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Artificial Neural Network Model to Predict Anchored-Pile-Wall Displacements on Istanbul Greywackes

Yıl 2020, Cilt 31, Sayı 4, 10147 - 10166, 01.07.2020
https://doi.org/10.18400/tekderg.492280

Öz

Istanbul's main lithological unit is a greywacke formation locally known as the Trakya Formation. It is weathered and extensively fractured, and the stress relief induced by deep excavations causes excessive displacements in the horizontal direction. Therefore, predicting excavation-induced wall displacements is critical for avoiding damages. The aim of this study is to develop an Artificial Neural Network (ANN) model to predict anchored-pile-wall displacements at different stages of excavations performed on Istanbul's greywacke formations. A database was created on excavation and monitoring data from 11 individual projects in Istanbul. Five variables were used as input parameters, namely, excavation depth, maximum ground settlement measured behind the wall, system stiffness, standard penetration test N value of the soil depth, and index-of-observation point. The proposed model was trained, validated, and tested. Finally, two distinct projects were numerically modeled by applying the finite element method (FEM) and then used to examine the performance of the ANN model. The displacements predicted by the ANN model were compared with both the computed values obtained from the FEM analysis and actual measured displacements. The proposed ANN model accurately predicted the displacement of anchored pile walls constructed on Istanbul's greywackes at different excavation stages. 

Kaynakça

  • Peck, R.B., Deep excavations and tunneling in soft ground. Proceedings 7th I.C.S.M.F.E. State of Art Sayı., Mexico: 225-290, 1969
  • Mana, A.I., Clough, G.W., Prediction of movements for braced cuts in clay. J. Geotech. Eng. Div., 107, 759-777, 1981
  • Finno, R.J., Atmatzidis, D.K., Perkins, S.B., Observed performance of a deep excavation in clay. J. Geotech. Eng., 115(8), 1045-1064, 1989
  • Clough, G.W., O’Rourke, T.D., Construction induced movements of in-situ walls. Geotechnical special publication: Design and performance of earth retaining structures (GSP 25)., ASCE, Reston, VA, 439-470, 1990
  • Whittle, A.J., Hashash, Y.M.A., Whitman, R.V., Analysis of deep excavation in Boston., J. Geotech. Eng., 119(1), 69-90, 1993
  • Hashash, Y.M.A., Whittle, A.J.,Ground movement prediction for deep excavations in soft clay., J. Geotech. Eng., 122(6), 474-486, 1996
  • Hsieh, P.G., Ou, C.Y., Shape of ground surface settlement profiles caused by excavation., Can. Geotech. J., 35(6), 1004-1017, 1998
  • Long, M., Database for retaining wall and ground movements due to deep excavations., J. Geotech. Geoenviron. Eng., 127(3), 203-224, 2001
  • Hwang, R.N., Moh, Z.C., Evaluating effectiveness of buttresses and cross walls by reference envelopes., J. Geoeng. 3(1), 1-12, 2008
  • Wang, J.H., Xu, Z.H., Wang, W.D., Wall and ground movements due to deep excavations in Shanghai soft soils., J. Geotech. Geoenviron. Eng., 136 (7), 985-994, 2010
  • Bolton, M.D., Lam, S.Y., Vardanega, P.J., Ng, C.W., Ma, X., Ground movements due to deep excavations in Shanghai: Design charts., Front. Struct. Civ. Eng., 8(3), 201-236, 2014
  • Jan., J.C., Hung, S.L., Chi, S.Y., Chern, J.C., Neural network forecast model in deep excavation., J. Comput. Civil Eng., 16(1), 59-65, 2002
  • Goh, A.T.C., Goh, S.H., Support vector machines: their use in geotechnical engineering as illustrated using seismic liquefaction data., Comput. Geotech. 34(5), 410-421, 2007
  • Ghaboussi, J., Pecknold, D.A., Zhang, M., Haj-Ali, R., Autoprogressive training of neural network constitutive models., Int. J. Numer. Methods Fluids, 42(1), 105-126, 1998
  • Hashash, Y.M.A., Marulanda, C., Ghaboussi, J., Jung, S., Systematic update of a deep excavation model using field performance data., Comput. Geotech., 30(6), 477-488, 2003
  • Hashash, Y.M.A., Marulanda, C., Ghaboussi, J., Jung, S., Novel approach to integration of numerical modeling and field observations for deep excavation., J. Geotech. Geoenviron. Eng., 132(8), 1019-1031, 2006
  • Song, H., Osouli, A., Hashash, Y., Soil behavior and excavation instrumentation layout, 7th International symposium on field measurements in geomechanics FMGM., Boston, MA., 2007
  • Yıldız, E., Ozyazıcıoglu, M.H, Ozkan, M.Y., Lateral Pressures on Rigid Retaining Walls: A Neural Network Approach., Gazi Univ. J. Science, 23(2), 201-210, 2010
  • Johari, A., Javadi, A.A., Najafi, H., A genetic-based model to predict maximum lateral displacement of retaining wall in granular soil., Scientica Iranica, 23(1), 54-65, 2016
  • Istanbul Metropolitan Municipality Department of Earthquake Risk Management and Urban Development Directorate of Earthquake and Ground Analysis, Microzonation work for the southern European side of Istanbul, Final Report, 1-88, 2007
  • Eroskay, S.O., Graywackes of Istanbul Region, Proceedings of International Symposium on Design of Supports to Deep Excavations., Turkish Group of Soil Mechanics, Bosphorus University, 41-44, 1985
  • Yıldırım, M., Tonaroğlu, M., Selçuk, M.E., Akgüner, C., Revised stratigraphy of the tertiary deposits of Istanbul and their engineering properties., B. Eng. Geol. Environ., 72(3-4), 413-420, 2013
  • Shahin, M., Intelligent computing for modeling axial capacity of pile foundations., Can. Geotech. J, 47(2):230–243, 2010
  • Öztemel,E., Yapay Sinir Ağları, Papatya Yayıncılık, 29-162, 2012
  • Haykin, S., Neural Networks: A Comprehensive Foundation, 1,23-71, 2001
  • Rojas, R., Neural Networks A systematic Introduction, Berlin, 151-184, 1996
  • Rummelhart, D.E., Hinton, G.E., Williams, R.J., Learning Internal representation by error propagation., J. Parallel Distrib. Comput. , 1(8): 318-362, 1986

