| dc.relation.references | A. Papon, Y. R.-Y. (2012). Single- and multi-objective genetic algorithm optimization for identifying soil parameters. International Journal for Numerical and Analytical Methods in Geomechanics, 597-618.
Baecher, G. B., & Christian, J. T. (2003). Reliability and Statistics in Geotechnical Engineering. Wiley.
Benson Hsiung, B.-C., Yang, K.-H., Aila, W., & Ge, L. (2018). Evaluation of the wall deflections of a deep excavation in Central Jakarta using three-dimensional modeling. Tunnelling und Underground Space Technology, 84-96.
Berthold, M., & Hand, D. (2007). Intelligent Data Analysis, An Introduction. Berlin: Springer.
Binkgreve, R. B. (2005). Selection of Soils Models and parameter for geotechnical engineering application. En J. Yamamuro, & V. Kaliakin, Soil Constitutive Models (págs. 69-98). Reston,Virginia: ASCE.
Brinkgreve, D. U. (2018). PLAXIS 2018. Netherlands.
Brinkhoff, T. (5 de Noviembre de 2018). INDONESIA: Urban Population of Cities. Obtenido de INDONESIA: Urban Population of Cities: http://www.citypopulation.de/Indonesia-MU.html
Butkovich, J. N., & Hashash, Y. M. (2007). Neural network material model enhancement: Optimization through selective data removal. Cambridge Journal,, 61-72.
Calvello, M., & Finno, R. (2004). Selecting parameters to optimize in model calibration by inverse analysis. Computers and Geotechnics, 411-425.
Chua, C. G., & Goh, A. T. (2005). Estimating wall deflections in deep excavations using Bayesian neural networks. Tunnelling and Underground Space Technology, 400–409.
Cruz, P. P. (2010). Inteligencia Artificial con Aplicaciones a la Ingenieria. Mexico D.F: Alfaomega.
Deb, K. (2011). Multi-Objective Optimization Using Evolutionary Algorithms: An Introduction. KanGAL Report Number 2011003.
DeJong, K. (1975). An analysis of the behavior of a class of genetic adaptative systems. University of Michigan.
Hashash, Y. M., Song , H., & Oso, A. (2011). Three-dimensional inverse analyses of a deep excavation in Chicago clays. Int. J. Numer. Anal. Meth. Geomech., 1059-1075.
Hashash, Y., Marulanda, C., Ghaboussi, J., & Jung, S. (2006). Novel Approach to Integration of Numerical Modeling and Field Observations for Deep Excavations. Journal of Geotechnical and Geoenviromental Engineering, 1019-10.
Hill, R. (1950). The Mathematical Theory of Plasticity. Londres, UK: Oxford University.
Holland, J. (1992). Adaptation in natural and artificial systems : an introductory analysis with applications to biology, control and artificial intelligence. Bradford Books edition.
Honjo, Y., Wen-Tsung, L., & Guha, S. (1994). Inverse analysis of an embankment on soft clay by extended bayesian method. International Journal for Numerical and Analytical Methods in Geomechanics, 709-734.
Hsiung. (2009). A case study on the behavior of a deep excavation in sand. Comput. Geotechn. 36, 665-675.
Hsiung, B. C., Yang, K. H., Aila, W., & Ge, L. (2017). Evaluation of the wall deflections of a deep excavation in Central Jakarta using three-dimensional modeling. Tunnelling and Underground Space Technology, 84-96.
Hsiung, B., & Hwang, R. N. (2009). Evaluating Performance of Diaphragm Wall by Wall Deflection Path. SEAGS. Special Issue of Excavation and Tunneling in Geotechnical Engineering, 81-90.
Jacky, J. (1944). The coefficient of earth pressure at rest. J. Soc Hungarian Archt. Eng Bundapest, 355-358.
Janda , T., Šejnoha , M., & Šejnoha, J. (2018). Applying Bayesian approach to predict deformations during tunnel construction. Numerical and analytical method in geomechanics, 1765-1784.
