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A Contribution to a UHS-Based Seismic Risk Assessment in Croatia—A Case Study for the City of Osijek

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  • Gordana Pavić

    (Faculty of Civil Engineering and Architecture Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 3, 31000 Osijek, Croatia)

  • Marijana Hadzima-Nyarko

    (Faculty of Civil Engineering and Architecture Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 3, 31000 Osijek, Croatia)

  • Borko Bulajić

    (Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 106 314 Novi Sad, Serbia)

Abstract

Due to increases in the number of inhabitants and their concentrations in densely populated areas, there is a growing need in modern society to be cautious towards the impact of catastrophic natural events. An earthquake is a particularly major example of this. Knowledge of the seismic vulnerability of buildings in Europe and around the world has deepened and expanded over the last 20 years, as a result of the many devastating earthquakes. In this study, a review of seismic risk assessment methods in Croatia was presented with respect to the hazard, exposure, and vulnerability of buildings in the fourth largest city (Osijek) in Croatia. The proposed algorithm for a detailed risk assessment was applied to a database and is currently in its initial stage.

Suggested Citation

  • Gordana Pavić & Marijana Hadzima-Nyarko & Borko Bulajić, 2020. "A Contribution to a UHS-Based Seismic Risk Assessment in Croatia—A Case Study for the City of Osijek," Sustainability, MDPI, vol. 12(5), pages 1-24, February.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:5:p:1796-:d:325953
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    1. Mohsen Alizadeh & Esmaeil Alizadeh & Sara Asadollahpour Kotenaee & Himan Shahabi & Amin Beiranvand Pour & Mahdi Panahi & Baharin Bin Ahmad & Lee Saro, 2018. "Social Vulnerability Assessment Using Artificial Neural Network (ANN) Model for Earthquake Hazard in Tabriz City, Iran," Sustainability, MDPI, vol. 10(10), pages 1-23, September.
    2. Sandi, H. & Pomonis, A. & Francis, S. & Georgescu, E. S. & Mohindra, R. & Borcia, I. S., 2007. "Seismic vulnerability assessment: Methodological elements and applications to the case of Romania," MPRA Paper 25724, University Library of Munich, Germany.
    3. Anita Anticevic & Marijana Hadzima-Nyarko & Anamarija Rabi, 2015. "Seismic Vulnerability Of Kindergarten Buildings In The City Of Osijek," Economy of eastern Croatia yesterday, today, tommorow, Josip Juraj Strossmayer University of Osijek, Faculty of Economics, Croatia, vol. 4, pages 642-650.
    4. Nela Ivandic & Marijana Hadzima-Nyarko & Tihomir Stefic, 2015. "Seismic Vulnerability Of Primary Schools In The City Osijek," Economy of eastern Croatia yesterday, today, tommorow, Josip Juraj Strossmayer University of Osijek, Faculty of Economics, Croatia, vol. 4, pages 651-659.
    5. Mehdi Nikoo & Fatemeh Ramezani & Marijana Hadzima-Nyarko & Emmanuel Karlo Nyarko & Mohammad Nikoo, 2016. "Flood-routing modeling with neural network optimized by social-based algorithm," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 82(1), pages 1-24, May.
    6. Alexandru Banica & Lucian Rosu & Ionel Muntele & Adrian Grozavu, 2017. "Towards Urban Resilience: A Multi-Criteria Analysis of Seismic Vulnerability in Iasi City (Romania)," Sustainability, MDPI, vol. 9(2), pages 1-17, February.
    7. Yongmei Zhai & Shenglong Chen & Qianwen Ouyang, 2019. "GIS-Based Seismic Hazard Prediction System for Urban Earthquake Disaster Prevention Planning," Sustainability, MDPI, vol. 11(9), pages 1-12, May.
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