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首页> 外文期刊>Journal of Management in Engineering >Identification of Metrics for the Purdue Index for Construction Using Latent Dirichlet Allocation
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Identification of Metrics for the Purdue Index for Construction Using Latent Dirichlet Allocation

机译：用潜在龙芯片分配识别施工普渡指标的指标

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摘要

The construction industry is one of the most significant contributors to the growth of the US economy as well as the global market. The Purdue Index for Construction (Pi-C) was developed in the form of a composite index consisting of five dimensions (Economy, Stability, Social, Development, and Quality) to monitor the health status of the construction industry and facilitate data-driven decision making. Despite its great potential, metrics under the Development and Quality dimensions are still missing, which limits our understanding of the health status of the construction industry. A promising approach to identify the missing metrics is to apply the latent Dirichlet allocation (LDA), which supports the discovery of latent topics from a large set of textual data. In this regard, this work introduces an LDA-based method to identify new metrics for the Development and Quality dimensions of the Pi-C. A total of 10,466 abstracts of research papers relevant to Development and Quality were collected from academic search engines using a web crawler. The LDA analysis was conducted to identify metrics and corresponding variables. As a result, two new metrics-Technology and Education-in the Development dimension and one new metric-Sustainability-in the Quality dimension were identified for Pi-C. Results revealed that the updated Pi-C improves our understanding of the construction industry in terms of technology, education, and sustainability. The updated Pi-C is expected to assist decision makers in data-driven decision-making and strategy development in the construction industry.
机译：建筑业是美国经济增长以及全球市场的贡献者最重要的贡献者之一。施工（PI-C）的纯净指数以综合指数的形式制定，包括五个维度（经济，稳定，社会，发展和质量）来监测建筑业的健康状况，并促进数据驱动的决定制作。尽管其潜力巨大，但在发展和质量方面的指标仍然缺失，这限制了我们对建筑业健康状况的理解。有希望的识别缺失度量标准的方法是应用潜在的Dirichlet分配（LDA），该分配支持从大量文本数据中发现潜在主题。在这方面，这项工作介绍了基于LDA的方法，以识别PI-C的开发和质量尺寸的新度量。使用Web履带，共收集与开发和质量相关的10,466篇关于发展和质量的摘要。进行LDA分析以识别度量和相应的变量。因此，针对PI-C鉴定了两种新的指标技术和教育 - 在显影维度和一种新的度量可持续性 - 在质量维度中鉴定出PI-C.结果表明，更新的PI-C在技术，教育和可持续发展方面提高了对建筑业的理解。预计更新的PI-C将协助决策者在建筑业的数据驱动决策和战略发展中。

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