Chapter
Jun 13, 2019
ASCE International Conference on Computing in Civil Engineering 2019

Integrating Positional and Attentional Cues for Construction Working Group Identification: A Long Short-Term Memory Based Machine Learning Approach

Publication: Computing in Civil Engineering 2019: Data, Sensing, and Analytics

ABSTRACT

Construction entities interact with each other to accomplish assigned tasks, constituting working groups. Recognizing working groups is important as it enables the correct comprehension of jobsite context, which in turn facilitates the interpretation of entities’ intentions, and the prediction of their movements. Aiming at identifying working groups formed by interacting workers and/or equipment, this study devises a machine learning approach to integrate positional and attentional cues. Methods are created to represent the spatial and attentional states of individual entities and compute positional and attentional cues between two entities. A long short-term memory network is adopted to identify the working groups. The proposed method is validated using construction videos that are available online and taken by the authors. The results suggest that by integrating positional and attentional cues, the working groups can be identified with an accuracy of over 95%, much higher than that obtained using positional cues only.

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Information & Authors

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Published In

Go to Computing in Civil Engineering 2019
Computing in Civil Engineering 2019: Data, Sensing, and Analytics
Pages: 35 - 42
Editors: Yong K. Cho, Ph.D., Georgia Institute of Technology, Fernanda Leite, Ph.D., University of Texas at Austin, Amir Behzadan, Ph.D., Texas A&M University, and Chao Wang, Ph.D., Louisiana State University
ISBN (Online): 978-0-7844-8243-8

History

Published online: Jun 13, 2019
Published in print: Jun 13, 2019

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Authors

Affiliations

Jiannan Cai [email protected]
Lyles School of Civil Engineering, Purdue Univ., 550 Stadium Mall Dr., West Lafayette, IN 47906. E-mail: [email protected]
Lyles School of Civil Engineering, Purdue Univ., 550 Stadium Mall Dr., West Lafayette, IN 47906. E-mail: [email protected]
Hubo Cai, Ph.D., M.ASCE [email protected]
P.E.
Lyles School of Civil Engineering, Purdue Univ., 550 Stadium Mall Dr., West Lafayette, IN 47906. E-mail: [email protected]

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