Civil Engineering / İnşaat Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/13
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Article Citation - WoS: 13Citation - Scopus: 13Effect of Soil-Type and Fines Content on Liquefaction Resistance—shear-Wave Velocity Correlation(Taylor & Francis, 2020) Ecemiş, NurhanDirect measurement of shear-wave velocity, Vs, in the field to evaluate the liquefaction resistance of soils is an alternative or complement approach to penetration-based methods. However, the existing liquefaction assessment methods established on the Vs have uncertainties about how the fines content and soil-type change the relationship between Vs and liquefaction resistance. The first part of this paper discusses the existence of fines on the correlation between cone penetration resistance and Vs. The second part focuses on the liquefaction resistance that is construed over again using the simplified cone penetration test (CPT)-based liquefaction screening procedure in terms of Vs for three distinct ranges of non-/low plastic fines content <35% fines. The outcomes of the investigation indicate that for each fines content, the correlation between CRR and Vs1 is not unique; there is a significant scattering of the curves for different soil types. Finally, using the results of this investigation as well as the simplified CPT-based liquefaction screening method, a soil-type specific CRR–Vs1 relationship developed for the unbounded, very young (Holocene-age) soils. © 2018 Taylor & Francis Group, LLCArticle Citation - WoS: 17Citation - Scopus: 21The Use of Neural Networks for the Prediction of Cone Penetration Resistance of Silty Sands(Springer Verlag, 2017) Erzin, Yusuf; Ecemiş, NurhanIn this study, an artificial neural network (ANN) model was developed to predict the cone penetration resistance of silty sands. To achieve this, the data sets reported by Ecemis and Karaman, including the results of three high-quality field tests, namely piezocone penetration test, pore pressure dissipation tests, and direct push permeability tests performed at 20 different locations on the northern coast of the Izmir Gulf in Turkey, have been used in the development of the ANN model. The ANN model consisted of three input parameters (relative density, fines content, and horizontal coefficient of consolidation) and a single output parameter (normalized cone penetration resistance). The results obtained from the ANN model were compared with those obtained from the field tests. It is found that the ANN model is efficient in determining the cone penetration resistance of silty sands and yields cone penetration resistance values that are very close to those obtained from the field tests. Additionally, several performance indices such as the determination coefficient, variance account for, mean absolute error, root mean square error, and scaled percent error were computed to examine the performance of the ANN model developed. The performance level attained in the ANN model shows that the ANN model developed in this study can be employed for predicting cone penetration of silty sands quite efficiently.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.