The Optimal ANN Model for Predicting Bearing Capacity of Shallow Foundations trained on Scarce Data

Abstract

This study is focused on determining the potential of using deep neural networks (DNNs) to predict the ultimate bearing capacity of shallow foundation in situations when the experimental data which may be used to train networks is scarce. Two experiments involving testing over 17000 networks were conducted. The first experiment was aimed at comparing the accuracy of shallow neural networks and DNNs predictions. It shows that when the experimental dataset used for preparing models is small then DNNs have a significant advantage over shallow networks. The second experiment was conducted to compare the performance of DNNs consisting of different number of neurons and layers.Obtained results indicate that the optimal number of layers varies between 5 to 7. Networks with less and -surprisingly -more layers obtain lower accuracy. Moreover, the number of neurons in DNN has a lower impact on the prediction accuracy than the number of DNN's layers. DNNs perform very well, even when trained with only 6 samples. Basing on the results it seems that when predicting the ultimate bearing capacity with ANN models obtaining small but high-quality experimental training datasets instead of large training datasets affected by a higher error is an advisable approach.

Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest

“…Accurately predicting the axial bearing capacity of pile is of crucial importance because of many possible advantages and contributions to foundation engineering. Numerical or experimental approaches in the available literature still face some limitations, for instance, the lack of dataset samples (Momeni et al [44] with 36 samples; Bagińska and Srokosz [45] with 50 samples; Teh et al [46] with 37 samples), accuracy assessment and improvement of the ML algorithms or comparison with classical prediction methodologies. Therefore, the contribution of the present work could be highlighted through the following ideas: (i) the largest dataset, to the best of the author's knowledge, was used for the construction of ML models, including 2314 experimental tests; (ii) a comparison of two ML algorithms, namely ANN and RF, was conducted and compared with classical MVR and five formulas in the literature to fully assess the prediction performance of each approach; (iii) the performance of ML algorithms was evaluated under the presence of random splitting dataset, which could truly find out the efficiency of ML algorithms; and (iv) a sensitivity analysis was performed to reveal the role of each input parameters in predicting the axial bearing capacity of piles.…”

Design deep neural network architecture using a genetic algorithm for estimation of pile bearing capacity

“…The numerical or experimental methods in the existing literature still have some limitations, such as lack of data set samples (Marto et al [55] with 40 samples; Momeni et al [45] with 36 samples; Momeni et al [56] with 150 samples; Bagińska and Srokosz [57] with 50 samples; Teh et al [58] with 37 samples), refinement of ML approaches or failure to fully consider key parameters which affects the predicting results of the model. For this, the contribution of the present work can be marked through the following ideas: (i) large data set, including 472 experimental tests; (ii) reduce the input variables from 10 to 4 which help the model achieve more accurate results with faster training time, (iii) automatically design the optimal architecture for the DLNN model, all key parameters are considered, include: the number of hidden layers, the number of neurons in each hidden layer, the activation function and the training algorithm.…”

“…[ 55 ] with 40 samples; Momeni et al [ 45 ] with 36 samples; Momeni et al . [ 56 ] with 150 samples; Bagińska and Srokosz [ 57 ] with 50 samples; Teh et al . [ 58 ] with 37 samples), refinement of ML approaches or failure to fully consider key parameters which affects the predicting results of the model.…”

“…With a particular interest in a recently developed Deep Learning Neural Network (DLNN), which has gained tremendous success in many areas of application [50][51][52][53][54], the main objective of this study is dedicated to the development of a novel hybrid ML algorithm using DLNN and GA to predict the axial load capacity of driven piles. For this aim, a dataset consisting of 472 pile load test reports from the construction sites of Ha Nam-Vietnam was gathered.…”

Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging

“…Such networks can achieve a high level of accuracy without requiring large amounts of training data. The accuracy of prediction seems to be more dependent on the number of layers in the neural network than the number of neurons [37]. For these reasons, among the various machine learning methods that use neural networks, this study utilized an ANN for the prediction of the soil water content.…”