Artificial Neural Network Model to Predict Anchored-Pile-Wall Displacements on Istanbul Greywackes

Yıl 2020, Cilt 31, Sayı 4, 10147 - 10166, 01.07.2020
https://doi.org/10.18400/tekderg.492280

Öz

Istanbul's main lithological unit is a greywacke formation locally known as the Trakya Formation. It is weathered and extensively fractured, and the stress relief induced by deep excavations causes excessive displacements in the horizontal direction. Therefore, predicting excavation-induced wall displacements is critical for avoiding damages. The aim of this study is to develop an Artificial Neural Network (ANN) model to predict anchored-pile-wall displacements at different stages of excavations performed on Istanbul's greywacke formations. A database was created on excavation and monitoring data from 11 individual projects in Istanbul. Five variables were used as input parameters, namely, excavation depth, maximum ground settlement measured behind the wall, system stiffness, standard penetration test N value of the soil depth, and index-of-observation point. The proposed model was trained, validated, and tested. Finally, two distinct projects were numerically modeled by applying the finite element method (FEM) and then used to examine the performance of the ANN model. The displacements predicted by the ANN model were compared with both the computed values obtained from the FEM analysis and actual measured displacements. The proposed ANN model accurately predicted the displacement of anchored pile walls constructed on Istanbul's greywackes at different excavation stages. 

