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Visual Data and Predictive Analytics for Proactive Project Controls on Construction Sites

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Advanced Computing Strategies for Engineering (EG-ICE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10863))

Abstract

This paper presents the theoretical foundation for a project controls system that improves understanding of how construction performance can be captured, communicated, and analyzed in form of a visual production system; predicts and effectively communicates the reliability of the weekly work plan and look-ahead schedules, supports root-cause assessment on plan failure at both project and task-levels; facilitates information flows; and decentralizes decision-making. Our model-driven system builds upon novel visual data analytics to map the current state of production in 4D (3D+time), compare to 4D BIM, and expose waste at both project and task-levels. Using predictive analytics and based on actual progress and productivity data, reliability in the future state of production is forecasted to highlight potential issues in a location-driven scheme and support collaborative decision making that eliminates root causes of waste. To evaluate the performance of our system, several case studies are conducted on real-world commercial building projects. It is shown that the developed system provides visual interfaces between people and information on and offsite, enables effective pull flows, decentralizes work tracking, facilitates in-process quality control and hand-overs among contractors, and most importantly transforms retroactive and task-driven workflows in contractor coordination meetings to proactive location-driven practices.

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Acknowledgement

This material is in part based upon work supported by the National Science Foundation Grant #1446765. The support and help of our industrial collaborators in collecting data and implementing the work tracking system is greatly appreciated. The opinions, findings, and conclusions or recommendations expressed are those of the authors and do not reflect the views of the NSF, or the industrial partner mentioned above.

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Authors and Affiliations

  1. University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA

    Jacob J. Lin & Mani Golparvar-Fard

Authors
  1. Jacob J. Lin
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  2. Mani Golparvar-Fard
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Correspondence to Jacob J. Lin .

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Editors and Affiliations

  1. Applied Computing and Mechanics Laboratory (IMAC), School of Architecture, Civil and Environmental Engineering (ENAC), Swiss Federal Institute of Technology, Lausanne (EPFL), Lausanne, Switzerland

    Ian F. C. Smith

  2. Institute for Landscape, Architecture, Construction and Territory (inPact) Construction and Environment Department (CED), University of Applied Sciences, Geneva (HEPIA), Geneva, Switzerland

    Bernd Domer

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Lin, J.J., Golparvar-Fard, M. (2018). Visual Data and Predictive Analytics for Proactive Project Controls on Construction Sites. In: Smith, I., Domer, B. (eds) Advanced Computing Strategies for Engineering. EG-ICE 2018. Lecture Notes in Computer Science(), vol 10863. Springer, Cham. https://doi.org/10.1007/978-3-319-91635-4_21

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  • DOI: https://doi.org/10.1007/978-3-319-91635-4_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91634-7

  • Online ISBN: 978-3-319-91635-4

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