Japan, A. I. (2001). Recommendations of Design of Building Foundation. Japon.
Kulhawy, F. H., & Mayne, P. W. (1990). Manual on Estimating Soil Propierties for fundation design. Electric Power Research Institute.
Levasseur, S. (2008). Analyse Inverse en Géotechnique; développement d'une méthode a base d'algorithmes génétiques. Grenoble.
Levasseur, S., Malecot , Y., Boulon , M., & Flavigny, E. (2009). Statistical inverse analysis based on genetic algorithm and principal component analysis: Applications to excavation problems and pressuremeter tests. Int. J. Numer. Anal. Meth. Geomech, 1485-1511.
Levasseur, S., Malécot, Y., Boulon, M., & Flavigny, E. (2008). Soil parameter identification using a genetic algorithm. Numerical and Analytical Methods in Geomechanics.
LI, X., & otros, y. (2006). Intelligent Back Analysis of Tunnel Rock Displacement and Its Application. Underground Space.
Lin, H. D., Dang, P. H., & Hsieh, Y. M. (2015). RIFT: Robust and interpolation-free technique for objective functions in geotechnical inverse analysis. Computers and Geotechnics, 96-104.
Ling, X., Zhang, F., Zhu, Z., & Tang, L. (2008). Estimating mechanics parameters of rock mass based on improved genetic algorithm. IEEE.
Linlong, M., Finno, R. J., Huang, M., Kim, T., & Kern, K. (2015). Defining the soil parameters for computing deformations caused by braced excavation. Maejo Int. J. Sci. Technol, 165-180.
Magnan. (2001). Optimisation et algorithmes génétiques,.
Mayne, P., & Kulhawy, F. (1982). Ko-OCR relationships in soils. J. Geotechn Eng. Divis. 108 (GT6), 851-872.
Muir-Wood. (2004). Geotechnical Modelling. Bristol.
Muir-Wood, A. (1983). Index properties and critical state soil mechanics. Proceeding of the Symposium on Recent Developments in Laboratory and Field Tests Tests and Analysis of Geotechnical Problems.
Ou. (2006). Deep Excavation:Theory and Practice. Holanda: Taylor & Francis.
Ou, C.-Y. (2006). Deep Excavation Theory and practice. Taylor & Francis.
Pacheco, A., & Hidalgo, C. A. (2011). Herramientas para analisis por confiabilidad en geotecnia:La teoria. Ingenierias Universidad de Medellin, 70-77.
Padmanabhan, G., Sasikala, G., & Ravisankar, A. (2018). Site characterization for deep excavation and evaluation of stiffness properties of backfilled soil from field instrumentation. Innovative Infrastructure Solutions.
Pal, S., Wathugala, W., & Kundu, S. (1996). Calibration of a constitutive model using genetic algorithms. Computers and Geotechnics, 325-348.
Patel. (2005). La Méthode Observationnelle en Géotechnique. ICE Manual of Geotechnical Engineering, 1489-1501.
Patel, D. (2005). La Méthode Observationnelle en Géotechnique. En ICE, ICE Manual of Geotechnical Engineering (págs. 1489-1501).
Patel, D. (2005). La Méthode Observationnelle en Géotechnique. En ICE, ICE Manual of Geotechnical Engineering (págs. 1489-1501).
Peck. (1969). Advantages and limitations of the observational Method in applied soil mechanic. Geotechnique 19, 71-187.
Peck, R. B. (1969). Advantages and limitations of the observtional Method in applied soil mechanic. Geotechnique 19, 71-187.
Ponce Cruz, P. (2010). Inteligencia Artificial con aplicaciones a la Ingenieria. Ciudad de Mexico: Alfaomega.
Prästings, A., Müller, R., & Larsson, S. (2014). The observational method applied to a high embankment founded on sulphide clay. Engineering Geology, 112-123.