Kaynakça

  • Peck, R.B., Deep excavations and tunneling in soft ground. Proceedings 7th I.C.S.M.F.E. State of Art Sayı., Mexico: 225-290, 1969
  • Mana, A.I., Clough, G.W., Prediction of movements for braced cuts in clay. J. Geotech. Eng. Div., 107, 759-777, 1981
  • Finno, R.J., Atmatzidis, D.K., Perkins, S.B., Observed performance of a deep excavation in clay. J. Geotech. Eng., 115(8), 1045-1064, 1989
  • Clough, G.W., O’Rourke, T.D., Construction induced movements of in-situ walls. Geotechnical special publication: Design and performance of earth retaining structures (GSP 25)., ASCE, Reston, VA, 439-470, 1990
  • Whittle, A.J., Hashash, Y.M.A., Whitman, R.V., Analysis of deep excavation in Boston., J. Geotech. Eng., 119(1), 69-90, 1993
  • Hashash, Y.M.A., Whittle, A.J.,Ground movement prediction for deep excavations in soft clay., J. Geotech. Eng., 122(6), 474-486, 1996
  • Hsieh, P.G., Ou, C.Y., Shape of ground surface settlement profiles caused by excavation., Can. Geotech. J., 35(6), 1004-1017, 1998
  • Long, M., Database for retaining wall and ground movements due to deep excavations., J. Geotech. Geoenviron. Eng., 127(3), 203-224, 2001
  • Hwang, R.N., Moh, Z.C., Evaluating effectiveness of buttresses and cross walls by reference envelopes., J. Geoeng. 3(1), 1-12, 2008
  • Wang, J.H., Xu, Z.H., Wang, W.D., Wall and ground movements due to deep excavations in Shanghai soft soils., J. Geotech. Geoenviron. Eng., 136 (7), 985-994, 2010
  • Bolton, M.D., Lam, S.Y., Vardanega, P.J., Ng, C.W., Ma, X., Ground movements due to deep excavations in Shanghai: Design charts., Front. Struct. Civ. Eng., 8(3), 201-236, 2014
  • Jan., J.C., Hung, S.L., Chi, S.Y., Chern, J.C., Neural network forecast model in deep excavation., J. Comput. Civil Eng., 16(1), 59-65, 2002
  • Goh, A.T.C., Goh, S.H., Support vector machines: their use in geotechnical engineering as illustrated using seismic liquefaction data., Comput. Geotech. 34(5), 410-421, 2007
  • Ghaboussi, J., Pecknold, D.A., Zhang, M., Haj-Ali, R., Autoprogressive training of neural network constitutive models., Int. J. Numer. Methods Fluids, 42(1), 105-126, 1998
  • Hashash, Y.M.A., Marulanda, C., Ghaboussi, J., Jung, S., Systematic update of a deep excavation model using field performance data., Comput. Geotech., 30(6), 477-488, 2003
  • Hashash, Y.M.A., Marulanda, C., Ghaboussi, J., Jung, S., Novel approach to integration of numerical modeling and field observations for deep excavation., J. Geotech. Geoenviron. Eng., 132(8), 1019-1031, 2006
  • Song, H., Osouli, A., Hashash, Y., Soil behavior and excavation instrumentation layout, 7th International symposium on field measurements in geomechanics FMGM., Boston, MA., 2007
  • Yıldız, E., Ozyazıcıoglu, M.H, Ozkan, M.Y., Lateral Pressures on Rigid Retaining Walls: A Neural Network Approach., Gazi Univ. J. Science, 23(2), 201-210, 2010
  • Johari, A., Javadi, A.A., Najafi, H., A genetic-based model to predict maximum lateral displacement of retaining wall in granular soil., Scientica Iranica, 23(1), 54-65, 2016
  • Istanbul Metropolitan Municipality Department of Earthquake Risk Management and Urban Development Directorate of Earthquake and Ground Analysis, Microzonation work for the southern European side of Istanbul, Final Report, 1-88, 2007
  • Eroskay, S.O., Graywackes of Istanbul Region, Proceedings of International Symposium on Design of Supports to Deep Excavations., Turkish Group of Soil Mechanics, Bosphorus University, 41-44, 1985
  • Yıldırım, M., Tonaroğlu, M., Selçuk, M.E., Akgüner, C., Revised stratigraphy of the tertiary deposits of Istanbul and their engineering properties., B. Eng. Geol. Environ., 72(3-4), 413-420, 2013
  • Shahin, M., Intelligent computing for modeling axial capacity of pile foundations., Can. Geotech. J, 47(2):230–243, 2010
  • Öztemel,E., Yapay Sinir Ağları, Papatya Yayıncılık, 29-162, 2012
  • Haykin, S., Neural Networks: A Comprehensive Foundation, 1,23-71, 2001
  • Rojas, R., Neural Networks A systematic Introduction, Berlin, 151-184, 1996
  • Rummelhart, D.E., Hinton, G.E., Williams, R.J., Learning Internal representation by error propagation., J. Parallel Distrib. Comput. , 1(8): 318-362, 1986

