Enabling Component Reuse from Existing Buildings through Machine Learning, Using Google Street View to Enhance Building Databases

Abstract

Intense urbanization has led us to rethink construction and demolition practices on a global scale. There is an opportunity to respond to the climate crisis by moving towards a circular built environment. Such a paradigm shift can be achieved by critically examining the possibility of reusing components from existing buildings. This study investigates approaches and tools needed to analyse the existing building stock and methods to enable component reuse. Ocular observations were conducted in Google Street View to analyse two building-specific characteristics: (1) façade material and (2) reusable components (window, doors, and shutters) found on building facades in two cities: Barcelona and Zurich. Not all products are equally suitable for reuse and require an evaluation metric to understand which components can be reused effectively. Consequently, tailored reuse strategies that are defined by a priority order of waste prevention are put forth. Machine learning shows promising potential to visually collect building-specific characteristics that are relevant for component reuse. The data collected is used to create classification maps that can help define protocols and for urban planning. This research can upscale limited information in countries where available data about the existing building stock is insufficient.

A 5D Digital Circular Workflow: Digital Transformation Towards Matchmaking of Environmentally Sustainable Building Materials through Reuse from Disassembly

“…Since data on existing building materials was limited, ML and CV were applied to assist in documenting building facade materials. The method combines street-level imagery and CV techniques to scale up the documentation of building facade materials [100]. The use of publicly accessible street view imagery and GIS data ensures that the methodology is not restricted by proprietary data limitations.…”

Spatial approaches to a circular economy

“…Scholars have developed increasingly accurate methods for estimating locations and amount of material stock. This includes improving the accuracy of material intensity estimations with building demolition experts (Sprecher et al, 2021) or image recognition (Raghu et al, 2022), and estimating when material will become available in the future using probability distributions (Heeren & Hellweg, 2019).…”

Towards a circular building industry through digitalisation

“…This helps also eliminate hazardous content from building stock, thus, supporting the regeneration actions. This innovative way of using AI somewhat differs from the exploratory work of Raghu et al (2022a), which deploys similar image processing techniques to enable component reuse from the existing stock. SHOs could further explore expanding these AIbased inspection systems to identify reusable materials in their portfolio.…”

“…Such methods for automated retrieval of material information are becoming increasingly popular due to advancements in both software and hardware sensors. For instance, Raghu et al (2022b) built a model to detect external façade materials such as brick, stone, wood and stucco, while Kim et al (2021) explored the generation of algorithms to identify concrete and metal roofs. The algorithms can also be leveraged for condition assessment of buildings, providing insights into the current state of the building and identifying potential maintenance issues, thus, supporting maintenance operations.…”

“…-Data Integration: AI can help organise material data into structured databases or digital MPs. Automated tagging and categorisation of materials can create a searchable and easily navigable repository of information (Kovačević & Davidson, 2008;Radinger et al, 2013).…”