使用大型语言模型和建筑历史的瑞士外墙材料库存的时空映射

IF 5.4 3区 环境科学与生态学 Q2 ENGINEERING, ENVIRONMENTAL
Carlo Schmid, Fabian Kastner, Dachuan Zhang, Silke Langenberg, Stefanie Hellweg
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引用次数: 0

摘要

建筑材料存量研究是推进建筑循环经济的重要内容。然而,现有的模型往往缺乏准确性和可伸缩性。虽然机器学习已经证明了提高预测准确性的巨大潜力,但由于缺乏高质量的训练数据,它的采用受到了阻碍。在这项研究中,我们引入了一种新的方法,利用大型语言模型从建筑能源性能证书中提取以前未开发的建筑材料数据,重点是外墙。这种方法使我们能够创建超过20,000个建筑物的数据集,比以前的研究中使用的数据集要大得多。利用这个数据集,我们开发了一个机器学习模型,根据建筑特征(如建造年份、用途和位置)预测材料成分。此外,我们整合了建筑历史的知识,根据数据集中每栋建筑的体积、质量和相关的二氧化碳排放量来估计墙壁的材料存量。我们的分析揭示了材料使用模式的显著区域差异,强调了地点的关键作用——一个在现有建筑材料库存模型中经常被忽视的参数。这些发现为改进建筑存量模型提供了有价值的见解,并突出了区域定制政策在推进建筑部门循环经济方面的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Spatiotemporal mapping of Swiss exterior wall material stock using a large language model and architectural history

Spatiotemporal mapping of Swiss exterior wall material stock using a large language model and architectural history

Building material stock studies are essential for advancing the circular economy in construction. However, existing models often lack both accuracy and scalability. While machine learning has demonstrated significant potential to enhance predictive accuracy, its adoption has been hindered by a shortage of high-quality training data. In this study, we introduce a novel methodology leveraging a large language model to extract previously untapped building material data from building energy performance certificates with a focus on exterior walls. This approach enabled us to create a dataset of over 20,000 buildings—significantly larger than those used in previous studies. Leveraging this dataset, we developed a machine learning model to predict material composition based on building characteristics such as construction year, use, and location. Furthermore, we integrated knowledge of construction history to estimate the material stock of walls in terms of volume, mass, and associated CO2 emissions for each building in the dataset. Our analysis revealed significant regional variations in material use patterns, emphasizing the critical role of location—a parameter often overlooked in existing building material stock models. These findings provide valuable insights for improving building stock modeling and highlight the importance of regionally tailored policies in advancing the circular economy in the construction sector.

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来源期刊
Journal of Industrial Ecology
Journal of Industrial Ecology 环境科学-环境科学
CiteScore
11.60
自引率
8.50%
发文量
117
审稿时长
12-24 weeks
期刊介绍: The Journal of Industrial Ecology addresses a series of related topics: material and energy flows studies (''industrial metabolism'') technological change dematerialization and decarbonization life cycle planning, design and assessment design for the environment extended producer responsibility (''product stewardship'') eco-industrial parks (''industrial symbiosis'') product-oriented environmental policy eco-efficiency Journal of Industrial Ecology is open to and encourages submissions that are interdisciplinary in approach. In addition to more formal academic papers, the journal seeks to provide a forum for continuing exchange of information and opinions through contributions from scholars, environmental managers, policymakers, advocates and others involved in environmental science, management and policy.
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