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Evaluating the Comprehension of Construction Schedules of an Artificial Intelligence

  • Tulio Sulbaran

Construction schedules are an important tool to communicate with the project stakeholders and are critical for the project management team to plan, coordinate, and manage construction projects. Each construction project has a unique schedule that is created based on the construction drawings, specifications, contracting requirements, construction methods, and the judgment of the project management team. Therefore, each construction schedule is unique in many aspects such as the number of activities, the names of the activities, the duration of those activities, and the relationship between the activities. The names of the activities are of particular interest as they are the critical core unit to creating the schedule. Furthermore, the activities are the ones that bring together all other aspects of the schedule. Unfortunately, there is no standard naming conversion for those activities and they vary from project to project as well as from project management team to project management team. This inconsistency of the activity name makes it extremely challenging for both humans and machines to understand the meaning and scope of the activities. Thus, the problem that this paper addresses is the challenge faced by machines to comprehend the activities of a construction schedule. Therefore, the objective of this paper is to evaluate the ability of an Artificial Intelligence (AI) implementation to comprehend activities in a construction schedule. This research was conducted following a mixed research method. The AI implementation training was done by providing the Construction Specifications Institute (CSI) Master Format activity list to a Sentence Transformer. Then the AI was given the task of interpreting the activities of a construction schedule according to the 50 Divisions of the CSI Master Format. A group of senior construction students was also given the same interpretation task. The evaluation was done by comparing the results of the AI vs the humans for each of the activities in the construction schedule. The result was that the AI has 0.56 accuracy, 0.50 precision, 0.85 recall and, 0.64 F1 Score. This result is very promising and it supports further research to refine the AI to increase its ability to comprehend construction schedule activities. Upon achieving a higher level of comprehension an AI could be used to assist humans in the preparation of construction schedules or perhaps prepare drafts of the construction schedules for the human to review

  • Keywords:
  • Construction Scheduling,
  • Decision Support,
  • Artificial Intelligence,
  • Comprehension,
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Tulio Sulbaran

The University of Texas at San Antonio, United States

  1. Amer, F., & Golparvar-Fard, M. (2019). Automatic Understanding of Construction Schedules: Part-of-Activity Tagging. Proceedings of the 2019 European Conference on Computing in Construction, 1, 190–197. DOI: 10.35490/EC3.2019.196
  2. Amer, F., Jung, Y., & Golparvar-Fard, M. (2021). Transformer machine learning language model for auto-alignment of long-term and short-term plans in construction. Automation in Construction, 132, 103929. DOI: 10.1016/j.autcon.2021.103929
  3. Bentahar, O., & Cameron, R. (2015). Design and Implementation of a Mixed Method Research Study in Project Management. The Electronic Journal of Business Research Methods. https://www.semanticscholar.org/paper/Design-and-Implementation-of-a-Mixed-Method-Study-Bentahar-Cameron/7541f31e4c91c95e68a83f89fa15edb7eba30b63
  4. Devika, R., Vairavasundaram, S., Mahenthar, C. S. J., Varadarajan, V., & Kotecha, K. (2021). A Deep Learning Model Based on BERT and Sentence Transformer for Semantic Keyphrase Extraction on Big Social Data. IEEE Access, 9, 165252–165261. DOI: 10.1109/ACCESS.2021.3133651
  5. Essam, N., Khodeir, L., & Fathy, F. (2023). Approaches for BIM-based multi-objective optimization in construction scheduling. Ain Shams Engineering Journal, 14(6), 102114. DOI: 10.1016/j.asej.2023.102114
  6. Grewal, D. S. (2014). A Critical Conceptual Analysis of Definitions of Artificial Intelligence as Applicable to Computer Engineering. IOSR Journal of Computer Engineering, 16, 09–13.
  7. Halpin, D. W. & Senior, Bolivar. (2017). Construction management (5th Edition). John Wiley & Sons.
  8. Heigermoser, D., García de Soto, B., Abbott, E. L. S., & Chua, D. K. H. (2019). BIM-based Last Planner System tool for improving construction project management. Automation in Construction, 104, 246–254. DOI: 10.1016/j.autcon.2019.03.019
  9. Hong, Y., Xie, H., Bhumbra, G., & Brilakis, I. (2021). Comparing Natural Language Processing Methods to Cluster Construction Schedules. Journal of Construction Engineering and Management, 147(10), 04021136. DOI: 10.1061/(ASCE)CO.1943-7862.0002165
  10. Hong, Y., Xie, H., Hovhannisyan, V., & Brilakis, I. (2022). A graph-based approach for unpacking construction sequence analysis to evaluate schedules. Advanced Engineering Informatics, 52, 101625. DOI: 10.1016/j.aei.2022.101625
  11. Kettunen, J., & Kwak, Y. H. (2018). Scheduling Public Requests for Proposals: Models and Insights. Production and Operations Management, 27(7), 1271–1290. DOI: 10.1111/poms.12868
  12. Leedy, P. D., Ormrod, J. E., & Johnson, L. R. (2019). Practical research: Planning and design (Twelfth edition). Pearson.
  13. Magalhães-Mendes, J. (2011). A two-level genetic algorithm for the multi-mode resource-constrained project scheduling problem. 5, 271–278.
  14. Molina-Azorin, J., & Cameron, R. (2010). The Application of Mixed Methods in Organisational Research: A Literature Review. Electronic Journal of Business Research Methods, 8, 95–105.
  15. NYDOT. (2016). Best Practices for CPM Schedule Specification Compliance (p. 53). New York Department of Transportation. https://www.dot.ny.gov/main/business-center/contractors/construction-division/construction-repository/Best_Practices_for_CPM_Schedule_Spec_Compliance.pdf
  16. Okonkwo, C., Garza, R., Sulbaran, T., & Awolusi, I. (2022). A Review of Genetic Algorithm as a Decision-Making Optimization Tool in Project Management. EPiC Series in Built Environment, 3, 254–262. DOI: 10.29007/jzcl
  17. Perkel, J. M. (2018). Why Jupyter is data scientists’ computational notebook of choice. Nature, 563(7732), 145–147. DOI: 10.1038/d41586-018-07196-1
  18. Rosłon, J., Książek-Nowak, M., & Nowak, P. (2020). Schedules Optimization with the Use of Value Engineering and NPV Maximization. Sustainability, 12(18), 7454. DOI: 10.3390/su12187454
  19. Sulbaran, T., & Ahmed, F. (2017). Expert System for Construction Scheduling Decision Support Based on Travelling Salesman Problem. 53rd ASC Annual International Conference Proceedings. http://ascpro.ascweb.org/chair/paper/CPRT223002017.pdf
  20. Tiwari, A. (2022). From theory to applications—Chapter 2—Supervised learning: In R. Pandey, S. K. Khatri, N. kumar Singh, & P. Verma (Eds.), Artificial Intelligence and Machine Learning for EDGE Computing (pp. 23–32). Academic Press. DOI: 10.1016/B978-0-12-824054-0.00026-5
  21. Yurchyshyna, A., & Zarli, A. (2009). An ontology-based approach for formalisation and semantic organisation of conformance requirements in construction. Automation in Construction, 18(8), 1084–1098. DOI: 10.1016/j.autcon.2009.07.008
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  • Publication Year: 2023
  • Pages: 545-552

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  • Publication Year: 2023

Chapter Information

Chapter Title

Evaluating the Comprehension of Construction Schedules of an Artificial Intelligence

Authors

Tulio Sulbaran

DOI

10.36253/979-12-215-0289-3.53

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

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Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

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