caa a22 4500 605453772 CHVBK 20210128100203.0 cr unu---uuuuu 210128e20150201xx s 000 0 eng 10.1007/s10064-014-0606-8 doi (NATIONALLICENCE)springer-10.1007/s10064-014-0606-8 The use of neural networks for CPT-based liquefaction screening [Elektronische Daten] [Yusuf Erzin, Nurhan Ecemis] This 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. Springer-Verlag Berlin Heidelberg, 2014 Artificial neural networks nationallicence Cone penetration resistance nationallicence Liquefaction resistance nationallicence Ottowa sand nationallicence Erzin Yusuf Department of Civil Engineering, Faculty of Engineering, Celal Bayar University, 45140, Manisa, Turkey aut Ecemis Nurhan Department of Civil Engineering, Faculty of Engineering, Izmir Institute of Technology, 35430, Izmir, Turkey aut Bulletin of Engineering Geology and the Environment Springer Berlin Heidelberg 74/1(2015-02-01), 103-116 1435-9529 74:1<103 2015 74 10064 https://doi.org/10.1007/s10064-014-0606-8 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10064-014-0606-8 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Erzin Yusuf Department of Civil Engineering, Faculty of Engineering, Celal Bayar University, 45140, Manisa, Turkey aut NATIONALLICENCE 700 1- Ecemis Nurhan Department of Civil Engineering, Faculty of Engineering, Izmir Institute of Technology, 35430, Izmir, Turkey aut NATIONALLICENCE 773 0- Bulletin of Engineering Geology and the Environment Springer Berlin Heidelberg 74/1(2015-02-01), 103-116 1435-9529 74:1<103 2015 74 10064