Prediction of the unconfined compressive strength of compacted granular soils by using inference systems |
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Authors: | Ekrem Kalkan Suat Akbulut Ahmet Tortum Samet Celik |
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Institution: | (1) Oltu Vocational Training School, Ataturk University, 25240 Erzurum, Turkey;(2) Civil Engineering Department, Engineering Faculty, Ataturk University, 25240 Erzurum, Turkey |
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Abstract: | Adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) models have been extensively used to predict
different soil properties in geotechnical applications. In this study, it was aimed to develop ANFIS and ANN models to predict
the unconfined compressive strength (UCS) of compacted soils. For this purpose, 84 soil samples with different grain-size
distribution compacted at optimum water content were subjected to the unconfined compressive tests to determine their UCS
values. Many of the test results (for 64 samples) were used to train the ANFIS and the ANN models, and the rest of the experimental
results (for 20 samples) were used to predict the UCS of compacted samples. To train these models, the clay content, fine
silt content, coarse silt content, fine sand content, middle sand content, coarse sand content, and gravel content of the
total soil mass were used as input data for these models. The UCS values of compacted soils were output data in these models.
The ANFIS model results were compared with those of the ANN model and it was seen that the ANFIS model results were very encouraging.
Consequently, the results of this study have important findings indicating reliable and simple prediction tools for the UCS
of compacted soils. |
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