| dc.creator |
AKAN, Recep; SÜLEYMAN DEMİREL ÜNİVERSİTESİ |
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| dc.date |
2022-03-23T00:00:00Z |
|
| dc.date.accessioned |
2022-05-10T10:56:49Z |
|
| dc.date.available |
2022-05-10T10:56:49Z |
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| dc.identifier |
https://dergipark.org.tr/tr/pub/jesd/issue/69033/999619 |
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| dc.identifier |
10.21923/jesd.999619 |
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| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/96155 |
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| dc.description |
Sheet pile walls are flexible retaining structures that are used to hold the horizontal soil pressures behind them, especially in situations that cause stress changes such as excavation. They are divided into two as cantilever and externally supported. Cantilever walls are used in excavations with a maximum depth of 6 meters and are supported by anchors in excavations deeper than this. Some of the values to be calculated in the design of cantilever sheet pile walls are the embedment depth and the maximum bending moment that(Mmax) will occur in the cross-section of the wall. There are various approaches in analytical methods that have complex calculation steps such as determining earth pressures, solving second and third-order equations. In this study, the Mmax that will occur in the cross-section of a cantilever sheet pile wall penetrates in the sand is estimated by the expressions obtained with the help of multiple linear regression(MLR) analysis. The results showed that the Mmax may not be achieved by only MLR models but with the help of polynomial equations. |
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| dc.description |
Palplanş duvarlar, özellikle kazı gibi gerilme değişikliklerine neden olan durumlarda, yatay zemin basınçlarını arkalarında tutmak için kullanılan esnek istinat yapılarıdır. Temel olarak konsol ve dış destekli olarak ikiye ayrılırlar. Maksimum 6 metre derinliğe sahip kazılarda konsol duvarlar kullanılır ve bundan daha derin kazılarda ankrajlarla desteklenir. Konsol palplanş duvarların tasarımında hesaplanacak değerlerden bazıları gömme derinliği ve duvar kesitinde oluşacak maksimum eğilme momentidir. Toprak basınçlarının belirlenmesi ve ikinci ve üçüncü mertebeden denklemlerin çözülmesi gibi karmaşık hesaplama adımlarına sahip analitik yöntemler için çeşitli yaklaşımlar bulunmaktadır. Bu çalışmada, yapılacak bir kazı nedeniyle kuma gömülü bir konsol palplanş duvarın kesitinde oluşacak maksimum eğilme momenti, çoklu lineer regresyon analizi yardımıyla elde edilen ifadelerle tahmin edilmeye çalışılmıştır. Sonuçlar, sadece lineer regresyon modellerinin yardımı ile değil ancak tahmin sonuçlarının polinom ifadeler yardımıyla iyileştirilmesi sonucunda tatmin edici derecede başarılı tahmin edilebileceğini göstermiştir. |
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| dc.format |
application/pdf |
|
| dc.language |
en |
|
| dc.publisher |
Süleyman Demirel Üniversitesi |
|
| dc.publisher |
Süleyman Demirel University |
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| dc.relation |
https://dergipark.org.tr/tr/download/article-file/1990292 |
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| dc.source |
Volume: 10, Issue: 1
247-256 |
en-US |
| dc.source |
1308-6693 |
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| dc.source |
Mühendislik Bilimleri ve Tasarım Dergisi |
