Use of Machine Learning Algorithms in Location Determination for Safe Construction
DOI:
https://doi.org/10.58190/ijamec.2023.67Keywords:
Earthquake, Machine Learning, Soil characterization, Safe construction, TBDY-2018Abstract
Disasters are events that affect life activities that cause physical, economic and social losses. These events cause loss of life and property, as well as damage to structures such as schools and hospitals that will affect the continuation of education and health services. There are two types of disasters. The first is man-made disasters and the second is natural disasters. Natural disasters occur as a result of natural events. Earthquake is a natural disaster. Disaster management is a process that covers pre-disaster, disaster and post-disaster. This study focuses on pre-earthquake disaster management. Safe construction is necessary to reduce the effects of earthquakes. Soil class is very important in a safe construction. Soil classification was made according to TBDY-2018 by using machine learning techniques for a safe construction in the Mediterranean region. 12 different machine learning algorithms were used for Classification and the results were analyzed. As a result of the analysis, the accuracy values of the algorithms are respectively: Naive Bayes 87%, LDA 88%, KNN 84%, Adaboost 96%, Logit boost 95%, Ultraboost 92%, BF Tree 98%, Extra Tree 84%, Random Forest 93%, Random Tree%. 95, Rep Tree 96%, SimpleCart 98%. The most successful algorithms in classification are Simle Cart and BT tree. The least successful algorithm is the Extra Tree algorithm.
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E. Şakalak And M. S. Döndüren, "Evaluation Of The Historical İplikçi Mosque According To Dbybhy 2007 And Tbdy 2018 Regulations," Konya Journal of Engineering Sciences, vol. 11, no. 1, pp. 191-204, 2023.
E. Serkan, "Parametric Analysis of the Performance of Steel-Concrete Composite Structures Designed with TBDY 2018," International Journal of Innovative Engineering Applications, vol. 6, no. 1, pp. 7-16, 2022.
M. I. Nurwidyanto, M. Zainuri, A. Wirasatriya, And G. Yulianto, "Microzonation For Earthquake Hazards With Hvsr Microtremor Method In The Coastal Areas Of Semarang, Indonesia," Geographia Technica, vol. 18, no. 1, 2023.
M. C. Acar and D. Kaya, "Geographic information system approach in evaluating the geotechnical properties of soils: A case study of Oymaağaç in Kayseri," Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 38, no. 2, pp. 1079-1092, 2023.
V. Linardos, M. Drakaki, P. Tzionas, and Y. L. Karnavas, "Machine Learning in disaster management: Recent developments in methods and applications," Machine Learning and Knowledge Extraction, vol. 4, no. 2, pp. 446-473, 2022.
M. Shakeel, K. Itoyama, K. Nishida, and K. Nakadai, "Detecting earthquakes: a novel deep learning-based approach for effective disaster response," Applied Intelligence, vol. 51, no. 11, pp. 8305-8315, 2021.
S. Mangalathu and H. V. Burton, "Deep learning-based classification of earthquake-impacted buildings using textual damage descriptions," International Journal of Disaster Risk Reduction, vol. 36, p. 101111, 2019.
F. Alam, F. Ofli, M. Imran, T. Alam, and U. Qazi, "Deep learning benchmarks and datasets for social media image classification for disaster response," in 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2020: IEEE, pp. 151-158.
L. Dwarakanath, A. Kamsin, R. A. Rasheed, A. Anandhan, and L. Shuib, "Automated machine learning approaches for emergency response and coordination via social media in the aftermath of a disaster: A review," IEEE Access, vol. 9, pp. 68917-68931, 2021.
R. Jena, B. Pradhan, M. Almazroui, M. Assiri, and H.-J. Park, "Earthquake-induced liquefaction hazard mapping at national-scale in Australia using deep learning techniques," Geoscience Frontiers, vol. 14, no. 1, p. 101460, 2023.
K. Cengiz et al. "Investigation of Site Characterization in the Akdeniz Region by Using Seismic Refraction and Surface Wave Methods". International Journal of Applied and Physical Sciences, vol. 4, pp. 92-102, 2018. https://dx.doi.org/10.20469/ijaps.4.50003-3
TBDY. (2018) Turkey building earthquake regulation.
[Online]. Available: https://bit.ly/2Dxgafu
L. Breiman, "Random forests," Machine learning, vol. 45, pp. 5-32, 2001.
A. Wood, V. Shpilrain, K. Najarian, and D. Kahrobaei, "Private naive bayes classification of personal biomedical data: Application in cancer data analysis," Computers in biology and medicine, vol. 105, pp. 144-150, 2019.
C. Sitawarin and D. Wagner, "On the robustness of deep k-nearest neighbors," in 2019 IEEE Security and Privacy Workshops (SPW), 2019: IEEE, pp. 1-7.
M. D. Başar, P. Sarı, N. Kılıç, and A. Akan, "Detection of chronic kidney disease by using Adaboost ensemble learning approach," in 2016 24th Signal Processing and Communication Application Conference (SIU), 2016: IEEE, pp. 773-776.
[24] Fisher, R.A., ''The use of multiple measurements in taxonomic problems. ''Annals of eugenics, 1936. 7(2): p. 179-188.
S. Kalmegh, "Analysis of weka data mining algorithm reptree, simple cart and randomtree for classification of indian news," International Journal of Innovative Science, Engineering & Technology, vol. 2, no. 2, pp. 438-446, 2015.
P. Geurts, D. Ernst, and L. Wehenkel, "Extremely randomized trees," Machine learning, vol. 63, pp. 3-42, 2006.
J. Friedman, T. Hastie, and R. Tibshirani, "Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors)," The annals of statistics, vol. 28, no. 2, pp. 337-407, 2000.
A. F. Moustafa et al., "Color doppler ultrasound improves machine learning diagnosis of breast cancer," Diagnostics, vol. 10, no. 9, p. 631, 2020.
H. Shi, "Best-first decision tree learning," The University of Waikato, 2007.
A. Tiwari, A. Chugh, and A. Sharma, "Ensemble framework for cardiovascular disease prediction," Computers in Biology and Medicine, vol. 146, p. 105624, 2022.
L. Breiman, ''Random forests'', Mach. Learning, 45 (1) (2001) 5-32. http://dx.doi.org/10.1023/A:1010933404324
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