Subject Areas : Artificial Intelligence in Structural Engineering
Mohammad Khodaparasti 1 , Ali Alijamaat 2 , majid pouraminian 3
1 - Department of Computer Engineering,Rasht Branch,Islamic Azad University,Rasht, Iran
2 - Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran
3 - Department of Civil Engineering, Islamic Azad University, Ramsar branch
Keywords:
Abstract :
[1]. F. F. E. M. A. (US), Rapid Visual Screening of Buildings for Potential Seismic Hazards: A Handbook (FEMA P-154), Applied Technological Council (ATC).
[2]. Harirchian, E.; Aghakouchaki Hosseini, S.E.; Jadhav, K.; Kumari, V.; Rasulzade, S.; Işık, E.; Wasif, M. & Lahmer. T. A. (2021). Review on application of soft computing techniques for the rapid visual safety evaluation and damage classification of existing buildings. Journal of Building Engineering. 43, 102536. https://doi.org/10.1016/j.jobe.2021.102536
[3]. Shaheryar, A.; Abarca, A.; Perrone, D. & Monteiro, R. (2022). Large-scale seismic assessment of RC buildings through rapid visual screening. International Journal of Disaster Risk Reduction. 80, 103219. https://doi.org/10.1016/j.ijdrr.2022.103219
[4]. Hassan, Ahmed F., & Mete A. Sozen. (1997). Seismic vulnerability assessment of low-rise buildings in regions with infrequent earthquakes. ACI structural journal, 94(1). pp31-39
[5]. Elyasi, N.; Kim, E. & Yeum. C.M. (2023). A Machine-Learning-Based Seismic Vulnerability Assessment Approach for Low-Rise RC Buildings. Journal of Earthquake Engineering, 1-17. https://doi.org/10.1080/13632469.2023.2220033
[6]. Chollet F. (2017). Deep learning with Python, Manning Publications, ISBN 9781617296864
[7]. Kunapuli, G. (2023). Ensemble Methods for Machine Learning, Manning Publications, Shelter Island. ISBN 1617297135.
[8]. D. Mienye & Y. Sun, (2022) A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects. IEEE Access, 10, pp. 99129-99149. https://doi.org/10.1109/ACCESS.2022.3207287
[9]. Kuncheva, L. and Whitaker, C., (2003), Measures of diversity in classifier ensembles. Machine Learning, 51, pp. 181-207. https://doi.org/10.1023/A:1022859003006
[10]. Le Cessie S. & Van Houwelingen JC. (1992), Ridge estimators in logistic regression. J R Stat Soc Ser C (Appl Stat),, 41(1), 191–201. https://doi.org/10.2307/2347628
[11]. Pedregosa F.; Varoquaux G.; Gramfort A.; Michel V.; Thirion B.; Grisel O.; Blondel M.; Prettenhofer P.; Weiss R.; Dubourg V.; et al. (2011), Scikit-learn: machine learning in python. J Mach Learn Res., 12, 2825–30.
[12]. G´eron A. (2019), Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems, O’Reilly Media.
[13]. Breiman L. (2001). Random forests, Machine Learning, 45, pp5–32. https://doi.org/10.1023/A:1010933404324
[14]. Guo, X. and Hao, P. (2021), Using a Random Forest Model to Predict the Location of Potential Damage on Asphalt Pavement. Appl. Sci., 11, 10396. https://doi.org/10.3390/app112110396
[15]. Belmokre, A., Mihoubi, M.K. & Santillán, D. (2019), Analysis of Dam Behavior by Statistical Models: Application of the Random Forest Approach. KSCE J Civ Eng, 23, 4800–4811. https://doi.org/10.1007/s12205-019-0339-0
[16]. Qinghua, H., Ma, Q., Dang, D.& Xu, J. (2023), Modal Parameters Prediction and Damage Detection of Space Grid Structure under Environmental Effects Using Stacked Ensemble Learning, Structural Control and Health Monitoring, pp1-24. https://doi.org/10.1155/2023/5687265
[17]. Khodaparasti, M.; Alijamaat, A; & Pouraminian, M. (2023). Prediction of the concrete compressive strength using improved random forest algorithm, J Build Rehabil, 8(92). https://doi.org/10.1007/s41024-023-00337-8
[18]. John GH., and Langley P. (1995), Estimating continuous distributions in Bayesian classifiers. In: Proceedings of the Eleventh conference on Uncertainty in artificial intelligence, Morgan Kaufmann Publishers Inc. pp338–345.
[19]. Sarker IH. (2019), A machine learning based robust prediction model for real-life mobile phone data. Internet Things, 5, pp180–93. https://doi.org/10.1016/j.iot.2019.01.007
[20]. Kohavi, R. (1996). Scaling Up the Accuracy of NaïveBayes Classifiers a Decision Tree Hybrid. Second International Conference on Knowledge Discovery and Data Mining, pp. 202-207