Civil Engineering / İnşaat Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/13
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Article Citation - WoS: 9Citation - Scopus: 8Effect of Drainage Conditions on Cpt Resistance of Silty Sand: Physical Model and Field Tests(Springer, 2023) Ecemis, Nurhan; Arık, Mustafa Sezer; Taneri, HazalThe influence of drainage conditions on cone penetration test (CPT) resistance and the excess pore pressure during cone penetration in sand and silty sand are examined using field and physical model tests. Drainage can generally occur in saturated clean sand and silty sand under certain conditions. This work aims to understand and explain the effect of sand and silty sand drainage conditions on CPT resistance and pore pressure through the coefficient of consolidation (c h) and penetration rate (v). The physical model test results indicate the significant effect of excess pore pressures and their dissipation rates, depending on the coefficient of consolidation (silt content) and the penetration rate on cone resistance. For the same relative density, normalized CPT resistance decreases as there is a reduction in c h (or an increase in silt content) or an increase in penetration rate. The difference in CPT resistance in silty sand is attributed to drainage conditions. Finally, the results revealed in this study and the field test data reported in the literature were combined to develop an equation for the effect of drainage conditions on excess pore water pressure and CPT resistance. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Article Citation - WoS: 34Citation - Scopus: 38The Use of Neural Networks for Cpt-Based Liquefaction Screening(Springer Verlag, 2014) Erzin, Yusuf; Ecemiş, NurhanThis study deals with development of two different artificial neural network (ANN) models: one for predicting cone penetration resistance and the other for predicting liquefaction resistance. For this purpose, cone penetration numerical simulations and cyclic triaxial tests conducted on Ottawa sand–silt mixes at different fines content were used. Results obtained from ANN models were compared with simulation and experimental results and found close to them. In addition, the performance indices such as coefficient of determination, root mean square error, mean absolute error, and variance were used to check the prediction capacity of the ANN models developed. Both ANN models have shown a high prediction performance based on the performance indices. It has been demonstrated that the ANN models developed in this study can be employed for predicting cone penetration and liquefaction resistances of sand–silt mixes quite efficiently.