) standard and proposing the most convenient renovation alternative (based on a catalogue of Energy Conservation Measures). Finally, the proposed diagnosis tool has been applied to a reference test building for different locations. The comparison shows how significant differences appear in the results depending on the situation of the building and the regulatory requirements to which it must be subjected.

Suggested Citation

  • Sofía Mulero-Palencia & Sonia Álvarez-Díaz & Manuel Andrés-Chicote, 2021. "Machine Learning for the Improvement of Deep Renovation Building Projects Using As-Built BIM Models," Sustainability, MDPI, vol. 13(12), pages 1-29, June.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:12:p:6576-:d:571678
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    References listed on IDEAS

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    1. Yuting Qi & Queena Qian & Frits Meijer & Henk Visscher, 2020. "Causes of Quality Failures in Building Energy Renovation Projects of Northern China: A Review and Empirical Study," Energies, MDPI, vol. 13(10), pages 1-19, May.
    2. Yuting Qi & Queena K. Qian & Frits M. Meijer & Henk J. Visscher, 2019. "Identification of Quality Failures in Building Energy Renovation Projects in Northern China," Sustainability, MDPI, vol. 11(15), pages 1-23, August.
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    Cited by:

    1. Maria Kozlovska & Stefan Petkanic & Frantisek Vranay & Dominik Vranay, 2023. "Enhancing Energy Efficiency and Building Performance through BEMS-BIM Integration," Energies, MDPI, vol. 16(17), pages 1-23, August.

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