Guidelines for applied machine learning in construction industry-A case of profit margins estimation
Guidelines for applied machine learning in construction industry-A case of profit margins estimation
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DOI：
10.1016/j.aei.2019.101013
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发表时间：
2020-01-01
影响因子：
8.8
通讯作者：
Oyedele, Lukumon O.
中科院分区：
文献类型：
--
作者：
Bilal, Muhammad;Oyedele, Lukumon O.
机器学习（ML）领域的进展使人们能够自动化直到最近才被认为是不可能编程的任务。今天的这些进步促使企业寻求智能解决方案作为其企业软件堆栈的一部分。即使是地球仪的政府也在通过政策激励公司进入ML竞技场，因为它有望带来增长，生产力和效率的机会。作为反射，许多公司在不知道它需要什么的情况下开始ML。到目前为止的结果并不像预期的那样，因为ML，正如科技公司所炒作的那样，并不是银弹。然而，无论ML提供什么，公司都敦促将其资本化以获得竞争优势。将ML应用于现实生活中的建筑行业问题不仅仅是原型预测模型。它需要密集的活动，除了训练强大的ML模型外，还提供了一个全面的框架，用于回答建筑人员在部署智能解决方案以替代或促进其决策任务时提出的问题。IT行业中使用的现有ML指南在很大程度上仅限于训练ML模型。本文介绍了建筑行业应用机器学习（AML）从培训到操作模型的指导方针，这些指导方针来自我们与建筑人员合作提供建筑模拟工具（CST）的经验。这些指南的独特之处不仅在于为训练模型提供了一个新的框架，而且还回答了与模型置信度、信任度、可解释性、偏差、特征重要性和模型外推能力相关的关键问题。一般来说，ML模型被认为是黑箱;因此认为没有人知道模型学习了什么以及它如何生成预测。即使是极少数ML人员也几乎不知道如何回答最终用户提出的问题。如果不解释ML的能力，就无法在建筑行业更广泛地采用智能解决方案。本文提出了AML在建筑行业开发智能解决方案的详细流程。研究中的大部分讨论都是在新项目的利润率估计的背景下进行的。
The progress in the field of Machine Learning (ML) has enabled the automation of tasks that were considered impossible to program until recently. These advancements today have incited firms to seek intelligent solutions as part of their enterprise software stack. Even governments across the globe are motivating firms through policies to tape into ML arena as it promises opportunities for growth, productivity and efficiency. In reflex, many firms embark on ML without knowing what it entails. The outcomes so far are not as expected because the ML, as hyped by tech firms, is not the silver bullet. However, whatever ML offers, firms urge to capitalise it for their competitive advantage. Applying ML to real-life construction industry problems goes beyond just prototyping predictive models. It entails intensive activities which, in addition to training robust ML models, provides a comprehensive framework for answering questions asked by construction folks when intelligent solutions are getting deployed at their premises to substitute or facilitate their decision-making tasks. Existing ML guidelines used in the IT industry are vastly restricted to training ML models. This paper presents guidelines for Applied Machine Learning (AML) in the construction industry from training to operationalising models, which are drawn from our experience of working with construction folks to deliver Construction Simulation Tool (CST). The unique aspect of these guidelines lies not only in providing a novel framework for training models but also answering critical questions related to model confidence, trust, interpretability, bias, feature importance and model extrapolation capabilities. Generally, ML models are presumed black boxes; hence argued that nobody knows what a model learns and how it generates predictions. Even very few ML folks barely know approaches to answer questions asked by the end users. Without explaining the competence of ML, the broader adoption of intelligent solutions in the construction industry cannot be attained. This paper proposed a detailed process for AML to develop intelligent solutions in the construction industry. Most discussions in the study are elaborated in the context of profit margin estimation for new projects.