A New ANN Based Rapid Assessment Method for RC Residential Buildings

Özkan E., Demir A., Turan M. E.

Structural Engineering International, vol.33, no.1, pp.32-40, 2023 (SCI-Expanded, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 33 Issue: 1
  • Publication Date: 2023
  • Doi Number: 10.1080/10168664.2021.1961654
  • Journal Name: Structural Engineering International
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Communication Abstracts, Compendex, ICONDA Bibliographic, Metadex, DIALNET, Civil Engineering Abstracts
  • Page Numbers: pp.32-40
  • Keywords: Residential RC buildings, ANN, earthquake behavior, rapid assessment, FFBP, GRNN
  • Manisa Celal Bayar University Affiliated: Yes

Abstract

This study is about the development of an Artificial Neural Network (ANN) based practical rapid assessment method for Reinforced Concrete (RC) buildings by using the minimum possible number of input data. The problem is formulated as a classification problem and evaluated as two sub-problems. Feed Forward Back Propagation (FFBP) and Generalized Regression Neural Networks (GRNNs) are used in each case and eight different ANN models are developed. To develop ANN models, a total of 402 residential building models are generated of three types and up to eight storeys. The earthquake performance of these building models is investigated through the nonlinear incremental mode combination method. By using the building properties as inputs and the results of structural analyses as outputs, the ANN models are trained and tested. Additionally, existing buildings are used for validation. The results show that the earthquake behavior of RC buildings can be predicted successfully using an ANN.