The use of neural networks for the prediction of cone penetration resistance of silty sands

Abstract

In 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.

Consolidation Coefficient of Soil Prediction by Using Teaching Learning based Optimization with Fuzzy Neural Network

“…In their investigation and comparison of ANN [25] and decision trees (DT) for evaluating sound soil strength [26] investigating the use of Operational Networks for determining clay's residual strength and results demonstrate that the proposed method performs better than the ANN but worse than the (SVM Support Vector Machine). Using probabilistic neural networks, Kiran et al [27] estimated the soil's shear strength parameters such as cohesion (c) and internal friction angle (φ) and PNN is effective in calculating the soil shear strength. Erzin and Ecemis [28] used the ANN to successfully predict the conical friction coefficient of fine sand soils.…”

CPT Data Interpretation Employing Different Machine Learning Techniques

“…This process is performed until a minimum error is reached or the incremental improvement between iterations reaches zero. In recent research, ANNs have been used to classify soils from CPT data [3], identify soil parameters [24] or estimate the cone resistance of a cone penetration test [25].…”

Artificial Neural Network Trained to Predict High-Harmonic Flux

“…In this respect, artificial neural networks (ANN) are known to be high speed mathematical models that are capable of solving linear and non-linear multivariate regression problems [24,25], being generally used for their excellent prediction capabilities [26,27] but also for their capacity to solve problems such as classification [28,29], pattern recognition [30] or system control [31][32][33]. Inspired by the human brain, these numerical modelling techniques are able to gain high efficiency and accuracy in the presence of uncertainties due to their ability to learn from experience [34]. Based on these properties and also on their effectiveness in processing large amount of data, the ANNs have applicability in various fields such as medicine [35], engineering [36,37], science technology [38], nanotechnology [39,40] or physics [41].…”