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首页> 外文期刊>Journal of Management in Engineering >Automated Identification of Substantial Changes in Construction Projects of Airport Improvement Program: Machine Learning and Natural Language Processing Comparative Analysis
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Automated Identification of Substantial Changes in Construction Projects of Airport Improvement Program: Machine Learning and Natural Language Processing Comparative Analysis

机译：机场改进计划建设项目的实质性变化自动识别：机器学习与自然语言处理比较分析

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摘要

Contractual changes-mainly substantial changes-within airport improvement program (AIP) projects represent a critical risk that could result in severe negative time and cost impacts. It is critical for airport projects to have in place efficient procedures to process changes effectively, or otherwise this may create an administrative choke point for their stakeholders. Further, with the current US airport infrastructure scoring a D+ (i.e., lacking behind the general US infrastructure), associated authorities called for rebuilding the US airport infrastructure. Thus, it is expected that contractual changes are going to increase for current as well as future US airport projects. This makes it critical to identify these changes early on to incorporate proper change management strategies. However, analysis of contract documents is a process that is known to be inefficient, tedious, and prone to human error. The goal of this research is to create an automated framework to predict substantial contractual changes effectively and efficiently within AIP construction projects. An independent multistep research methodology was used based on principles of natural language processing (NLP) and machine learning techniques (ML). First, the authors adopted a data set containing 876 contractual changes made to the Federal Aviation Administration (FAA) document of guidelines and policies that govern AIP projects (FAA 5100.38D). Second, the authors used NLP techniques to preprocess the aforementioned data. Third, the authors developed hyperparameter-tuned ML models, including k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), artificial neural network (ANN), extreme gradient boosting (XGBoost), and logistic regression (LR) to predict substantial changes made to the FAA 5100.38D. Accordingly, results indicate that RF showed the most accurate prediction with an area under curve (AUC) value of 0.928, a testing accuracy of 87.45%, and a mean cross-validation accuracy of 92.67%. As such, this automated framework grants stakeholders associated with AIP construction projects a computational decision support tool to easily recognize substantial changes within contract documents, both efficiently and effectively. Ultimately, this research promotes better change management implementation and supports overall AIP project success. (C) 2021 American Society of Civil Engineers.
机译：合同变更 - 主要改变 - 机场改进计划（AIP）项目代表可能导致严重负时间和成本影响的危急风险。对于机场项目来说至关重要，以便有效地处理更改的有效程序，或者这可能会为其利益相关者创造一个行政扼流圈。此外，随着目前的美国机场基础设施得分D +（即，缺乏美国基础设施背后），相关机构要求重建美国机场基础设施。因此，预计对当前的合同变化将增加以及未来的美国机场项目。这使得尽早确定这些变化是至关重要的，以纳入适当的变革管理策略。然而，合同文件的分析是一个已知是低效，繁琐的，易于人为错误的过程。该研究的目标是创建一个自动化框架，以在AIP建设项目中有效且有效地高效地预测大量的合同变革。基于自然语言处理原理（NLP）和机器学习技术（ML）的原则使用独立的多步研究方法。首先，作者采用了一个数据集，其中包含了向联邦航空管理局（FAA）的876年合同变更，管理AIP项目的准则和政策文件（FAA 5100.38D）。其次，作者使用NLP技术进行预处理上述数据。第三，作者开发了高参数调整ML型号，包括K-最近邻（KNN），支持向量机（SVM），随机森林（RF），人工神经网络（ANN），极端梯度升压（XGBoost）和逻辑回归（LR）预测对FAA 5100.38D的大量变化。因此，结果表明，RF在曲线（AUC）值下的面积为0.928，测试精度为87.45％，平均交叉验证精度为92.67％。因此，这种自动框架授予与AIP建设相关的利益相关者项目将计算决策支持工具能够在有效且有效地识别合同文件内的大量变化。最终，该研究促进了更好的变革管理实施，并支持整体AIP项目成功。 （c）2021年美国土木工程师协会。

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  • 来源
    《Journal of Management in Engineering》 |2021年第6期|04021062.1-04021062.15|共15页
  • 作者

    Khalef Ramy; El-adaway Islam H.;

  • 作者单位

    Missouri Univ Sci & Technol Dept Civil Architectural & Environm Engn 326 Butler Carlton Hall 1401 N Pine St Rolla MO 65409 USA;

    Missouri Univ Sci & Technol Dept Engn Management & Syst Engn Dept Civil Architectural & Environm Engn Construct Engn & Management 228 Butler Carlton Hall 1401 N Pine St Rolla MO 65409 USA|Missouri Univ Sci & Technol Dept Engn Management & Syst Engn Dept Civil Architectural & Environm Engn Civil Engn 228 Butler Carlton Hall 1401 N Pine St Rolla MO 65409 USA|Missouri Univ Sci & Technol Dept Engn Management & Syst Engn Dept Civil Architectural & Environm Engn Missouri Consortium Construct Innovat 228 Butler Carlton Hall 1401 N Pine St Rolla MO 65409 USA;

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