3D characterization of a Boston Ivy double-skin green building facade using a LiDAR system

Abstract

On the way to more sustainable and resilient urban environments, the incorporation of urban green infrastructure (UGI) systems, such as green roofs and vertical greening systems, must be encouraged. Unfortunately, given their variable nature, these nature-based systems are difficult to geometrically characterize, and therefore there is a lack of 3D objects that adequately reflect their geometry and analytical properties to be used in design processes based on Building Information Modelling (BIM) technologies. This fact can be a disadvantage, during the building's design phase, of UGIs over traditional grey solutions. Areas of knowledge such as precision agriculture, have developed technologies and methodologies that characterize the geometry of vegetation using point cloud capture. The main aim of this research was to create the 3D characterization of an experimental double-skin green facade, using LiDAR technologies. From the results it could be confirmed that the methodology used was precise and robust, enabling the 3D reconstruction of the green facade's outer envelope. Detailed results were that foliage volume differences in height were linked to plant growth, whereas differences in the horizontal distribution of greenery were related to the influence of the local microclimate and specific plant diseases on the south orientation. From this research, along with complementary previous research, it could be concluded that, generally speaking, with vegetation volumes of 0.2 m3/m2, using Boston Ivy (Parthenocissus Tricuspidata) under Mediterranean climate, reductions in external building surface temperatures of around 13 °C can be obtained and used as analytic parameter in a future 3D-BIM-object.

The authors at GREiA research group would like to thank the Catalan Government for the quality accreditation given to their research group (2017 SGR 1537). GREiA is a certified agent TECNIO in the category of technology developers from the Government of Catalonia. The authors at GRAP research group would like to thank the Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya (grant 2017 SGR 646), the Spanish Ministry of Economy and Competitiveness (project AGL2013-48297-C2-2-R) and the Spanish Ministry of Science, Innovation and Universities (project RTI2018-094222-B-I00). This work is partially supported by ICREA under the ICREA Academia programme. The authors also wish to thank Dr. Jaume Arnó for his contribution in the statistical analysis of the results.