Email this article Fuzzy logic based prediction of retaining wall stability
DOI: https://doi.org/10.20528/cjsmec.2023.04.003
View Counter: Abstract | 757 times | ‒ Full Article | 497 times |
Full Text:
PDFAbstract
In geotechnical engineering, retaining walls are widely employed to solve the problem of supporting horizontal loads occurring between two different soil levels. In the traditional retaining wall design, stability checks continue until a safe design is obtained according to selected wall dimensions and soil properties. This design method is a process that is time-consuming and based on trial and error. In this study, the stability control of the retaining wall, which is a complex engineering design, has been carried out with fuzzy logic methods. Adaptive network-based fuzzy inference systems (ANFISs) including Grid Partition (ANFIS-GP) and Substructive Clustering (ANFIS-SC) have been utilized as fuzzy logic methods. The sliding stability criterion of the cantilever retaining wall has been obtained by performing 1024 retaining wall designs which are created using different wall dimensions. Ninety percent and ten percent of the 1024 sliding safety factor values acquired through numerical analyses were respectively allocated to the training and testing phases. The prediction performances of the methods have been evaluated by considering the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²) obtained for the sliding safety factors during the training and testing stages. Upon juxtaposing the actual and anticipated sliding safety factors for a dataset comprising 1024 observations, it has become evident that the ANFIS-SC methodology outperforms the ANFIS-GP approach in terms of predictive accuracy. Furthermore, this analysis culminated in the determination that the application of fuzzy logic methods stands as an efficacious and dependable means for checking the stability criteria of retaining walls.
Keywords
References
ACI 318R-05 (2005). Building Code Requirements for Structural Concrete and commentary. American Concrete Institute, Farmington Hills, Mich.
Arama ZA, Kayabekir AE, Bekdaş G, Geem ZW (2020). CO2 and cost optimization of reinforced concrete cantilever soldier piles: A parametric study with harmony search algorithm. Sustainability (Switzerland), 12(15), 7–10.
Arslan Ö, Keskin I, Ateş A (2018). Farklı deprem yüklerinin betonarme konsol bir istinat duvarının maliyetine etkisinin analizi. Anadolu Üniversitesi Bilim ve Teknoloji Dergisi - B Teorik Bilimler, 6, 28–35.
Başakın EE, Ekmekcioğlu Ö, Çıtakoğlu H, Özger M (2022). A new insight to the wind speed forecasting: robust multi-stage ensemble soft computing approach based on pre-processing uncertainty assessment. Neural Computing and Applications, 34, 783–812.
Benmouiza K, Cheknane A (2019). Clustered ANFIS network using fuzzy c-means, subtractive clustering, and grid partitioning for hourly solar radiation forecasting. Theoretical and Applied Climatology, 137(1–2), 31–43.
Binici E, Öztürk Ş (2019). Konsol istinat duvar tasarımı üzerine parametrik bir çalışma. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 9(1), 203–210.
Citakoglu H, Cobaner M, Haktanir T, Kisi O (2014). Estimation of monthly mean reference evapotranspiration in Turkey. Water Resources Management, 28, 99-113.
Çıtakoğlu H, Coşkun, Ö (2021). Dalgacık dönüşüm modelli yapay zekâ teknikleri kullanılarak Orta Anadolu Bölge istasyonlarının yağış tahmini. Harran Üniversitesi Mühendislik Dergisi, 6(1), 39-54.
Dağdeviren U, Kaymak, B (2018). Investigation of parameters affecting optimum cost design of reinforced concrete retaining walls using artificial bee colony algorithm. Journal of the Faculty of Engineering and Architecture of Gazi University, 33(1), 239–253.
Das BM, Sivakugan N (2017). Principles of Foundation Engineering (9th Edition). Cengage Learning. Retrieved from http://thuvienso.hau.edu.vn:8888/dspace/handle/hau/5215
Fattahi H (2016). Indirect estimation of deformation modulus of an in situ rock mass: an ANFIS model based on grid partitioning, fuzzy c-means clustering and subtractive clustering. Geosciences Journal, 20(5), 681–690.
