Abstract
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.
Keywords
Artificial neural network Anchored pile walls Finite element method Wall displacement Istanbul greywackes
References
- 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
Abstract
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.
Keywords
Artificial neural network Anchored pile walls Finite element method Wall displacement Istanbul greywackes
References
- 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
Details
| Primary Language | English |
|---|---|
| Subjects | Civil Engineering |
| Journal Section | Articles |
| Authors | |
| Publication Date | July 1, 2020 |
| Submission Date | December 4, 2018 |
| Published in Issue | Year 2020 Volume: 31 Issue: 4 |
