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Making Each Workhour Count: Improving the Prediction of Construction Durations and Resource Allocations

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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

Construction duration is an important performance aspect of building projects because it determines the time-to-market for a building, i.e., it stands between a client’s final decision to construct a building and obtaining the benefits from the designed building. This paper shows how intelligent computing (including semantic modeling, simulation, genetic algorithms, and machine learning) improves the ability of construction professionals to predict the construction schedule duration and direct cost of building projects at the beginning of construction and during construction. Such predictions are important because they inform the schedule commitments made and the allocation of resources that practitioners believe will let them meet the commitments. Hence, the prediction methods must capture the most important phenomena that are likely to impact the duration of activities and of construction. The two applications discussed – Tri-Constraint Method (TCM) and Activity-Flow Model (AFM) – incorporate key phenomena observed in practice, such as handling of workspace constraints and flows required for the execution of activities, that are not part of the currently prevalent concepts and tools. TCM and AFM significantly improve the ability of construction professionals to make more reliable predictions of construction duration.

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Notes

  1. 1.

    The TCM research was performed by the third author in the research lab of the first author. ALICE Technologies was started by the third author. The first author serves as an advisor to ALICE Technologies.

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Acknowledgments

The authors thank the members of the Center for Integrated Facility Engineering for the support of the research reported in this paper. They also thank Cynthia Brosque for her input to the manuscript.

Author information

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Stanford University, 473 via Ortega, Room 297, Stanford, CA, 94305-4020, USA

    Martin Fischer

  2. Grupo Galopa, Cl. 119 #1143, Bogotá, Colombia

    Nelly P. Garcia-Lopez

  3. ALICE Technologies, 1040 Noel Dr Suite 203, Menlo Park, CA, 94025, USA

    René Morkos

Authors
  1. Martin Fischer
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  2. Nelly P. Garcia-Lopez
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  3. René Morkos
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Corresponding author

Correspondence to Martin Fischer .

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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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Fischer, M., Garcia-Lopez, N.P., Morkos, R. (2018). Making Each Workhour Count: Improving the Prediction of Construction Durations and Resource Allocations. 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_15

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  • DOI: https://doi.org/10.1007/978-3-319-91635-4_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91634-7

  • Online ISBN: 978-3-319-91635-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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