ESTIMATION OF SOIL LIQUEFACTION POTENTIAL IN THE DALAMAN RESIDENTIAL AREA USING A SUPERVISED MACHINE LEARNING MODEL

Abstract

Liquefaction is a critical phenomenon in geotechnical engineering, especially in mixed depositional environments where different soil types coexist. In such environments, assessment of liquefaction potential may be challenging. However, machine learning techniques overcome these challenges. In this study, to estimate the liquefaction potentials of the soils in the Dalaman residential area which is situated in a mixed depositional environment, supervised Multilayer Perceptron (MLP) model has been generated by using seismic parameters from the Chi-Chi and Kocaeli earthquakes and the parameters of the soils affected by these earthquakes. Sensitivity, specificity, accuracy, precision F1 score and AUC have been calculated for training and testing phases in model generation stage and for Dalaman Region. These values have been found to be 77.9%, 91.5, 85.7%, 87.0%, 0.822 and 0.930 in training phase; 80%, 83.1%, 81.8%, 75%, 0.774 and 0.930 in testing phase. For Dalaman residential area, these values have been found as 81.3%, 86.2%, 83.5%, 87.25% and 0.841. When the values from training and testing phase are compared to the results of Dalaman Region, it can be said that the model accurately estimated the liquefaction potentials of the soils in the Dalaman residential area.

Keywords

• Liquefaction
• Multilayer Perceptron Machine Learning Technique
• Mixed Depositional Environment
• Dalaman

Supporting Institution

No grants or funds were recieved for this study

Ethical Statement

This article was produced from Orkun TÜRE's Ph.D. thesis entitled "Determination of the Geo-Engineering Properties and Liquefaction Potential of the Quaternary Deposits of Dalaman-Muğla/SW Anatolia". These is no competing interest between the authors. No grants or funds were received. All rights of the data used within the scope of this study belongs to the site investigation companies and the Fethiye Municipality. Therefore, data must be requested from the site investigation companies that have been mentioned in the Acknowledgment part.

Thanks

The authors would like to thank Muğla Metropolitan Municipality, Dalaman Municipality and local site investigation companies (Ufuk Mühendislik, Alfa Mühendislik, Erdem Yerbilimleri, Şahin Mühendislik, Durak Mühendislik and Etüt Mühendislik) who shared their knowledge and/or any kind of documents including borehole logs, laboratory testing results, in-situ test results with us.

DALAMAN YERLEŞİM ALANINDAKİ TOPRAK ZEMİNLERİN SIVILAŞMA POTANSİYELİNİN DENETİMLİ MAKİNE ÖĞRENMESİ MODELİ İLE TAHMİNİ

Abstract

Sıvılaşma, özellikle karmaşık zemin tiplerinin bir arada bulunduğu karmaşık çökelim ortamlarında, geoteknik mühendisliği açısından kritik bir olgudur. Bu tür ortamlarda sıvılaşma potansiyelinin değerlendirilmesi zorlayıcı olabilir ancak makine öğrenimi teknikleri kullanarak, bu zorluklar kolayca aşılabilir. Bu çalışmada, karışık çökelim ortamında bulunan Dalaman yerleşim alanındaki zeminlerin sıvılaşma potansiyellerini tahmin etmek amacıyla, Chi-Chi ve Kocaeli depremlerine ait sismik parametreler ile bu depremlerden etkilenen zeminlerin parametreleri kullanılarak, denetimli bir makine öğrenimi algoritması olan Çok Katmanlı Algılayıcı (MLP) modeli geliştirilmiştir. Modelin oluşturulma aşamasındaki duyarlılık, özgüllük, doğruluk, kesinlik, F1-değeri ve AUC değeri eğitim aşamasında %77,9, %91,5, %85,7, %87,0, 0.822 ve 0.930 olarak, test aşamasında ise %80, %83,1, %81,8, %75, 0.774 ve 0.930 olarak bulunmuştur. Oluşturulan modelin Dalaman yerleşim alanındaki performans ölçütleri ise %81,3, %86,2, %83,5, %87,25 ve 0.841 olarak bulunmuştur. Bu değerler kıyaslandığında oluşturulan modelin Dalaman bölgesindeki zeminlerin sıvılaşma potansiyelini başarılı bir şekilde tahmin ettiği görülmüştür.

