Machine Learning in Building Documentation (ML-BAU-DOK) - Foundations for Information Extraction for Energy Efficiency and Life Cycle Analysis

Jonathan Rothenbusch, Konstantin Schütz, Feibai Huang and Björn-Martin Kurzrock

ERES from European Real Estate Society (ERES)

Abstract: The construction and real estate industry features long product life cycles, a wide range of stakeholders and a high information density. Information is not only available in large quantities, but also in a very heterogeneous and user-specific manner. Digital building documentation is partially available in some companies and even non-existent in others. The result is often analogue, unstructured building documentation, which makes the processing of data and information considerably more difficult and, in the worst case, leads to media disruptions between those involved.However, the benefits of a lean, complete and targeted digital building documentation can be manifold. In particular, automated information extraction and further information retrieval are seen as having great potential. Information extraction as an ultimate aim, requires a defined handling of analogue documents, transparent criteria regarding data quality and machine readability as well as a clear classification system.The research project ML-BAU-DOK (funded by The Federal Office for Building and Regional Planning BBSR, SWD-10.08.18.7-20.26) presents the necessary preparatory processes for advanced digital use of building documentation. First, a set of rules is created to digitize paper-based documentation in a targeted manner. The automated separation of mass documentation into individual documents, as well as the classification of documents into selected document classes, is mapped using machine-learning. The document classes are consolidated from the current worldwide class standards and prioritized according to their information content. The project includes the evaluation of 600,000 document pages, which are analysed class-specifically with regard to two use cases, energy efficiency and life cycle analysis. The methodology ensures transferability of the results to other use cases.The key result of the ML-BAU-DOK is an algorithm that automatically separates individual documents from a mass scan, assigns the individual documents to defined document classes, and thus reduces the amount of scanning and filing required. This leads to a classification system that enables information extraction as a subsequent goal and brings the construction and real estate industry closer to a Common Data Environment.

Keywords: Document Classification; Document Separation; Heterogeneous Building Documentation; Machine Learning (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2022-01-01
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ene
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