Title
Automated Knowledge Discovery And Data-Driven Simulation Model Generation Of Construction Operations
Abstract
Computer simulation models help construction engineers evaluate different strategies when planning field operations. Construction jobsites are inherently dynamic and unstructured, and thus developing simulation models that properly represent resource operations and interactions requires meticulous input data modeling. Therefore, unlike existing simulation modeling techniques that mainly target long-term planning and close to steady-state scenarios, a realistic construction simulation model reliable enough for short-term planning and control must be built using factual data obtained from ongoing processes of the real system. This paper presents the latest findings of authors' work in designing an integrated data-driven simulation framework that employs a distributed network of sensors to collect multi-modal data from construction equipment activities. Collected data are fused to create metadata structures and data mining methods are then applied to extract key parameters and discover contextual knowledge necessary to create or refine data-driven simulation models that represent the latest conditions on the ground. © 2013 IEEE.
Publication Date
12-1-2013
Publication Title
Proceedings of the 2013 Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013
Number of Pages
3030-3041
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/WSC.2013.6721670
Copyright Status
Unknown
Socpus ID
84894138165 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84894138165
STARS Citation
Akhavian, Reza and Behzadan, Amir H., "Automated Knowledge Discovery And Data-Driven Simulation Model Generation Of Construction Operations" (2013). Scopus Export 2010-2014. 5813.
https://stars.library.ucf.edu/scopus2010/5813