DETERMINATION OF BUILDINGS WITH TORSIONAL IRREGULARITY BY ARTIFICIAL INTELLIGENCE METHODS

Year 2022, , 280 - 285, 31.08.2022

Pınar Usta , Zeki Muhammet Mücahit Kaya , Merdan Özkahraman

https://doi.org/10.46519/ij3dptdi.1138781

Abstract

DETERMINATION OF BUILDINGS WITH TORSIONAL IRREGULARITY BY ARTIFICIAL INTELLIGENCE METHODS

Abstract

Reinforced Concrete (RC) frame buildings with shear wall are widely used in severe seismic zones. Shear walls are bearing system elements that provide the greatest resistance against hori-zontal force under the effect of the earthquake, limit displacements, and prevent torsions. A re-inforced concrete shear wall is one of the most critical structural members in buildings, in terms of carrying lateral loads. However, irregular layouts cause torsional irregularity in buildings. For this purpose, different shear wall frame reinforced concrete building models are designed. The model buildings have a regular formwork plan. The shear wall layout has different variations in each plan. These structure plans were mainly classified into two classes according to their tor-sional irregularities as structures with torsional irregularities and Structures with non-torsional ir-regularities. Artificial intelligence (AI) has revolutionized industries such as healthcare, agricul-ture, transportation, and education, as well as a variety of structural engineering problems. Arti-ficial intelligence is transforming decision-making easier and reshaping building design process-es to be smarter and automated. Artificial intelligence technology of learning from an existing knowledge base is used to automate various civil engineering applications such as compressive strength estimation of concrete, project pre-cost and duration, structural health monitoring, crack detection, and more. In this study, it is aimed to determine the structures with torsional irregulari-ty using artificial intelligence methods. Besides, the study is expected to introduce and demon-strate the capability of Artificial intelligence-based frameworks for future relevant studies within structural engineering applications and irregularities.

Keywords

Artificial intelligence, Shear walls, Reinforced concrete shear wall, torsional irregularities., Artificial intelligence, Shear walls, Reinforced concrete shear wall, torsional irregularities.

References

• 1. Demirtürk, D. and Tunç, G., “İnşaat Mühendisliği Eğitimi Ve Türkiye’de İnşaat Sektörünün Lisans Eğitimine Bakış Açısı”, NWSA Acad. Journals, Vol. 16, Issue 1, Pages 15–38, 2021.
• 2. Akgöz, B., Baltacıoğlu, A., Civalek, Ö. and Korkmaz, K., “Deprem Hasarlarinin Hizli Tespitinde Yapay Sinir Ağlari Yaklaşimi”, Mühendislik Bilim. ve Tasarım Derg., Vol. 1, Issue 1, Pages 22–27, 2010.
• 3. Usta, P., Bozdağ, Ö. and Onat, Ö., “Seismic Performance Evaluation of RC Buildings Using Irregularity Based Indices”, Teh. Vjesn. - Tech. Gaz., Vol. 29, Issue 4, Pages 1362-1371, 2022.
• 4. Uysal, N., Usta, P. and Bozdağ, Ö., “Structural Resistance of the Referenced Reinforced Concrete Structure under Earthquake and Wind Load in Mediterranean Region”, Türk Doğa ve Fen Derg., Vol. 11, Issue 1, Pages 142-150, 2022.
• 5. Özmen, G., Girgin, K. and Durgun, Y., “Torsional irregularity in multi-story structures”, Int. J. Adv. Struct. Eng., Vol. 121, Issuse 131, Pages 121–131, 2014.
• 6. Gokdemir, H., Ozbasaran, H., Dogan, M. Unluoglu E. and Albayrak, U., “Effects of torsional irregularity to structures during earthquakes”, Eng. Fail. Anal., vol. 35, Pages 713–717, 2013.
• 7. “Torsion in building”, https://civiltej.com/torsion-in-building/, December 17, 2018.
• 8. Turkish Earthquke Codes,“Specifcations for the buildings to be constructed in disaster areas”, https://www.resmigazete.gov.tr/eskiler/2018/03/20180318M1-2.htm, December 14, 2018.
• 9. Hussain, M. S. and Tengli, K. S., “Study on torsional effects of irregular buildings under seismic loads”, Int J Appl Eng Res, Vol. 13, Issue 7, Pages 55–60, 2018.
• 10. Crisafulli, F., Reboredo, A. and Torrisi, G., “Consideration of torsional effects in the displacement control of ductile buildings”, 13th World Conference on Earthquake Engineering, Vancouver, Canada, 2004.
• 11. Muhammad, U., Wang, W., Chattha, S. P. and Ali, S., “Pre-trained VGGNet Architecture for Remote-Sensing Image Scene Classification”, 24th International Conference on Pattern Recognition (ICPR), Beijing, China, Pages 1622–1627, 2018.
• 12. Hu, G., Peng, X., Yang, Y., Hospedales, T. M. and Verbeek, J., “Frankenstein: Learning Deep Face Representations Using Small Data”, IEEE Trans. Image Process., Vol. 27, Issue 1, Pages 293–303, 2018.
• 13. Haghighat, M., Abdel-Mottaleb, M. and Alhalabi, W., “Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition”, IEEE Trans. Inf. Forensics Secur., Vol. 11, Issue 9, Pages 1984–1996, 2016.
• 14. Shi, C. and Pun, C.-M., “Superpixel-based 3D deep neural networks for hyperspectral image classification”, Pattern Recognit., Vol. 74, Pages 600–616, 2018.
• 15. Li, J., Xia, C. and Chen, X., “A Benchmark Dataset and Saliency-Guided Stacked Autoencoders for Video-Based Salient Object Detection”, IEEE Trans. Image Process., Vol. 27, Issue 1, Pages 349–364, 2018. 16. Aksoy, B., Salman, O. K. M. and Ekrem, Ö., “Detection of Turkish Sign Language Using Deep Learning and Image Processing Methods”, Appl. Artif. Intell., Vol. 35, Issue 12, Pages 952–981, 2021.
• 17. Özsoy, K. and Aksoy, B., “Real-Time Data Analysis with Artificial Intelligence in Parts Manufactured by FDM Printer Using Image Processing Method”, J. Test. Eval., Vol. 50, Issue 1, Pages 629-645, 2022.
• 18. Metlek, S. and Kayaalp, K., “Derin Öğrenme ve Destek Vektör Makineleri İle Görüntüden Cinsiyet Tahmini”, Düzce Üniversitesi Bilim ve Teknol. Derg., Vol. 8, Issue 3, Pages 2208-2228, 2020.
• 19. Metlek, S. and Kayaalp, K., “Derin Öğrenme Yöntemleri ile Arıların Sağlık Durumunun Tespit Edilmesi”, Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Derg., Vol. 36, Issue 3, Pages 1715-1732, 2021.
• 20. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition”, Computer Vision and Pattern Recognition, arXiv preprint arXiv:1409.1556, Pages 1-14, 2014.
• 21. Jun, H., Shuai, L., Jinming, S., Yue, L., Jingwei, W. and Peng, J., “Facial Expression Recognition Based on VGGNet Convolutional Neural Network”, Chinese Automation Congress (CAC), Pages 4146–4151, 2018.
• 22. Saxe, A. M., Koh, P. W., Chen, Z., Bhand, M., Suresh, B. and Ng, A. Y., “On random weights and unsupervised feature learning”, 28th International Conference on International Conference on Machine Learning, , Bellevue, WA, USA, Pages 1089–1096, 2012.