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Neural network models for actual duration of Greek highway projects

机译:希腊公路工程实际持续时间的神经网络模型

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Purpose - This paper aims to examine selected similar Greek highway projects to create artificial neural network-based models to predict their actual construction duration based on data available at the bidding stage. Design/methodology/approach - Relevant literature review is presented that highlights similar research approaches. Thirty-seven highway projects, constructed in Greece, with similar type of available data, were examined. Considering each project's characteristics and the actual construction duration, correlation analysis is implemented, with the aid of SPSS. Correlation analysis identified the most significant project variables toward predicting actual duration. Furthermore, the WEKA application, through its attribute selection function, highlighted the most important subset of variables. The selected variables through correlation analysis and/or WEKA and appropriate combinations of these are used as input neurons for a neural network. Fast Artificial Neural Network (FANN) Tool is used to construct neural network models in an effort to predict projects' actual duration. Findings - Variables that significantly correlate with actual time at completion include initial cost, initial duration, length, lanes, technical projects, bridges, tunnels, geotechnical projects, embankment, landfill, land requirement (expropriation) and tender offer. Neural networks' models succeeded in predicting actual completion time with significant accuracy. The optimum neural network model produced a mean squared error with a value of 6.96E-06 and was based on initial cost, initial duration, length, lanes, technical projects, tender offer, embankment, existence of bridges, geotechnical projects and landfills. Research limitations/implications - The sample size is limited to 37 projects. These are extensive highway projects with similar work packages, constructed in Greece. Practical implications - The proposed models could early in the planning stage predict the actual project duration. Originality/value - The originality of the current study focuses both on the methodology applied (combination of Correlation Analysis, WEKA, FannTool) and on the resulting models and their potential application for future projects.
机译:目的 - 本文旨在审查所选择的类似希腊公路项目,以创建基于人工网络的模型,以预测其基于竞标阶段可用的数据的实际施工持续时间。设计/方法/方法 - 提出了相关的文献综述,突出了类似的研究方法。检查了在希腊建造的三十七个公路项目,具有类似类型的可用数据。考虑到每个项目的特点和实际施工持续时间,借助SPSS实施相关分析。相关性分析确定了预测实际持续时间的最重要的项目变量。此外,通过其属性选择函数,Weka应用程序突出显示最重要的变量子集。通过相关性分析和/或Weka的所选变量以及它们的适当组合用作神经网络的输入神经元。快速人工神经网络(FANN)工具用于构建神经网络模型,以预测项目的实际持续时间。调查结果 - 与完成时的实际时间明显相关的变量包括初始成本,初始持续时间,长度,车道,技术项目,桥梁,隧道,岩土工程,堤防,垃圾填埋场,土地要求(征收)和投标优惠。神经网络的模型成功地预测了具有显着精度的实际完成时间。最佳的神经网络模型产生了一个平均平方误差,值为6.96E-06,并且基于初始成本,初始持续时间,长度,车道,技术项目,招标优惠,堤防,桥梁存在,岩土工程和垃圾填埋场。研究限制/含义 - 样本大小限制为37个项目。这些是广泛的高速公路项目,具有类似的工作包,在希腊建造。实际意义 - 建议的模型在规划阶段的早期可能预测实际的项目持续时间。原创性/值 - 目前研究的原创性重点介绍应用于应用的方法(相关分析,Weka,Fanntool)以及由此产生的模型和未来项目的潜在应用程序。

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