Displacement-based earthquake loss assessment methodology for RC frames infilled with masonry panels

Abstract. This paper provides a comparison between Earthquake Loss Estimation (ELE) software packages and their application using an "Open Source Procedure for Assessment of Loss using Global Earthquake Modelling software" (OPAL). The OPAL procedure was created to provide a framework for optimisation of a Global Earthquake Modelling process through: 1. overview of current and new components of earthquake loss assessment (vulnerability, hazard, exposure, specific cost, and technology); 2. preliminary research, acquisition, and familiarisation for available ELE software packages; 3. assessment of these software packages in order to identify the advantages and disadvantages of the ELE methods used; and 4. loss analysis for a deterministic earthquake (Mw = 7.2) for the Zeytinburnu district, Istanbul, Turkey, by applying 3 software packages (2 new and 1 existing): a modified displacement-based method based on DBELA (Displacement Based Earthquake Loss Assessment, Crowley et al., 2006), a capacity spectrum based method HAZUS (HAZards United States, FEMA, USA, 2003) and the Norwegian HAZUS-based SELENA (SEismic Loss EstimatioN using a logic tree Approach, Lindholm et al., 2007) software which was adapted for use in order to compare the different processes needed for the production of damage, economic, and social loss estimates. The modified DBELA procedure was found to be more computationally expensive, yet had less variability, indicating the need for multi-tier approaches to global earthquake loss estimation. Similar systems planning and ELE software produced through the OPAL procedure can be applied to worldwide applications, given exposure data.

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Displacement-Based Earthquake Loss Assessment of Masonry Buildings in Mansehra City, Pakistan

Journal of Earthquake Engineering ◽

10.1080/13632461003651794 ◽

2010 ◽

Vol 14 (sup1) ◽

pp. 1-37 ◽

Cited By ~ 10

Author(s):

Naveed Ahmad ◽

Helen Crowley ◽

Rui Pinho ◽

Qaisar Ali

Keyword(s):

Masonry Buildings ◽

Loss Assessment ◽

Earthquake Loss ◽

Displacement Based

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A Displacement-Based Optimal Design Method of RC Frames Subjected to Minor Earthquakes

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.594-597.795 ◽

2012 ◽

Vol 594-597 ◽

pp. 795-799

Author(s):

Gui Tao Chen ◽

De Min Wei

Keyword(s):

Optimal Design ◽

Optimization Design ◽

Virtual Work ◽

Design Method ◽

Horizontal Displacement ◽

Target Displacement ◽

Programming Technique ◽

Direct Displacement Based Design ◽

Rc Frames ◽

Displacement Based

A displacement-based optimization design method of RC structure was proposed by combining direct displacement-based design method with nonlinear programming technique. To avert the influence of target displacement, the stationary constraint displacement was presented, and the target displacement can be updated during the optimal design process. Principle of virtual work and Gaussian integral method was employed to simplify the explicit relationship between horizontal displacement and the section dimension. Comparison analysis of the local optimal results corresponding to different displacement shapes was conducted to achieve global optimal design. The numerical tests presented demonstrate the computational advantages of the discussed methods and suggesting that the proposed method is a reliably and efficiently tool for displacement-based optimal design.

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Rapid earthquake loss assessment based on machine learning and representative sampling

Earthquake Spectra ◽

10.1177/87552930211042393 ◽

2021 ◽

pp. 875529302110423

Author(s):

Zoran Stojadinović ◽

Miloš Kovačević ◽

Dejan Marinković ◽

Božidar Stojadinović

Keyword(s):

Machine Learning ◽

Classification Model ◽

Local Network ◽

Loss Assessment ◽

Damage Classification ◽

Sampling Algorithm ◽

Representative Sampling ◽

Random Forest Classification ◽

Area Of Interest ◽

Earthquake Loss

This article proposes a new framework for rapid earthquake loss assessment based on a machine learning damage classification model and a representative sampling algorithm. A random forest classification model predicts a damage probability distribution that, combined with an expert-defined repair cost matrix, enables the calculation of the expected repair costs for each building and, in aggregate, of direct losses in the earthquake-affected area. The proposed building representation does not include explicit information about the earthquake and the soil type. Instead, such information is implicitly contained in the spatial distribution of damage. To capture this distribution, a sampling algorithm, based on K-means clustering, is used to select a minimal number of buildings that represent the area of interest in terms of its seismic risk, independently of future earthquakes. To observe damage states in the representative set after an earthquake, the proposed framework utilizes a local network of trained damage assessors. The model is updated after each damage observation cycle, thus increasing the accuracy of the current loss assessment. The proposed framework is exemplified using the 2010 Kraljevo, Serbia earthquake dataset.

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