Transformer machine learning language model for auto-alignment of long-term and short-term plans in construction
Transformer machine learning language model for auto-alignment of long-term and short-term plans in construction
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DOI：
10.1016/j.autcon.2021.103929
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发表时间：
2021-12
影响因子：
10.3
通讯作者：
Fouad Amer;Y. Jung;M. Golparvar-Fard
中科院分区：
文献类型：
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作者：
Fouad Amer;Y. Jung;M. Golparvar-Fard
在施工中，主进度表和前瞻计划在不同的时间（每月与每周），由不同的角色（计划员与主管），使用不同的软件（调度解决方案与电子表格）以及不同的粒度级别（里程碑与生产细节）创建。它们的完全一致对于项目协调、进度更新和付款申请审查至关重要，缺乏它们可能会导致昂贵的诉讼。本文提出了第一次尝试自动链接前瞻性规划任务的主日程活动的NLP为基础的多阶段排名制定。我们的模型采用基于距离的匹配候选生成和Transformer架构的最终match.1验证结果从现实世界的项目表明，该方法可以帮助规划者匹配前瞻性规划任务的主日程安排活动，提出了一个列表的前五名的匹配精度为76.5%。我们还表明，该方法可以帮助管理员创建前瞻性计划，从主日程表生成的任务列表的基础上的活动描述。
In construction, master schedules and look-ahead plans are created at different times (monthly vs. weekly), by different personas (planner vs. superintendent), with different software (scheduling solution vs. spreadsheet), and at different levels of granularity (milestones vs. production details). Their full-alignment is essential for project coordination, progress updating, and payment application reviews, and its absence may lead to costly litigation. This paper presents the first attempt to automate linking look-ahead planning tasks to master-schedule activities following an NLP-based multi-stage ranking formulation. Our model employs distance-based matching for candidate generation and a Transformer architecture for final matching.1Validation results from real-world projects demonstrate that the method helps planners match look-ahead planning tasks to master schedule activities by presenting a list of top-five matches with a precision of 76.5%. We also show that the method helps superintendents create look-ahead plans from a master schedule by generating lists of tasks based on activity descriptions.