Gandomi AH, Kashani AR, Roke DA, Mousavi M (2015). Optimization of retaining wall design using recent swarm intelligence techniques. Engineering Structures, 103, 72–84.
Gokkus U, Yildirim MS, Yilmazoglu A (2018). Prediction of concrete and steel materials contained by cantilever retaining wall by modeling the artificial neural networks. Journal of Soft Computing in Civil Engineering, 2(4), 47-61.
Hekim M, Orhan, U (2011). Bulanık c-means kümeleme yöntemine çıkarımlı yaklaşım. İTÜ Mühendislik Dergisi, 10–1(356), 11–17.
Kaya H (2018). Akciğer hastalıkları teşhisinde sınıflandırma ve bulanık mantık yöntemlerinin uygulanması. M.Sc. thesis, Ankara University, Ankara.
Sağlamer A (1985). Derin kazıların sorunları ve destekleme sistemleri. Derin Kazılar ve İksa Metodları Sempozyumu. Boğaziçi University, İstanbul.
Manjunath K, Gowda K, Prasad, GE (2012). Optimization of cantilever earth retaining wall using artificial neural network. Proceedings of National Conference on Innovations in Civil Engineering.
Mishra P, Samui P, Mahmoudi E (2021). Probabilistic design of retaining wall using machine learning methods. Applied Sciences, 11(12), 5411.
Mustafa R, Samui P, Kumari S (2022). Reliability analysis of gravity retaining wall using hybrid anfis. Infrastructures, 7(9), 121.
Orhan M (2020). Geoteknik Mühendisliği ve Temel İnşaatı - I. Gazi University Publications, Ankara.
Önalp A, Arel E (2012). Geoteknik mühendisliğinde yapay sinir ağı uygulamaları ve bir örnek: Zemin profilinin tahmin edilmesi. İTÜ Dergisi/d, 10(4).
Priyono A, Ridwan M, Alias AJ, Atiq R, Rahmat OK, Hassan A, Ali, M (2005). Generation of fuzzy rules with subtractive clustering. Jurnal Teknologi, 43(1), 143–153.
Rankine W (1857). Earth Pressure Theory. Phil. Trans. of the Royal Soc.
Seyhan Ş (2021). Dayanma duvarlarında makine öğrenmesi ile güvenlik sayısı tahmini. M.Sc. thesis, KTO Karatay University, Konya.
Shahin MA, Jaksa MB, Maier HR (2009). Recent advances and future challenges for artificial neural systems in geotechnical engineering applications. Advances in Artificial Neural Systems, 2009.
TS 7994 (1990). Zemin Dayanma Yapıları; Sınıflandırma, Özellikleri ve Projelendirme Esasları, Turkish Standards Institute, Ankara.
Uray E, Carbas S, Erkan IH, Tan O (2019a). Parametric investigation for discrete optimal design of a cantilever retaining wall. Challenge Journal of Structural Mechanics, 5(3), 108-120.
Uray E, Demir V, Keskin A, Tan Ö (2019b). Use of artificial neural networks in stability control of cantilever retaining walls. International Civil Engineering and Architecture Conference 2019, Trabzon.
Uray E (2020). Dayanma Yapılarının Sezgisel Yöntemler Kullanılarak Optimum Tasarımı. Ph.D. thesis, Konya Technical University, Konya.
Uray E, Tan Ö, Çarbaş S, Erkan H (2021). Metaheuristics-based pre-design guide for cantilever retaining walls. Teknik Dergi, 32(4), 10967-10993.
Uray E, Carbas S, Geem ZW, Kim S (2022). Parameters optimization of Taguchi method integrated hybrid harmony search algorithm for engineering design problems. Mathematics 2022, 10(3), 327.
Yager RR, Filev DP (1994). Generation of fuzzy rules by mountain clustering. Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 2(3), 209–219.
Yepes V, Alcala J, Perea C, González-Vidosa, F (2008). A parametric study of optimum earth-retaining walls by simulated annealing. Engineering Structures, 30(3), 821–830.