Puller, M. (2003). Deep excavation a practical manual. New York: Thomas Telford.
Rahardjo. (2017). Different Methods of Exacvation for Basement in Jakarta Design, Reality and Associated Problems. Proceeding of the 2 Intrnational Symposium on Asian Urban GeoEngineering.
Rahardjo. (2017). Different Methods of Excavation for Basement in Jakarta Design, Reality and Associated Problems. Proceeding of the 2nd International Sympsosium on Asian Urban GeoEnginnering.
Rahardjo. (2017). Different Methods of Excavation for Basement in Jakarta:Design, Reality and Associated Problems. Proceeding of the 2nd International Sympsosium on Asian Urban GeoEnginnering.
Rahardjo, P. P. (2016). CPTu in consolidating soils. Proceeding 5th International Conference on Site Characterization. ISSMGE.
Samarasinghe, S. (2007). Neuronal Networks for Applied Sciences and Engineering. Boca Raton: Taylor & Frances Group.
Sambridge, M., & Mosegaard, K. (2002). Monte Carlo Methods in Geophysical Inverse Problems. Reviews of Geophysics.
Sena-Cruz, J., Moreira, N. R., Miranda, T., Pinheiro , M. M., Fernandes, P. M., Dias, D., & Costa, L. (2013). Back analysis of geomechanical parameters in underground works using an Evolution Strategy algorithm. Tunnelling and Underground Space Technolog, 143-158.
Smith, I., & Griffiths, D. (1982). Programming the Finite Element Method. Chiester,U.K: Jhon Wilie & Sons.
Sun, Y., Jiang, Q., Yin, T., & Zhou, C. (2018). A back-analysis method using an intelligent multi-objective optimization for predicting slope deformation induced by excavation. Engineering Geology.
Valliappan, S. (1981). Continuum Mechanics Fundamentals. Roterdam: A.A Balkema.
Wood, M. (1983). Index propierties and critical state soil mechanics. Proceeding of the Symposium on Recent Developments in Laboratory and Field Tests Tests and Analysis of Geotechnical Problems.
Wrobel, L., & Militiadou, P. (2004). Genetic algorithms for inverse cathodic protection problem. Engineering analysis with Boundary Elements, 267-277.
Yamamuro, & Kaliakin. (2005). Soil Constitutive Models Evaluation, Selection, and Calibration. Reston, Virginia: American Society of Civil Engineers.
Yamamuro, J., & Kaliakin, V. (2005). Soil Constitutive Models Evaluation Selection and Calibration. Austin, Texas: American Society of Civil Enginnering.
Yao, L., & Li, J. (2005). Parameter Identification of 3D groundwater Ffow model with improved genetic algorithm. Chinese Journal of Rock Mechanics and Engineering.
Yin, Z.‐Y., Jin, Y.‐F., Shen, Pierr, J. S., & Hicher, P. Y. (2017). Optimization techniques for identifying soil parameters in geotechnical engineering: Comparative study and enhancement. International Journal for Numerical and Analytical Methods in Geomechanics.
Yong, K. (2015). . Learning lessons from the construction of Singapore Downtown line (DTL). Proceedings of International Conference and Exhibition on Tunneling and Underground Space.
Yuzhen, Y., Bingyin, Z., & Huina, Y. (2007). An intelligent displacement back-analysis method for earth-rockfill dams. Computers and Geotechnics, 423-434.
Zhang, L. L., Zhang, J., Zhang, L. M., & Tang, W. H. (2010). Back analysis of slope failure with Markov chain Monte Carlo simulation. Computers and Geotechnics, 905-912.
Zhou, C. B., Liu, W., Chen, Y. F., Hu, R., & Weia, K. (2015). Inverse modeling of leakage through a rockfill dam foundation during its construction stage using transient flow model, neural network and genetic algorithm. Engineering Geology, 183-195. |
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