An Artificial Intelligence Based Model for Construction Activity Sequence Prediction in Highway Projects
Publication: Construction Research Congress 2022
ABSTRACT
Activity sequence determination plays a crucial role in construction scheduling. Many studies have been conducted to leverage sequence knowledge from historical schedules. However, they still lack in fully capturing the sequence knowledge due to the inherent unstructured characteristics of sequential patterns. This research applies an advanced Machine Learning technique called Long Short-Term Memory Recurrent Neural Network on historical schedule data to learn the activity dependency and sequence logic. The study collected 311 as-built highway schedules from a state department of transportation. Word Embedding approach is used for semantic annotation of activities to map them to feature vectors. Features based on work type and resource utilization are assigned to each activity. The model is trained to take a sequence of schedules and predict the most likely successors. The model validation yielded the accuracy of 80%. The activity feature vectors are used to visualize the project schedules. The contributions of this paper are (1) the visualized information of project activities can significantly help schedulers easily identify similar projects, and (2) schedulers can use the successor activity prediction capability of the model in activity sequence determination in developing pre-construction schedules and during construction update schedules.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Alikhani, H., and Alvanchi, A. (2019). Using genetic algorithms for long-term planning of network of bridges. Scientia Iranica, 26(5 A), 2653–2664. https://doi.org/10.24200/sci.2017.4604.
Alikhani, H., Le, C., and David Jeong, H. (2020). A Deep Learning Algorithms to Generate Activity Sequences Using Historical As-built Schedule Data. 39. https://doi.org/10.3311/CCC2020-039.
Alvanchi, A., Shiri, N., and Alikhani, H. (2020). In-depth investigation of project planning and control software package application in the construction industry of iran. International Journal of Engineering, Transactions A: Basics, 33(10), 1817–1825. https://doi.org/10.5829/IJE.2020.33.10A.01.
Amer, F., and Golparvar-Fard, M. (2019a). Automatic Understanding of Construction Schedules: Part-of-Activity Tagging. Proceedings of the 2019 European Conference on Computing in Construction, 1, 190–197. https://doi.org/10.35490/ec3.2019.196.
Amer, F., and Golparvar-Fard, M. (2019b). Formalizing Construction Sequencing Knowledge and Mining Company-Specific Best Practices from Past Project Schedules. Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019, 215–223. https://doi.org/10.1061/9780784482421.028.
Azaron, A., Perkgoz, C., and Sakawa, M. (2005). A genetic algorithm approach for the time-cost trade-off in PERT networks. Applied Mathematics and Computation, 168(2), 1317–1339. https://doi.org/10.1016/j.amc.2004.10.021.
Carbune, V., Gonnet, P., Deselaers, T., Rowley, H. A., Daryin, A., Calvo, M., Wang, L. L., Keysers, D., Feuz, S., and Gervais, P. (2020). Fast multi-language LSTM-based online handwriting recognition. International Journal on Document Analysis and Recognition, 23(2), 89–102. https://doi.org/10.1007/s10032-020-00350-4.
Chen, S. M., Griffis, F. H., Chen, P. H., and Chang, L. M. (2013). A framework for an automated and integrated project scheduling and management system. Automation in Construction, 35, 89–110. https://doi.org/10.1016/j.autcon.2013.04.002.
Chua, D. K. H., Nguyen, T. Q., and Yeoh, K. W. (2013). Automated construction sequencing and scheduling from functional requirements. Automation in Construction, 35, 79–88. https://doi.org/10.1016/j.autcon.2013.03.002.
Faghihi, V., Reinschmidt, K. F., and Kang, J. H. (2014). Construction scheduling using Genetic Algorithm based on Building Information Model. Expert Systems with Applications, 41(16), 7565–7578. https://doi.org/10.1016/j.eswa.2014.05.047.
Heon Jun, D., and El-Rayes, K. (2011). Multiobjective Optimization of Resource Leveling and Allocation during Construction Scheduling. Journal of Construction Engineering and Management, 137(12), 1080–1088. https://doi.org/10.1061/(ASCE)CO.
