Abstract Machine learning models have been shown to be useful for predicting and
assessing structural performance, identifying structural condition and informing preemptive …

This study presents a fuzzy logic model which is able to consider various uncertainties for
predicting the seismic fragility curves of the structure. The fuzzy models are generated based …

Seismic assessment of existing buildings is usually a building-specific task that relies on
refined finite element models. Such a task may require considerable computational demand …

With the rapid growth of economic, a growing number of earth and rockfill dams, especially
high concrete faced rockfill dams (CFRDs), are being constructed in the intensity earthquake …

Damages, out-of-services and interruptions of activities triggered by natural events on
healthcare facility networks highlight their vulnerability and the relevance of the …

Uncertainty quantification is a challenging task in the risk-based assessment of buildings.
This paper aims to compare reliability-based approaches to simulating epistemic and …

The purpose of this paper is to present an approach for multi-objective reliability-based
seismic design optimization of Steel Moment Resisting Frames (SMRFs), considering weight …

Infilled reinforced concrete (RC) frame building is prone to collapse during earthquakes,
highlighting the importance for seismic vulnerability assessment of infilled RC frame …

Estimating analytical fragility functions requires high computational costs due to numerous
incremental non-linear dynamic analyses. This study employs a soft computing approach to …

This paper proposes an optimal plan for seismically retrofitting reinforced concrete (RC)
frame structures. In this method, the columns are wrapped by fiber-reinforced polymer (FRP) …