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Publication Citation: Statistical Approaches in the NIST World Trade Center Analysis

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Author(s): James J. Filliben;
Title: Statistical Approaches in the NIST World Trade Center Analysis
Published: October 01, 2005
Abstract: The Federal Building and Fire Safety Investigation of the World Trade Center Disaster is currently essentially completed. The pre-collapse progression was extremely complicated, with structural, thermal, dynamic and stochastic interdependencies across time and space. Four pre-collapse stages (a simplification of reality) will be discussed: aircraft impact, fire spread, thermal propagation through insulation, and structural deformation. Engineering issues and the statistical methodologies to address them will be discussed. A major challenge in the statistical analysis of the World Trade Center was the relatively meager amount of data - little physical evidence remained that could shed light on important events occurring in the core of the WTC buildings. In this regard, the study was simultaneously assisted - and complicated - by reliance on computational engineering virtual data - primarily in the form of NIST FDS (Fire Dynamics Simulator) and phase-specific FEA (Finite Element Analysis) computational models. As analyses progress from component to subassembly to global models, such computational models require characterization and validation - it will be shown how experiment design played an important role in this regard. Various other statistical analysis techniques (e.g., complex demodulation for assessing post-impact building oscillation frequency and - indirectly - building damage) will also be discussed. This paper will emphasize the methodologies employed. Conclusions and recommendations resulting from the Federal Building and Fire Safety Investigation of the World Trade Center Disaster are presented in the investigation final report, due to be released in draft form in the Spring of 2005.
Proceedings: Proceedings of the 9th International Conference on Structural Safety and Reliability
Location: Rome,
Dates: June 19-23, 2005
Keywords: experiment design;exporatory data analysis;graphical data analysis;inference feasibility;orthogonality;problem solving framework;sensitivity analysis;statistical analysis;time series analysis
Research Areas: