Learning and critiquing pairwise activity relationships for schedule quality control via deep learning-based natural language processing
| Autor: | Fouad Amer, Julia Hockenmaier, Mani Golparvar-Fard |
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| Rok vydání: | 2022 |
| Předmět: |
Schedule
business.industry Computer science 050204 development studies Deep learning media_common.quotation_subject 05 social sciences Control (management) 02 engineering and technology Building and Construction Liquidated damages Software Control and Systems Engineering 0502 economics and business 0202 electrical engineering electronic engineering information engineering Domain knowledge 020201 artificial intelligence & image processing Quality (business) Pairwise comparison Artificial intelligence Software engineering business Civil and Structural Engineering media_common |
| Zdroj: | Automation in Construction. 134:104036 |
| ISSN: | 0926-5805 |
| DOI: | 10.1016/j.autcon.2021.104036 |
| Popis: | In construction, schedule mistakes causing delays beyond substantial completion dates cost contractors expensive liquidated damages. Hence, several industry guidelines, such as the DCMA's 14 point assessment, define schedule quality and offer systematic methods for ensuring it. These guidelines list “logic” as an essential control metric, and they require planners to ensure their schedules are free of missing or wrong logical dependencies. Checking the logic requires extensive construction domain knowledge, and planners perform it entirely manually as there are no available software solutions that support it. This paper offers a novel machine learning-based solution that learns construction scheduling domain knowledge from existing records completely automatically and applies it to validate the logic in input schedules achieving an F1 score of 88.3%. Furthermore, we tailor our method to use the learned knowledge to schedule a list of unordered activities. The details of the method, experimental results, benefits, and limitations are discussed. |
| Databáze: | OpenAIRE |
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