ПРОГНОЗ РЕЗУЛЬТАТОВ СТАНДАРТНОГО ПЕНЕТРАЦИОННОГО ТЕСТА (SPT N60) ПО ДАННЫМ СТАТИЧЕСКОГО ЗОНДИРОВАНИЯ (CPT) Model to Predict the Standard Penetration Test N60 Value from Cone Penetration Test Data

Wassel Al Bodour, Bashar K. Tarawneh, Yasmin Murad

Аннотация


Рассматривается вопрос об оценке эмпирической связи результатов статического зондирования (CPT) и стандартного пенетрационного испытания грунтоносом (SPT N60) с использованием процедур нейропрограммирования и алгоритма экспрессии генов. Исследованы около 140 пар данных CPT-SPT для песчаных, супесчаных, илистых и илисто-песчаных грунтов с использованием трех основных параметров: сопротивление зонда, боковое трение, давление переуплотнения. Показана высокая прогностическая результативность полученного корреляционного соотношения.

Полный текст статьи публикуется в английской версии журнала
«Soil Mechanics and Foundation Engineering”, vol.59, No.5


Литература


Alavi, H., and Gandomi, H., 2011. A robust data mining approach for formulation of geotechnical engineering systems, Engineering Computations, 28,(3), 242-274.

Alkroosh, I. and Ammash, H. 2015. Soft computing for modeling punching shear of reinforced concrete flat slabs, Ain Shams Engineering Journal, 6,(2), 439-448.

Ashteyat, A., Obaidat, Y., Murad, Y. 2019. Compressive Strength Prediction of Lightweight Short Columns at Elevated Temperatures using Gene Expression Programming and Artificial Neural Network, Journal of Civil Engineering and Management, ahead-of-print.

Bullock, P.J., Schmertmann, J.H., McVay, M.C. and Townsend, F.C. 2005. Side shear setup. II: results from Florida test piles, Journal of Geotechnical and Geoenvironmental Engineering, 131,(3), 301-310.

Ferreira, C., 2001. Gene expression programming: a new adaptive algorithm for solving problems, Complex Syst. 13, (2), 87–129.

Holloway, D.M. and Beddard, D.L. 1995. Dynamic testing results, indicator pile test program, I-880, Oakland, California. Deep Foundations Institute 20th Annual Members Conference and Meeting. Charleston, South Carolina, 105–126.

Kaydelen, C., 2011. Soil liquefaction modeling by genetic expression programming and neuro fuzzy, Expert Systems with Applications, 38,(4), 4080-4087.

Komurka, V.E., Winter, C.J., and Maxwell, S. 2005. Applying separate safety factors to end-of drive and set-up components of driven pile capacity. Geotechnical Applications for Transportation Infrastructure: Proceedings of the 13th Great Lakes Geotechnical and Geoenvironmental Conference, Milwaukee, Wis., 13 May 2005. Edited by H.H. Titi. Geotechnical Practice Publication 3, ASCE, Reston, VA, 65-80.

Long, J.H., Kerrigan, J.A. and Wysockey, M.H. 1999. Measured Time Effects for Axial Capacity of Driven Piling, Transportation Research Record 1663, Paper No.99-1183,

-15.

Mohanty, R., Suman, S., and Das, S. K., 2018. Prediction of vertical pile capacity of driven pile in cohesionless soil using artificial intelligence techniques, International Journal of Geotechnical Engineering, 12,(2), 209-216.

Mousavi, S. M., Aminian, P., Gandomi, A. H., Alavi, A. H., and Bolandi, H. 2012. A new predictive model for compressive strength of HPC using gene expression programming, Advances in Engineering Software, 45,(1), 105-114.

Murad, Y., Imam, R. Abu Hajar, H., Habeh, D., Hammad, A., and Shawash, Z. 2019. Predictive Compressive Strength Models for Green Concrete, International Journal of Structural Integrity, ahead-of-print, no. ahead-of-print.

Murad, Y, Ashteyat, A., Hunaifat, R. 2019. Predictive Model to the Bond Strength of FRP-to-Concrete under Direct Pullout using Gene Expression Programming, Journal of Civil Engineering and Management, ahead-of-print.

Nazari, A., and Riahi, S., 2011. Prediction split tensile strength and water permeability of

high strength concrete containing TiO2 nanoparticles by artificial neural network

and genetic programming. Composites Part B: Engineering, Engineering, 42,(3),

-488.

Pestana, J. M., Hunt, C. E., and Bray, J. D., 2002. Soil deformation and excess pore pressure field around a closed-ended pile. Journal of Geotechnical and Geoenvironmental Engineering, 128,(1), 1-12.

Shahin, M.A., Maier, H.R., and Jaksa, M.B., 2004. Data division for developing neural

networks applied to geotechnical engineering, Journal of Geotechnical and Geoenvironmental Engineering, ASCE, 18, (2), 105–114.

Skov, R., and Denver, H., 1988. Time-dependence of bearing capacity of piles. The 3rd International Conference on Application of Stress-wave Theory to Piles, Ottawa,

Canada, 879–888.

Soderberg, L.O. 1961. Consolidation theory applied to foundation pile time effects. Géotechnique, 11,(3), 217–225.

Sonmez, R., 2008. Parametric range estimating of building costs using regression models and

bootstrap, Journal of Construction Engineering and Management, 134,(12), 1011-1016.

Suman, S., Das, S. K., and Mohanty, R. 2016. Prediction of friction capacity of driven piles in clay using artificial intelligence techniques, International Journal of Geotechnical Engineering, 10, (5), 469-475.

Svinkin, M.R., 1996. Setup and relaxation in glacial sand-discussion. Journal of

Geotechnical and Geoenvironmental Engineering, ASCE, 122, (4), 319–321.

Svinkin, M.R., and Skov, R., 2000. Set-up effect of cohesive soils in pile capacity. In: The 6th International Conference on Application of Stress-wave Theory to Piles. Sao Paulo, Brazil, 107–111.

Tarawneh, B., 2013. Pipe pile setup: database and prediction model using artificial neural

network, Soils and Foundations, 53,(4), 607-615.

Tarawneh, B., 2017. Predicting standard penetration test N-value from cone penetration test data using artificial neural networks, Geoscience Frontiers,8,(1), 199-204.

Tarawneh, B. (2018) Gene expression programming model to predict driven pipe piles set- up, International Journal of Geotechnical Engineering, DOI: 10.1080/19386362.2018.1460964

Tarawneh, B., and Imam, R., 2014. Regression versus artificial neural networks: Predicting

Pile setup from empirical data, KSCE Journal of Civil Engineering, 18, (4), 1018.

Tarawneh, B., and Nazzal, M. D., 2014. Optimization of resilient modulus prediction from FWD results using artificial neural network, Periodica Polytechnica. Civil Engineering, 58,(2), 143.

Tatari, O., Sargand, S. M., Masada, T., and Tarawneh, B. 2013. Neural network approach to condition assessment of highway culverts: case study in Ohio, Journal of

Infrastructure Systems, 19,(4), 409-414.

Wang, S.T., and Reese, L.C., 1989. Predictions of the response of piles to axial loading.

Predicted and observed axial behavior of piles. Edited by R.J. Finno. Geotechnical

Special Publication 23, ASCE, Reston, VA, 173–187.

Yang, L., and Liang, R., 2006. Incorporating set-up into the reliability-based design of driven piles in clay, Canadian Geotechnical Journal, 43,(9), 946-955.


Ссылки

  • На текущий момент ссылки отсутствуют.