Classification of Seismic Vulnerability Based on Machine Learning Techniques for RC Frames

Document Type : Regular Article

Authors

1 Department of Civil and Environmental Engineering, Washington State University, Pullman, United States

2 Faculty of Civil Engineering, Semnan University, Semnan, Iran

Abstract

Seismic vulnerability means the inability of historical and monumental buildings to withstand the effects of seismic forces. This article presents a classification model to specify the damage state of the Reinforced Concrete (RC) frames based on a collection of datasets from the damaged buildings in Bingol earthquake of Turkey for use in the learning process of the algorithm. The proposed model uses two classifiers including the redundancy and also the construction quality of the buildings to estimate the class of damage from four categories including none, light, moderate and severe. The available database of the considered earthquake includes the information of 27 damaged RC buildings which are published in the literature. The model provided a simple structure for engineers to predict the class without complex calculations in which it needs a few steps to determine the class of damage for RC frames. The results show that the presented model can estimate the class of each input vector with an acceptable error.

Highlights

Google Scholar

Keywords

Main Subjects

References

[1]       Jeon JS, DesRoches R, Brilakis I, Lowes LN. Aftershock fragility curves for damaged non-ductile reinforced concrete buildings. 15th World Conf Earthq Eng, 2012.
[2]       Mirrashid M. Comparison Study of Soft Computing Approaches for Estimation of the Non-Ductile RC Joint Shear Strength. Soft Computing in Civil Engineering 2017;1:12–28. doi:10.22115/scce.2017.46318.
[3]       Thinley K, Hao H. Seismic performance of reinforced concrete frame buildings in Bhutan based on fuzzy probability analysis. Soil Dynamics and Earthquake Engineering 2017;92:604–20. doi:10.1016/j.soildyn.2016.11.004.
[4]       Naderpour H, Nagai K, Haji M, Mirrashid M. Adaptive neuro‐fuzzy inference modelling and sensitivity analysis for capacity estimation of fiber reinforced polymer‐strengthened circular reinforced concrete columns. Expert Systems 2019:e12410.
[5]       Xu Y, Wei S, Bao Y, Li H. Automatic seismic damage identification of reinforced concrete columns from images by a region-based deep convolutional neural network. Structural Control and Health Monitoring 2019;26:e2313. doi:10.1002/stc.2313.
[6]       Naderpour H, Rezazadeh Eidgahee D, Fakharian P, Rafiean AH, Kalantari SM. A new proposed approach for moment capacity estimation of ferrocement members using Group Method of Data Handling. Engineering Science and Technology, an International Journal 2019. doi:10.1016/j.jestch.2019.05.013.
[7]       Naderpour H, Mirrashid M. A computational model for estimating the compressive strength of mortars admixed with mineral materials. Journal of Computational Engineering and Physical Modeling 2018;1:16–25. doi:10.22115/CEPM.2018.136069.1031.
[8]       Naderpour H, Mirrashid M. Shear Strength Prediction of RC Beams Using Adaptive Neuro-Fuzzy Inference System. Scientia Iranica Transaction A, Civil Engineering 2018;2018.
[9]       Dung CV, Anh LD. Autonomous concrete crack detection using deep fully convolutional neural network. Automation in Construction 2019;99:52–8. doi:10.1016/j.autcon.2018.11.028.
[10]     Li S, Zhao X. Image-Based Concrete Crack Detection Using Convolutional Neural Network and Exhaustive Search Technique. Advances in Civil Engineering 2019;2019:1–12. doi:10.1155/2019/6520620.
[11]     Naderpour H, Mirrashid M. Moment capacity estimation of spirally reinforced concrete columns using ANFIS. Complex & Intelligent Systems 2019. doi:10.1007/s40747-019-00118-2.
[12]     Naderpour H, Mirrashid M. A Neuro-Fuzzy model for punching shear prediction of slab-column connections reinforced with FRP. Soft Computing in Civil Engineering 2019;3:16–26. doi:10.22115/SCCE.2018.136068.1073.
[13]     Naderpour H, Mirrashid M. Shear Failure Capacity Prediction of Concrete Beam–Column Joints in Terms of ANFIS and GMDH. Practice Periodical on Structural Design and Construction 2019;24:04019006. doi:10.1061/(ASCE)SC.1943-5576.0000417.
[14]     Naderpour H, Mirrashid M, Nagai K. An innovative approach for bond strength modeling in FRP strip-to-concrete joints using adaptive neuro–fuzzy inference system. Engineering with Computers 2019:1–18.
[15]     Naderpour H, Mirrashid M. Evaluation and Verification of Finite Element Analytical Models in Reinforced Concrete Members. Iranian Journal of Science and Technology, Transactions of Civil Engineering 2019. doi:10.1007/s40996-019-00240-8.
[16]     Tesfamariam S, Saatcioglu M. Seismic Vulnerability Assessment of Reinforced Concrete Buildings Using Hierarchical Fuzzy Rule Base Modeling. Earthquake Spectra 2010;26:235–56. doi:10.1193/1.3280115.

Files

Cited by

History

  • Receive Date: 14 March 2020
  • Revise Date: 25 April 2020
  • Accept Date: 25 April 2020

Share

How to cite

Statistics