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| dc.subject |
Cantilever Sheet Pile,Bending Moment,Multiple Linear Regression,Prediction,Sand |
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| dc.subject |
Konsol Palplanş,Eğilme Momenti,Çoklu Lineer Regresyon,Tahmin,Kum |
|
| dc.title |
ESTIMATION OF THE MAXIMUM BENDING MOMENT OF CANTILEVER SHEET PILE WALLS BY USING MULTIPLE LINEAR REGRESSION ANALYSIS |
en-US |
| dc.title |
KONSOL PALPLANŞ DUVARLARIN MAKSİMUM EĞİLME MOMENTİNİN ÇOKLU LİNEER REGRESYON ANALİZİ İLE TAHMİNİ |
tr-TR |
| dc.type |
info:eu-repo/semantics/article |
|
| dc.citation |
Abdi, Y., Garavand, A.T., Sahamieh, R.Z., 2018. Prediction of strength parameters of sedimentary rocks using artificial neural networks and regression analysis. Arab. J. Geosci. 2018 1119 11, 1–11. https://doi.org/10.1007/S12517-018-3929-0 |
|
| dc.citation |
Adefemi, B.A., Wole, A.C., 2013. Regression Analysis of Compaction Delay on CBR and UCS of Lime Stabilized Yellowish Brown Lateritic Soil. EJGE 18, 3301–3314. |
|
| dc.citation |
Akbay, Z., Dalyan, İ., Akın, M.S., Gençdal, H.B., 2020. An Application of TBEC-2018 in the Prediction of Retaining Wall Dimensions with Simple Regression Analysis. Glob. J. Civ. Eng. 2. |
|
| dc.citation |
Azzouz, A.S., Krizek, R.J., Corotis, R.B., 1976. Regression Analysis of Soil Compressibility. Soils Found. 16, 19–29. https://doi.org/10.3208/SANDF1972.16.2_19 |
|
| dc.citation |
Bera, A.K., Ghosh, Ambarish, Ghosh, Amalendu, 2005. Regression model for bearing capacity of a square footing on reinforced pond ash. Geotext. Geomembranes 23, 261–285. https://doi.org/10.1016/J.GEOTEXMEM.2004.09.002 |
|
| dc.citation |
Bolton, M.D., Powrie, W., Symons, I.F., 1990a. The design of stiff in-situ walls retaining over consolidated clay-Part II: short-term behaviour (continued). Gr. Eng. 22, 34–40. |
|
| dc.citation |
Bolton, M.D., Powrie, W., Symons, I.F., 1990b. The design of stiff in-situ walls retaining over consolidated clay-Part II: long-term behaviour (continued). Gr. Eng. 23, 22–28. |
|
| dc.citation |
Bolton, M.D., Powrie, W., Symons, I.F., 1989. The design of stiff in-situ walls retaining over consolidated clay-Part I: short-term behaviour. Gr. Eng. 22, 44–47. |
|
| dc.citation |
Bransby, J.E., Milligan, G.W.E., 1975. Soil Deformations near Cantilever Retaining Walls. Geotechnique 24, 175–195. |
|
| dc.citation |
Chantana, J., Kawano, Y., Kamei, A., Minemoto, T., 2019. Description of degradation of output performance for photovoltaic modules by multiple regression analysis based on environmental factors. Optik (Stuttg). 179, 1063–1070. https://doi.org/10.1016/J.IJLEO.2018.11.040 |
|
| dc.citation |
Choi, M., Lee, G., 2010. Decision tree for selecting retaining wall systems based on logistic regression analysis. Autom. Constr. 19, 917–928. https://doi.org/10.1016/J.AUTCON.2010.06.005 |
|
| dc.citation |
Choudry, D., Singh, S., Goel, S., 2006. New approach for analysis of cantilever sheet pile with line load. J. Geotech. Geoenvironmental Eng. 43, 540–549. |
|
| dc.citation |
Coduto, D.P., 2001. Foundation Design: Principles and Practices. Prentice Hall, New Jersey, USA. |
|
| dc.citation |
Dagdeviren, U., Kaymak, B., 2020. A regression-based approach for estimating preliminary dimensioning of reinforced concrete cantilever retaining walls. Struct. Multidiscip. Optim. 61, 1657–1675. https://doi.org/10.1007/s00158-019-02470-w |
|
| dc.citation |
Das, B.M., 2014. Principles of Foundation Engineering. Cengage Learning, Boston, MA, USA. |
