Probabilistic models for blast parameters and fragility estimates of steel columns subject to blast loads

This thesis proposes a probabilistic framework to predict the failure probabilities of steel columns subject to blast loads. The framework considers the uncertainties in the blast phenomenon, the demands imposed on the column, and the capacities of the column for the limit states of flexure, local buckling and global buckling. As part of the work, we propose four probabilistic blast load models. For different types of explosives and atmospheric conditions, two models predict the incident and reflected peak pressure generated by the explosion and two models predict the incident and reflected positive time duration of the blast wave. The models are probabilistic to capture the associated uncertainties, including variations in the atmospheric conditions, the inherent variability in the blast load data even for identical experimental conditions, and model error. The blast load models are used to predict the structural demands (maximum internal moment and deflection) imposed by the blast on a column. The demand models are combined with strain-rate dependent capacity models for flexure and global buckling to estimate the conditional probability of failure (or fragility) of a steel column for given scaled distance. As an example, fragility estimates for different columns representative of typical columns in steel frames are developed. The results highlight the effect of column dimensions and axial load on the failure probabilities.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Civil Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2019

Author dc:creator

• Singh, Karandeep

Contributors dc:contributor

• Gardoni, Paolo

• Blast loading
• Fragility estimates
• Probabilistic blast models
• Steel column
• SDOF analysis

Statement dc:rights

• Copyright 2018 Karandeep Singh

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/102960

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/102960