SUPPORTING CONSTRUCTABILITY REVIEWS OF DRAINAGE TUNNELLING PROJECTS THROUGH AN ARTIFICIAL NEURAL NETWORK CONCEPTUAL ESTIMATING MODEL

机译：通过人工神经网络概念估计模型支持排水隧道工程的可建设性审查

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Constructability reviews are important techniques that are usually used at an earlier stage of aproject’s life cycle. These reviews usually require quick yet feasible decisions which are based onconceptual estimates. This paper discusses a tool for supporting constructability reviews based on anArtificial Neural Networks (ANN) model. This ANN is used to conceptually estimate both; project directcost (PDC) and average crews’ productivity, of drainage tunnelling projects of repetitive nature based onsimilar projects’ historical data such as tunnel attributes, weather conditions and tunnelling method. ThisANN was developed for the City of Edmonton Drainage Section. The mechanism is to be used by themanagement team in constructability meetings during the conceptual design phase of tunnels. The ANNuses historical data of completed tunnelling projects to conceptually estimate project direct costs and postmobilization stage productivity. This paper presents the design and training of the NN model, theintegration of the model in spreadsheets to simplify its use , the limitations and future developments of themodel.

机译：可施工性审查是重要的技术，通常在开发的早期阶段使用项目的生命周期。这些审查通常需要基于以下内容的快速而可行的决定：概念上的估计。本文讨论了一种基于以下方面支持可施工性审查的工具：人工神经网络（ANN）模型。该ANN用于概念上估计两者;项目直接重复性排水隧道项目的成本（PDC）和平均船员的生产率，基于相似项目的历史数据，例如隧道属性，天气条件和隧道方法。这人工神经网络是为埃德蒙顿市排水科开发的。该机制将由团队在隧道的概念设计阶段参加可施工性会议。人工神经网络使用已完成的隧道项目的历史数据从概念上估算项目直接成本并发布动员阶段的生产力。本文介绍了NN模型的设计和训练，在电子表格中集成模型以简化其使用，模型的局限性和未来发展模型。

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《Canadian Society for Civil Engineering annual conference》|2009年|p.1-11|共11页
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A. ElNimr; C.Kuffert; S.Fernando; Y.Mohamed;
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机译：通过人工神经网络概念估计模型支持排水隧道工程的构造性评定
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SUPPORTING CONSTRUCTABILITY REVIEWS OF DRAINAGE TUNNELLING PROJECTS THROUGH AN ARTIFICIAL NEURAL NETWORK CONCEPTUAL ESTIMATING MODEL

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