Rapid screening method for the determination of seismic vulnerability assessment of RC building stocks


COŞKUN O., ALDEMİR A., ŞAHMARAN M.

BULLETIN OF EARTHQUAKE ENGINEERING, cilt.18, sa.4, ss.1401-1416, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 18 Sayı: 4
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1007/s10518-019-00751-9
  • Dergi Adı: BULLETIN OF EARTHQUAKE ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), Compendex, Geobase, INSPEC, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1401-1416
  • Anahtar Kelimeler: Rapid assessment, RC structures, Penalty scores, Multivariate regression, Seismic vulnerability
  • Hacettepe Üniversitesi Adresli: Evet

Özet

Until recently, seismic vulnerability assessment of large building inventories could only be done using rapid seismic assessment techniques. These techniques generally use some estimation variables whose status can be determined by visual inspection, and should therefore be well-trained to ensure sufficient accuracy. This study proposes a new rapid assessment method for reinforced concrete (RC) structures, developed based on the detailed assessment results of 545 RC structures. 400 of the available detailed assessment results were used to train the proposed rapid seismic assessment method. First, the estimation variables of the proposed method (i.e. number of stories, seismic zone, soil condition, building age, type of structural system, etc.) were selected. The penalty scores for these estimation variables were then determined using ordinary least square regression analysis and multi-variate linear regression analysis, successively. Finally, the remaining 145 RC buildings were used to test the performance of the proposed technique. The test showed that the overall correct estimation rate of the proposed method was as large as 83% for both databases.