A pilot study of the wind speed along the Rome–Naples HS/HC railway line.: Part 2—Probabilistic analyses and methodology assessment

Abstract The safety of railway operations under wind actions has recently become a topical matter due to the development of high-speed trains, which are very sensitive to crosswind conditions. For this reason, RFI (the Italian railway network) has entrusted the University of Genoa with a study of the wind hazard of the Rome–Naples High Speed (HS)/High Capacity (HC) railway line. A consistent part of such a study is focused on the probabilistic analysis of the wind speed and direction along that railway line, with the aim of developing a general procedure that can be applied to any railway line. This paper represents the logical prosecution of a companion paper where numerical simulations have been carried out of the wind fields along the line. These results are used together with a large amount of data measured by neighbouring meteorological stations in order to establish a probabilistic model of the wind speed and direction along the line. Such model provides a full representation of the wind climate and hazard of the railway line, and represents the basic step towards the development of a full risk analysis. Preliminary evaluations are also reported in order to assess the robustness and reliability of the methodology applied and the results obtained.

[1]  Jon Wiernga Representative roughness parameters for homogeneous terrain , 1993 .

[2]  Andrea Freda,et al.  A pilot study of the wind speed along the Rome–Naples HS/HC railway line. Part 1—Numerical modelling and wind simulations , 2010 .

[3]  E. Simiu,et al.  Extreme Wind Distribution Tails: A “Peaks over Threshold” Approach , 1996 .

[4]  B. J. Vickery,et al.  On the prediction of extreme wind speeds from the parent distribution , 1977 .

[5]  Giovanni Solari,et al.  Statistical analysis of extreme wind speeds , 1996 .

[6]  J. Holmes,et al.  Application of the generalized Pareto distribution to extreme value analysis in wind engineering , 1999 .

[7]  Ken Balkwill Engineering Sciences Data Unit (ESDU) , 2009 .

[8]  Giovanni Solari,et al.  Statistical analysis of high return period wind speeds , 1992 .

[9]  Ian R. Harris Generalised Pareto methods for wind extremes. Useful tool or mathematical mirage , 2005 .

[10]  E. Gumbel,et al.  Statistics of extremes , 1960 .

[11]  Michael Kasperski,et al.  Specification of the design wind load—A critical review of code concepts , 2009 .

[12]  R. Ian Harris,et al.  XIMIS, a penultimate extreme value method suitable for all types of wind climate , 2009 .

[13]  Giovanni Solari,et al.  Wind speed statistics , 1996 .

[14]  E. Simiu,et al.  Extreme wind load estimates based on the Gumbel distribution of dynamic pressures: an assessment , 2001 .

[15]  C. F. Ratto Modelling Of Atmospheric Flow Fields , 1996 .

[16]  Janos Galambos,et al.  Classical Extreme Value Model and Prediction of Extreme Winds , 1999 .

[17]  E. Takle,et al.  Note on the Use of Weibull Statistics to Characterize Wind-Speed Data , 1978 .

[18]  d Structures,et al.  Wind effects on buildings and structures : proceedings [of the] International Research Seminar, Ottawa, Canada, 11-15 September 1967 , 1968 .

[19]  Nicholas J. Cook,et al.  Exact and general FT1 penultimate distributions of extreme wind speeds drawn from tail-equivalent Weibull parents , 2004 .

[20]  W. Weibull A Statistical Distribution Function of Wide Applicability , 1951 .