Jeong, H. D., and Alikhani, H. (2020). Activity Sequencing Logics Using Daily Work Report Data. Montana. Dept. of Transportation. Research Programs. https://doi.org/10.21949/1518308.
Kataoka, M. (2008). Automated Generation of Construction Plans from Primitive Geometries. Journal of Construction Engineering and Management, 134(8), 592–600. https://doi.org/10.1061/(asce)0733-9364(2008)134:8(592).
Kim, K., Walewski, J., and Cho, Y. K. (2016). Multiobjective Construction Schedule Optimization Using Modified Niched Pareto Genetic Algorithm. Journal of Management in Engineering, 32(2), 04015038. https://doi.org/10.1061/(asce)me.1943-5479.0000374.
Larsen, J. K., Shen, G. Q., Lindhard, S. M., and Brunoe, T. D. (2016). Factors Affecting Schedule Delay, Cost Overrun, and Quality Level in Public Construction Projects. Journal of Management in Engineering, 32(1), 04015032. https://doi.org/10.1061/(asce)me.1943-5479.0000391.
Le, C., Shrestha, K. J., Jeong, H. D., and Damnjanovic, I. (2021). A sequential pattern mining driven framework for developing construction logic knowledge bases. Automation in Construction, 121, 103439. https://doi.org/10.1016/j.autcon.2020.103439.
Levy, O., and Goldberg, Y. (2014). Neural word embedding as implicit matrix factorization. In Advances in Neural Information Processing Systems (Vol. 3, Issue January).
Lipton, Z. C., Berkowitz, J., and Elkan, C. (2015). A Critical Review of Recurrent Neural Networks for Sequence Learning. 1–35. http://arxiv.org/abs/1506.00019.
Liu, J., Wang, G., Hu, P., Duan, L.-Y., and Kot, A. C. (2017). Global Context-Aware Attention LSTM Networks for 3D Action Recognition.
Merity, S., Keskar, N. S., and Socher, R. (2018, August 7). Regularizing and optimizing LSTM language models. 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings. https://arxiv.org/abs/1708.02182v1.
Park, D., Kim, S., Lee, J., Choo, J., Diakopoulos, N., and Elmqvist, N. (2018). ConceptVector: Text Visual Analytics via Interactive Lexicon Building Using Word Embedding. IEEE Transactions on Visualization and Computer Graphics, 24(1), 361–370. https://doi.org/10.1109/TVCG.2017.2744478.
Park, J., Cai, H., and Student, P. D. (2015). Automatic Construction Schedule Generation Method through BIM Model Creation. Computing in Civil Engineering.
Rong, X. (2014). word2vec Parameter Learning Explained. http://arxiv.org/abs/1411.2738.
Sherstinsky, A. (2018). Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network. Physica D: Nonlinear Phenomena, 404. https://doi.org/10.1016/j.physd.2019.132306.
Tauscher, E., Smarsly, K., König, M., and Beucke, K. (2014). Automated generation of construction sequences using building information models. Computing in Civil and Building Engineering - Proceedings of the 2014 International Conference on Computing in Civil and Building Engineering, 745–752. https://doi.org/10.1061/9780784413616.093.
Wang, B., Wang, A., Chen, F., Wang, Y., and Kuo, C. C. J. (2019). Evaluating word embedding models: Methods and experimental results. In APSIPA Transactions on Signal and Information Processing (Vol. 8). Cambridge University Press. https://doi.org/10.1017/ATSIP.2019.12.
Wang, Z., and Azar, E. R. (2019). BIM-based draft schedule generation in reinforced concrete-framed buildings. Construction Innovation Vol. 19 No. 2. https://doi.org/10.1108/CI-11-2018-0094.
Zhao, X., Yeoh, K. W., and Chua, D. K. H. (2020). Extracting Construction Knowledge from Project Schedules Using Natural Language Processing. Lecture Notes in Mechanical Engineering, 197–211. https://doi.org/10.1007/978-981-15-1910-9_17.
Information & Authors
Information
Published In
Construction Research Congress 2022
Pages: 414 - 421
History
Published online: Mar 7, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.