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East, E W and Liu, L Y (2006) Multiproject Planning and Resource Controls for Facility Management. Journal of Construction Engineering and Management, 132(12), 1294–305.

Elazouni, A M (2006) Classifying Construction Contractors Using Unsupervised-Learning Neural Networks. Journal of Construction Engineering and Management, 132(12), 1242–53.

Elmisalami, T, Walters, R and Jaselskis, E J (2006) Construction IT Decision Making Using Multiattribute Utility Theory for Use in a Laboratory Information Management System. Journal of Construction Engineering and Management, 132(12), 1275–83.

Menches, C L and Hanna, A S (2006) Conceptual Planning Process for Electrical Construction. Journal of Construction Engineering and Management, 132(12), 1306–13.

Menches, C L and Hanna, A S (2006) Quantitative Measurement of Successful Performance from the Project Manager’s Perspective. Journal of Construction Engineering and Management, 132(12), 1284–93.

Moussa, M, Ruwanpura, J and Jergeas, G (2006) Decision Tree Modeling Using Integrated Multilevel Stochastic Networks. Journal of Construction Engineering and Management, 132(12), 1254–66.

Sharma, V, Al-Hussein, M and AbouRizk, S M (2006) Residential Construction Lot Grading Approval Process Optimization: Case Study of City of Edmonton. Journal of Construction Engineering and Management, 132(12), 1225–33.

Song, Y and Chua, D K H (2006) Modeling of Functional Construction Requirements for Constructability Analysis. Journal of Construction Engineering and Management, 132(12), 1314–26.

Su, Y Y, Hashash, Y M A and Liu, L Y (2006) Integration of Construction As-Built Data Via Laser Scanning with Geotechnical Monitoring of Urban Excavation. Journal of Construction Engineering and Management, 132(12), 1234–41.

  • Type: Journal Article
  • Keywords: Construction management; Underground construction; Data analysis; Data collection; Excavation; Tracking;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(2006)132:12(1234)
  • Abstract:
    The demand for urban underground space has been increasing in the past decades to create living space and to avoid traffic congestion. A critical concern during the design and development of the underground space is the influence of construction-related ground movements on neighboring facilities and utilities. Currently, engineers can estimate ground movements using a combination of semiempirical methods and numerical model simulation. However, these advanced analyses require accurate as-built construction staging data, which most projects lack. The traditional approach of collecting construction-staging data is both labor intensive and time consuming. This paper explores the use of three-dimensional laser scanning technology to accurately capture construction activities during development of an urban excavation. The paper describes the planning, execution, and data processing phases of collecting accurate construction as-built staging information over a period of 4 months at an urban excavation site in Evanston, Ill. The resulting data provide an unprecedented level of detail on the as-built site conditions and provide much needed information to civil engineering disciplines involved in an urban excavation including construction management and structural and geotechnical engineering.

Zhang, H, Tam, C M, Li, H and Shi, J J (2006) Particle Swarm Optimization-Supported Simulation for Construction Operations. Journal of Construction Engineering and Management, 132(12), 1267–74.