Life Cycle Assessment (LCA) is essential for evaluating the environmental impact of sustainable activities in industry. Despite its importance, there exist challenges negatively impacting its deployment, particularly the time-consuming process of gathering inventory data. This research introduces a novel framework that leverages advanced text-based algorithms from Natural Language Processing (NLP), significantly enhancing the efficiency of data collection in LCA studies. Focusing on the inventory phase, the novelty of this research lies in its ability to reduce data collection time by an estimated 80%–90% compared to conventional methods and improve accuracy by directly extracting materials from bills of quantities (BoQs), which usually list all the construction materials. While our methodology shows promise, it faces challenges due to project complexity, particularly the need for consistent terminology between BoQ and reference databases, though future advancements in matching algorithms may enhance our approach’s efficiency. Real-world case studies demonstrate the framework’s effectiveness, offering flexibility across industries and system complexities.

中文翻译：

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基于文本的算法，用于在建筑行业生命周期评估研究中自动进行生命周期清单分析


生命周期评估 （LCA） 对于评估工业可持续活动对环境的影响至关重要。尽管它很重要，但仍然存在对其部署产生负面影响的挑战，尤其是收集库存数据的耗时过程。本研究引入了一种新的框架，该框架利用了自然语言处理 （NLP） 中基于文本的高级算法，显著提高了 LCA 研究中数据收集的效率。这项研究专注于库存阶段，其新颖之处在于，与传统方法相比，它能够将数据收集时间减少约 80%-90%，并通过直接从工程量清单 （BoQs） 中提取材料来提高准确性，该清单通常列出所有建筑材料。虽然我们的方法显示出前景，但由于项目复杂性，它面临着挑战，特别是需要 BoQ 和参考数据库之间术语的一致性，尽管匹配算法的未来进步可能会提高我们方法的效率。真实案例研究证明了该框架的有效性，为跨行业和系统复杂性提供了灵活性。