Civil Engineering / İnşaat Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/13
Browse
4 results
Search Results
Article 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: 39Influence of Consolidation Properties on the Cyclic Re-Liquefaction Potential of Sands(Springer Verlag, 2015) Ecemiş, Nurhan; Demirci, Hasan Emre; Karaman, MustafaThe relative density can be used as the main indicator to assess the liquefaction resistance of clean sands. As relative density of the sand deposit increases significantly following the initial liquefaction, one should expect that the soil can improve its liquefaction resistance. However, earthquake records indicate that densified sand can be liquefied again (re-liquefied) at smaller cycles by the similar seismic loadings. This work aims to clarify the counterintuitive finding that, after the first liquefaction, the resulting significant increase in relative density (induced by settlements and variation of the water level) do not necessarily imply an increase in the number of loading cycles for re-liquefaction. In this paper, we present a series of experimental results concerning the cyclic liquefaction and the following re-liquefaction of clean sand deposits. The experimental setup is performed by a shaking table, transmitting one-degree of freedom transversal motion to the soil within the 1.5 m high laminar shear box. At four different seismic demands, the input excitation was imposed three times to examine the influence of the initial distributions of the relative density and the consolidation characteristics on the liquefaction potential of the sand. The re-liquefaction cycles of the sand, which previously experienced liquefaction under the same seismic loadings, show that post-liquefaction reconsolidation of the sand deposits affects the re-liquefaction resistance.Article Citation - WoS: 16Citation - Scopus: 38Time-Dependent Physicochemical Characteristics of Malaysian Residual Soil Stabilized With Magnesium Chloride Solution(Springer Verlag, 2016) Latifi, Nima; Rashid, Ahmad Safuan A.; Ecemiş, Nurhan; Tahir, Mahmood Md; Marto, AminatonThe effects of non-traditional additives on the geotechnical properties of tropical soils have been the subject of investigation in recent years. This study investigates the strength development and micro-structural characteristics of tropical residual soil stabilized with magnesium chloride (MgCl2) solution. Unconfined compression strength (UCS) and standard direct shear tests were used to assess the strength and shear properties of the stabilized soil. In addition, the micro-structural characteristics of untreated and stabilized soil were discussed using various spectroscopic and microscopic techniques such as X-ray diffractometry (XRD), energy-dispersive X-ray spectrometry (EDAX), field emission scanning electron microscopy (FESEM), Fourier transform infrared spectroscopy (FTIR) and Brunauer, Emmett and Teller (BET) surface area analysis. From the engineering point of view, the results indicated that the strength of MgCl2-stabilized soil improved noticeably. The degree of improvement was approximately two times stronger than natural soil after a 7-day curing period. The results also concluded the use of 5 % of MgCl2 by dry weight of soil as the optimum amount for stabilization of the selected soil. In addition, the micro-structural study revealed that the stabilization process modified the porous network of the soil. The pores of the soils had been filled by the newly formed crystalline compounds known as magnesium aluminate hydrate (M-A-H).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.