Ayrıntılar

Birincil Dil İngilizce
Konular İnşaat Mühendisliği
Bölüm Makale
Yazarlar

Özgür YILDIZ (Sorumlu Yazar)
YILDIZ TECHNICAL UNIVERSITY
0000-0002-3684-3750
Türkiye


Mehmet M. BERİLGEN
YILDIZ TECHNICAL UNIVERSITY
0000-0001-6544-011X
Türkiye

Yayımlanma Tarihi 1 Temmuz 2020
Başvuru Tarihi 4 Aralık 2018
Kabul Tarihi 22 Nisan 2019
Yayınlandığı Sayı Yıl 2020, Cilt 31, Sayı 4

Kaynak Göster

Bibtex @araştırma makalesi { tekderg492280, journal = {Teknik Dergi}, issn = {1300-3453}, address = {}, publisher = {TMMOB İnşaat Mühendisleri Odası}, year = {2020}, volume = {31}, pages = {10147 - 10166}, doi = {10.18400/tekderg.492280}, title = {Artificial Neural Network Model to Predict Anchored-Pile-Wall Displacements on Istanbul Greywackes}, key = {cite}, author = {Yıldız, Özgür and Berilgen, Mehmet M.} }
APA Yıldız, Ö. & Berilgen, M. M. (2020). Artificial Neural Network Model to Predict Anchored-Pile-Wall Displacements on Istanbul Greywackes . Teknik Dergi , 31 (4) , 10147-10166 . DOI: 10.18400/tekderg.492280
MLA Yıldız, Ö. , Berilgen, M. M. "Artificial Neural Network Model to Predict Anchored-Pile-Wall Displacements on Istanbul Greywackes" . Teknik Dergi 31 (2020 ): 10147-10166 <https://dergipark.org.tr/tr/pub/tekderg/issue/44272/492280>
Chicago Yıldız, Ö. , Berilgen, M. M. "Artificial Neural Network Model to Predict Anchored-Pile-Wall Displacements on Istanbul Greywackes". Teknik Dergi 31 (2020 ): 10147-10166
RIS TY - JOUR T1 - Artificial Neural Network Model to Predict Anchored-Pile-Wall Displacements on Istanbul Greywackes AU - Özgür Yıldız , Mehmet M. Berilgen Y1 - 2020 PY - 2020 N1 - doi: 10.18400/tekderg.492280 DO - 10.18400/tekderg.492280 T2 - Teknik Dergi JF - Journal JO - JOR SP - 10147 EP - 10166 VL - 31 IS - 4 SN - 1300-3453- M3 - doi: 10.18400/tekderg.492280 UR - https://doi.org/10.18400/tekderg.492280 Y2 - 2019 ER -
EndNote %0 Teknik Dergi Artificial Neural Network Model to Predict Anchored-Pile-Wall Displacements on Istanbul Greywackes %A Özgür Yıldız , Mehmet M. Berilgen %T Artificial Neural Network Model to Predict Anchored-Pile-Wall Displacements on Istanbul Greywackes %D 2020 %J Teknik Dergi %P 1300-3453- %V 31 %N 4 %R doi: 10.18400/tekderg.492280 %U 10.18400/tekderg.492280
ISNAD Yıldız, Özgür , Berilgen, Mehmet M. . "Artificial Neural Network Model to Predict Anchored-Pile-Wall Displacements on Istanbul Greywackes". Teknik Dergi 31 / 4 (Temmuz 2020): 10147-10166 . https://doi.org/10.18400/tekderg.492280
AMA Yıldız Ö. , Berilgen M. M. Artificial Neural Network Model to Predict Anchored-Pile-Wall Displacements on Istanbul Greywackes. Teknik Dergi. 2020; 31(4): 10147-10166.
Vancouver Yıldız Ö. , Berilgen M. M. Artificial Neural Network Model to Predict Anchored-Pile-Wall Displacements on Istanbul Greywackes. Teknik Dergi. 2020; 31(4): 10147-10166.
IEEE Ö. Yıldız ve M. M. Berilgen , "Artificial Neural Network Model to Predict Anchored-Pile-Wall Displacements on Istanbul Greywackes", Teknik Dergi, c. 31, sayı. 4, ss. 10147-10166, Tem. 2020, doi:10.18400/tekderg.492280