000			02838cam a2200337 a 4500
003			EG-GiCUC
005			20250223031226.0
008			150510s2014 ua dhb f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aM.Sc
099			_aCai01.13.05.M.Sc.2014.Ah.C
100	0		_aAhmed Gamal Eldin Mahmoud Mahgoub
245	1	0	_aCorrelations from cone penetrating test and standard penetrating test using artificial neural networks / _cAhmed Gamal Eldin Mahmoud Mahgoub ; Supervised Mostafa A. Abukiefa , Dahlia H. Hafez
246	1	5	_aاستخدام الشبكات العصبية لايجاد علاقات من تجربة المخروط الرملى وتجربة الاختراق القياسى
260			_aCairo : _bAhmed Gamal Eldin Mahmoud Mahgoub , _c2014
300			_a248 P. : _bcharts , facsimiles , maps ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Civil Engineering
520			_aIn-situ tests generally investigate a much greater volume of soil more quickly than possible for sampling and laboratory tests, and therefore they have the potential to realize both cost savings and increased statistical reliability for foundation design. The principle objective of this study is to demonstrate the feasibility of using artificial neural networks (ANNs) to predict different correlations from CPT and SPT results considering the uncertainties and non-linearity of the problem. ANN is used to predict CPT results from SPT results and to estimate the angle of internal friction and soil modulus of elasticity, which are important soil parameters, from CPT and SPT results considering the additional factors to improve the correlations. A large amount of field and experimental data including SPT/ CPT results, grain size distribution of different samples and a calculated data of overburden pressure was obtained. This data was used for the training and the validation of the neural network. A comparison has been made between the obtained results from artificial neural networks (ANNs) approach, and some common traditional correlations of predicting CPT results (qc) from SPT results (N), angle of internal friction and soil modulus of elasticity from SPT results and angle of internal friction and soil modulus of elasticity from CPT results with respect to the actual results of the collected data
530			_aIssued also as CD
653		4	_aCone penetrating test
653		4	_aGeneral regression neural network
653		4	_aStandard penetrating test
700	0		_aDahlia Hesham Hafez , _eSupervisor
700	0		_aMostafa Abdelhamid Abukiefa , _eSupervisor
856			_uhttp://172.23.153.220/th.pdf
905			_aNazla _eRevisor
905			_aSoheir _eCataloger
942			_2ddc _cTH
999			_c50865 _d50865