|
| dc.citation |
Das, B.M., 2007. Principles of Foundation Engineering, 6th Edition. Brooks/Cole Publishing Company, Pasicif Grove, CA. |
|
| dc.citation |
Gajan, S., 2011. Normalized Relationships for Depth of Embedment of Sheet Pile Walls and Soldier Pile Walls in Cohesionless Soils. Soils Found. 51, 559–564. https://doi.org/10.3208/SANDF.51.559 |
|
| dc.citation |
Hagerty, D.J., Nofal, M.M., 1992. Design aids-anchored bulkheads in sand. Can. Geotech. J. 29, 789–795. |
|
| dc.citation |
Hirata, S., Yao, S., Nishida, K., 1990. MULTIPLE REGRESSION ANALYSIS BETWEEN THE MECHANICAL AND PHYSICAL PROPERTIES OF COHESIVE SOILS. SOILS Found. 30, 91–108. https://doi.org/10.3208/SANDF1972.30.3_91 |
|
| dc.citation |
Mahdiabadi, N., Khanlari, G., 2019. Prediction of Uniaxial Compressive Strength and Modulus of Elasticity in Calcareous Mudstones Using Neural Networks, Fuzzy Systems, and Regression Analysis. Period. Polytech. Civ. Eng. 63, 104–114. https://doi.org/10.3311/PPCI.13035 |
|
| dc.citation |
Olmschenk, G., Zhu, Z., Tang, H., 2019. Generalizing semi-supervised generative adversarial networks to regression using feature contrasting. Comput. Vis. Image Underst. 186, 1–12. https://doi.org/10.1016/J.CVIU.2019.06.004 |
|
| dc.citation |
Polat, Ö., 2015. A robust regression based classifier with determination of optimal feature set. J. Appl. Res. Technol. JART 13, 443–446. https://doi.org/10.1016/J.JART.2015.08.001 |
|
| dc.citation |
Rankine, W.J., 1857. II. On the stability of loose earth. Philos. Trans. R. Soc. London 147, 9–27. |
|
| dc.citation |
Rowe, P.W., 1952. Anchored Sheet-pile walls. Proc. Inst. Civ. Eng. 1, 27–70. https://doi.org/10.1680/iicep.1952.10942 |
|
| dc.citation |
Rowe, P.W., 1951. Cantilever sheet piling in cohesionless soil, in: Engineering. Institution of Civil Engineer, London, England, pp. 316–319. |
|
| dc.citation |
Sato-Ilic, M., 2017. Knowledge-based Comparable Predicted Values in Regression Analysis. Procedia Comput. Sci. 114, 216–223. https://doi.org/10.1016/J.PROCS.2017.09.063 |
|
| dc.citation |
Seok, J.W., Kim, O.Y., Chung, C.K., Kim, M.M., 2001. Evaluation of ground and building settlement near braced excavation sites by model testing. Can. Geotech. J. 38, 1127–1133. https://doi.org/10.1139/CGJ-38-5-1127 |
|
| dc.citation |
Sitharam, T.G., 2013. Advanced Foundation Engineering. Indian Institute of Science, Bangalore, India. |
|
| dc.citation |
Srivastava, A., Malhotra, M., 2016. Earth Pressure behind a Retaining Wall under Linearly Varying Geotechnical Parameters. Indian J. Sci. Technol. 9, 1–8. https://doi.org/10.17485/IJST/2016/V9IS1/105809 |
|
| dc.citation |
Teymen, A., Mengüç, E.C., 2020. Comparative evaluation of different statistical tools for the prediction of uniaxial compressive strength of rocks. Int. J. Min. Sci. Technol. 30, 785–797. https://doi.org/10.1016/J.IJMST.2020.06.008 |
|
| dc.citation |
Yoon, G.L., Kim, B.T., 2006. Regression Analysis of Compression Index for Kwangyang Marine Clay. KSCE J. Civ. Eng. 10, 415. |
|
| dc.citation |
Yoon, S., Lee, S.R., Kim, Y.T., Go, G.H., 2015. Estimation of saturated hydraulic conductivity of Korean weathered granite soils using a regression analysis. Geomech. Eng. 9, 101–113. https://doi.org/10.12989/GAE.2015.9.1.101 |
|
| dc.citation |
Zhang, J., Thomas, L.C., 2012. Comparisons of linear regression and survival analysis using single and mixture distributions approaches in modelling LGD. Int. J. Forecast. 28, 204–215. https://doi.org/10.1016/J.IJFORECAST.2010.06.002 |
|