Keywords

• Sıvılaşma
• Çok Katmanlı Algılayıcı Makine Öğrenimi Tekniği
• Karışık Çökelim Ortamı
• Dalaman

References

1. Meerow, S., Newell, J. P., and Stults, M. “Defining urban resilience: A review”. Landscape and urban planning, 147, 38-49, 2016.
2. Slemmons, D. B. and Depolo, C. M. "Evaluation of active faulting and associated hazards." Active Tectonics. 45-62, 1986.
3. Boggs, S. Principles of sedimentology and stratigraphy. Vol. 662. Upper Saddle River, NJ: Pearson Prentice Hall, 2006.
4. Kramer, S. L. Geotechnical Earthquake Engineering Prentice-Hall, New Jersey. 1996.
5. Do, J., Heo, S. B., Yoon, Y. W., and Chang, I. "Evaluating the liquefaction potential of gravel soils with static experiments and steady state approaches." KSCE Journal of Civil Engineering 21, 642-651, 2017.
6. Çakır, E., Çetin, K. Ö., Eyigün, Y. and Gökçeoğlu, C. Soil liquefaction manifestations at Hatay Airport after the February 6 Kahramanmaraş Earthquake Sequence. 9th Geotechnical Symposium. İstanbul. Doi: 10.5505/2023geoteknik.SS-10. 2023.
7. Bol, E. et al. "Evaluation of soil liquefaction in the city of Hatay triggered after the February 6, 2023 Kahramanmaraş-Türkiye earthquake sequence." Engineering Geology. 339. 2024.
8. Hanna, A. M., Ural, D., and Saygılı, G. "Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data." Soil Dynamics and Earthquake Engineering. 27, 521-540, 2007.

