Multi-Hazard Assessment of Steel Hangar Structures Subjected to Seismic and Wind Loads

Remo Chiodi; Domenico Asprone; Filippo Maimone; Andrea Prota; Francesco Ricciardelli

doi:10.4028/www.scientific.net/AMM.82.778

Paper Titles

An Experimental Study on Fire Resistance of Slim Floor Beam
p.752

Fire Protection of High-Performance Concrete Using Protective Lining
p.758

On the Fire Scenario in Road Tunnels: A Comparison between Zone and Field Models
p.764

Robustness Based Structural Design: An Integrated Approach for Multi-Hazard Risk Mitigation
p.770

Multi-Hazard Assessment of Steel Hangar Structures Subjected to Seismic and Wind Loads
p.778

Assessment Spectra for Structures Subject to Passing Underground Trains
p.784

Spatial Geotechnical Information-Based Assessment of Seismic Structural Vulnerability Related to Site Effects at Daegu Metropolitan City
p.790

Structural Health Monitoring of Bridges Using Wireless Sensor Network
p.796

Dynamic Monitoring Systems for Structures under Extreme Loads
p.804

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 82Multi-Hazard Assessment of Steel Hangar Structures...

Multi-Hazard Assessment of Steel Hangar Structures Subjected to Seismic and Wind Loads

Abstract:

The Italian wind climate is characterized by rather low annual average wind velocity, and moderately high extremes; on the other hand, Italy is one of the most seismic countries in the Mediterranean area, both in terms of frequency and intensity of earthquake occurrences . These events have to be examined to define a reliable prediction of extreme loads, and a probabilistic multi-hazard approach can be employed to investigate the performance of a structure under critical events and to ensure its acceptable performance during its entire lifetime. This paper examines the case of steel airport hangars located in areas with low seismicity, where the contribution of the wind risk can represent the most important hazard. In this framework the wind vulnerability has to be characterized with a probabilistic approach and all possible failure mechanism induced by wind loads have to be analyzed. The main objective of this paper is to provide a tool for assessment and retrofit of existing structures, as well as for the design of new structures.

You might also be interested in these eBooks

Performance, Protection and Strengthening of Structures under Extreme Loading

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 82)

Pages:

778-783

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.82.778

Citation:

Cite this paper

Online since:

July 2011

Authors:

Remo Chiodi, Domenico Asprone, Filippo Maimone, Andrea Prota, Francesco Ricciardelli

Keywords:

Incremental Dynamic Analysis, Monte-Carlo Simulation, Multi-Hazard Assessment, Performance-Based Wind Engineering, Seismic Fragility, Steel Hangars, Wind Fragility, Wind Hazard

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

References

[1] B.R. Ellingwood (2006). Mitigating risk from abnormal loads and progressive collapse. J. Perform. Construct. Facil., 20:4, p.315–323.

DOI: 10.1061/(asce)0887-3828(2006)20:4(315)

Google Scholar

[2] E. Paté-Cornell (1994). Quantitative safety goals for risk management of industrial facilities. Structural Safety. 13:3, p.145–157.

DOI: 10.1016/0167-4730(94)90023-x

Google Scholar

[3] C. Meletti and V. Montaldo (2007). Stime di pericolosità sismica per diverse probabilità di superamento in 50 anni: valori di ag. Progetto DPC-INGV S1, Deliverable S1, http://esse1.mi.ingv.it/d2.html

Google Scholar

[4] F. Jalayer (2003). Direct probabilistic seismic analysis: implementing non-linear dynamic assessments. Ph.D. Thesis, Department of Civil Environment Engineering, Stanford University, p.150.

Google Scholar

[5] R.A. Fisher and L.H.C. Tippett (1928). Limiting forms of the frequency distribution of the largest or smaller member of a sample. Proc. Cambridge Philosophical Society. 24.

DOI: 10.1017/s0305004100015681

Google Scholar

[6] I.I. Gringorten (1963). A plotting rule for extreme probability paper. Journal of Geophysical Research, 68, pp.813-814.

DOI: 10.1029/jz068i003p00813

Google Scholar

[7] E.J. Gumbel (1954). Statistical theory of extreme values and some practical applications. Applied Math Series. 33, National Bureau of Standards, Washington DC.

DOI: 10.2307/3608620

Google Scholar

[8] E.J. Gumbel (1958). Statistics of Extremes. Columbia University Press, New York.

Google Scholar

[9] Lagomarsino S., Piccardo, G. and Solari, G. (1992). Statistical analysis of high return period wind speeds. Journal of Wind Engineering and Industrial Aerodynamics. 41:44, pp.485-496.

DOI: 10.1016/0167-6105(92)90452-g

Google Scholar

[10] L. Gomes and B.J. Vickery (1977). On the prediction of extreme wind speeds from the parent distribution. Journal of Wind Engineering and Industrial Aerodynamics. 2, pp.21-36.

DOI: 10.1016/0167-6105(77)90003-4

Google Scholar

[11] E.S. Takle and J.M. Brown (1978). Note on the use of Weibull statistics to characterize wind speed data". Journal of Applied Meteorology, 17.

DOI: 10.1175/1520-0450(1978)017<0556:notuow>2.0.co;2

Google Scholar

[12] B.J. Vickery (1986). Gust factors for internal pressures in low rise buildings. Journal of Wind Engineering and Industrial Aerodynamics. 23, pp.259-271.

DOI: 10.1016/0167-6105(86)90047-4

Google Scholar