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    <pageNumber>27</pageNumber>
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    <issue>2025,22</issue>
    <volume>15</volume>
    <type>article</type>
    <publisherName>MDPI AG</publisherName>
    <publisherPlace>Basel</publisherPlace>
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    <completedDate>2025-11-19</completedDate>
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    <title language="eng">Harnessing Machine Learning for Multiclass Seismic Risk Assessment in Reinforced Concrete Structures</title>
    <parentTitle language="eng">Buildings</parentTitle>
    <identifier type="issn">2075-5309</identifier>
    <identifier type="doi">10.3390/buildings15224185</identifier>
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This algorithm identifies buildings that require preventive measures before an earthquake and buildings that require immediate repair or demolition after an earthquake. A key aspect of the approach is calculating each building\u2019s risk level as the ratio of its risky story to the total number of stories. That calculation provides a normalized figure, enabling comparison between buildings of varying sizes and complexities in an equitable way. The dataset of this study includes 100 buildings affected by previous earthquakes in T\u00fcrkiye and 782 buildings with detailed seismic analysis. Thirteen different building parameters, structural, seismic, and geometric, have been considered within the scope of this study. Rapid visual screening (RVS) methods were applied for structural integrity analysis, and machine learning models were used for improvement in accuracy and efficiency. In the comparison of the model sets, the approach achieved the highest accuracy of 77% with an ensemble of four models. The results demonstrate the value of blending AI with traditional methodologies for risk analysis. It shows a viable and scalable mechanism for prioritization of retrofit and inspections and helps engineers and policymakers enhance disaster preparedness. By identifying structures at high risk, this work contributes towards overall aims for earthquake resilience in buildings. This study introduces a Pearson-correlation-based feature analysis and a Random Oversampling strategy to enhance model balance. The ensemble model achieved 83% external accuracy and outperformed the traditional RVS method (68%), reducing computation time from minutes to seconds.&lt;\/jats:p&gt;","DOI":"10.3390\/buildings15224185","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T10:30:59Z","timestamp":1763634659000},"page":"4185","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Harnessing Machine Learning for Multiclass Seismic Risk Assessment in Reinforced Concrete Structures"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1589-9429","authenticated-orcid":false,"given":"Ali Erhan","family":"Yilmaz","sequence":"first","affiliation":[{"name":"Civil Engineering Department, Hacettepe University, 06800 Ankara, T\u00fcrkiye"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4569-0204","authenticated-orcid":false,"given":"Omer Faruk","family":"Cinar","sequence":"additional","affiliation":[{"name":"Civil Engineering Department, Hacettepe University, 06800 Ankara, T\u00fcrkiye"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2425-9685","authenticated-orcid":false,"given":"Alper","family":"Aldemir","sequence":"additional","affiliation":[{"name":"Civil Engineering Department, Hacettepe University, 06800 Ankara, T\u00fcrkiye"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5757-736X","authenticated-orcid":false,"given":"Burcu G\u00fcld\u00fcr","family":"Erkal","sequence":"additional","affiliation":[{"name":"Faculty of Architecture and Civil Engineering, Technical University of Applied Sciences Augsburg, 86161 Augsburg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7609-7804","authenticated-orcid":false,"given":"Onur","family":"Coskun","sequence":"additional","affiliation":[{"name":"Civil Engineering Department, Hacettepe University, 06800 Ankara, T\u00fcrkiye"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,19]]},"reference":[{"key":"ref_1","unstructured":"Council, B.S.S. (2000). Prestandard and Commentary for the Seismic Rehabilitation of Buildings, Report FEMA-356; Federal Emergency Management Agency."},{"key":"ref_2","unstructured":"Europ\u00e9en, C. (2004). Eurocode 2: Design of Concrete Structures\u2014Part 1-1: General Rules and Rules for Buildings, British Standard Institution."},{"key":"ref_3","unstructured":"Disaster and Emergency Management Presidency (2018). TEC 2018: Turkish Earthquake Code Specification for Structures to Be Built in Disaster Areas, Ministry of the Interior."},{"key":"ref_4","unstructured":"Ministry of Environment and Urbanization (2013). Guidelines for the Assessment of Buildings Under High Risk, Ministry of Environment and Urbanization."},{"key":"ref_5","unstructured":"Ministry of Environment Urbanization and Climate Change (2019). 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    <author>Ali Erhan Yilmaz</author>
    <author>Omer Faruk Cinar</author>
    <author>Alper Aldemir</author>
    <author>Burcu Güldür Erkal</author>
    <author>Onur Coskun</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>rapid visual screening</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>seismic risk assessment</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>reinforced</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>concrete</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>(RC) buildings</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>level prediction disaster resilience</value>
    </subject>
    <collection role="ddc" number="62">Ingenieurwissenschaften</collection>
    <collection role="institutes" number="">Fakultät für Architektur und Bauwesen</collection>
    <collection role="DFG-Fachsystematik" number="45">Bauwesen und Architektur</collection>
    <collection role="persons" number="güldür">Güldür Erkal, Burcu (Prof. Dr.)</collection>
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