9. Hoang, N. D., and Bui, D. T. “Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: a multi-dataset study.” Bulletin of Engineering Geology and the Environment, 77, 191-204, 2018.
10. Zhang, J., and Wang, Y. “An ensemble method to improve prediction of earthquake-induced soil liquefaction: a multi-dataset study”. Neural Computing and Applications, 33, 1533-1546, 2021.
11. Zhou, J., Huang, S., Zhou, T., Armaghani, D. J., and Qiu, Y. “Employing a genetic algorithm and grey wolf optimizer for optimizing RF models to evaluate soil liquefaction potential”. Artificial intelligence review, 55, 5673-5705, 2022.
12. Demir, S., and Sahin, E. K. “An investigation of feature selection methods for soil liquefaction prediction based on tree-based ensemble algorithms using AdaBoost, gradient boosting, and XGBoost.” Neural Computing and Applications, 35(4), 3173-3190, 2023.
13. Lee, C. J., and Hsiung, T. K. "Sensitivity analysis on a multilayer perceptron model for recognizing liquefaction cases." Computers and geotechnics 36.7: 1157-1163, 2009.
14. Doğu, A. F. “Köyceğiz-Dalaman Çevresinde Tarihi Yerleşme Alanlarının Jeomorfolojik Birimlerle İlişkisi (Güneybatı Anadolu)”. A.Ü. DTCF Coğrafya Araştırmaları Dergisi, 32, 319-328, 1988.
15. Doğu, A. F. “Köyceğiz-Dalaman Ovaları ve Çevresinin Jeomorfolojisi.” Türkiye Bilimsel ve Teknik Araştırma Kurumu: Matematik, Fizik ve Biyolojik Bilimler Araştırma Grubu. Proje= TBAG-563. 1986.
16. Çeker, A. “Doğal Ortamın Jeomorfoloji-Hidrografya Tarımsal Faaliyetlere Etkileri Bağlamında Bir Alan İncelemesi: Dalaman Ovası Örneği” Yeni Fikir Dergisi, 8(16), 29-50, 2016.
17. Seed, H. B. and Idriss, I. M. “Simplified procedure for evaluating soil liquefaction potential” J. Soil Mech. Found. Div., ASCE 97, 1249-1273, 1971.
18. Türe, O., and Karacan; E. "Soil Liquefaction Hazard Assessment of Dalaman Residential Area." Mugla Journal of Science and Technology 10.1: 72-81, 2024.
19. Bozkurt, E. "Neotectonics of Turkey–a synthesis." Geodinamica acta 14.1-3, 3-30, 2001.
20. Türe, O. “Determination of the geo-engineering properties and liquefaction potential of the Quaternary deposits of Dalaman-Muğla/SW Anatolia”, PhD Dissertation, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman Üniversitesi, 2023, 417p.
21. Okay, Aral İ. "Geology of the Menderes Massif and the Lycian Nappes south of Denizli, western Taurides." Bulletin of the Mineral Research and Exploration 109, 37-51, 1989.
22. Gül, Murat, et al. "Coastal geology of Iztuzu Spit (Dalyan, Muğla, SW Turkey)." Journal of African Earth Sciences 151, 173-183, 2019.
23. Mwandau, B. “Investigating Keystroke Dynamics as a Two-Factor Biometric Security” (MSc Thesis Strathmore University). 2018.
24. Willems, K. Keras Tutorial: Deep Learning in Python. 2017. Retrieved from DataCamp: (https://www.datacamp.com/community/tutorials/deep-learning-python)
25. Popescu, M. C., et al. "Multilayer perceptron and neural networks." WSEAS Transactions on Circuits and Systems 8, 579-588, 2009.
26. Huang, Lei. Normalization Techniques in Deep Learning. Springer, 2022.
27. Møller, M. F.. "A scaled conjugate gradient algorithm for fast supervised learning." Neural networks 6, 525-533. 1993.
28. Dubey, Shiv Ram, Satish Kumar Singh, and Bidyut Baran Chaudhuri. "Activation functions in deep learning: A comprehensive survey and benchmark." Neurocomputing 503, 92-108, 2022.
29. Heaton, Jeff. "Ian goodfellow, yoshua bengio, and aaron courville: Deep learning: The mit press, 2016, 800 pp, isbn: 0262035618." Genetic programming and evolvable machines 19, 305-307, 2018.
30. Jayawardana, R., and T. Sameera Bandaranayake. "Analysis of optimizing neural networks and artificial intelligent models for guidance, control, and navigation systems." International Research Journal of Modernization in Engineering, Technology and Science 3.3: 743-759, 2021.
31. Wood, T. “What is Softmax Function” (https://deepai.org/machine-learning-glossary-and-terms/softmax-layer)
32. Bishop, Christopher M., and Nasser M. Nasrabadi. Pattern recognition and machine learning. Vol. 4. No. 4. New York: springer, 2006.
33. Ozdemir, G., Işık, N. S., Koçkar, M. K. and Gültekin, N. “Türkiye Bina Deprem Yönetmeliği-2018 İle Uyumlu Basitleştirilmiş Zemin Sıvılaşma Potansiyeli Analizi Ve Sıvılaşma Sonrası Oturma, Yanal Deformasyon, Kayma Dayanımı Kaybı ve Kapak Tabakası Etkisi Hesap Cetveli Programı (V-2)”, 2021.
34. Ozdemir, G., Işık, N. S., Koçkar, M. K. and Gültekin, N. “Türkiye Bina Deprem Yönetmeliği-2018 İle Uyumlu Basitleştirilmiş Zemin Sıvılaşma Potansiyeli Analizi Ve Sıvılaşma Sonrası Oturma, Yanal Deformasyon, Kayma Dayanımı Kaybı ve Kapak Tabakası Etkisi Hesap Cetveli Programı kullanma kılavuzu (V-2)”, 2021.
35. Powers, D. M. "Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation." arXiv preprint arXiv:2010.16061. 2020.
36. Samui, P. and Sitharam, T. G. “Machine learning modelling for predicting soil liquefaction susceptibility” Nat. Hazards Earth Syst. Sci., 11, 1–9, 2011.