An extreme learning machine model for geosynthetic-reinforced sandy soil foundations

Abstract

In the past, several experimental and theoretical studies have been carried out to evaluate the ultimate bearing capacity (UBC) of geosynthetic-reinforced sandy soil foundations (GRSSFs). The experimental studies consist of model footing load tests which are expensive and time consuming whereas the results obtained by theoretical expressions often lack consistency. In the study reported in this paper, a cost-effective, extreme learning machine (ELM) model was used for the first time to obtain a more realistic prediction of the UBC of a GRSSF. A large dataset consisting of actual field and laboratory measurements of UBC was used to develop and validate the model. Its predictive performance was then compared against robust machine learning regression models and traditional theoretical methods. The study shows that the proposed model is useful and attains an adequate level of accuracy in predicting the UBC of GRSSFs when compared with other data-driven models and some traditional methods. The research also shows that the ELM technique is a realistic and reliable approach that could be employed in geotechnical engineering intelligent systems for the prediction of multivariate non-linear problems.

Performance Analysis of Multi-Spindle Drilling of Al2024 with TiN and TiCN Coated Drills Using Experimental and Artificial Neural Networks Technique

“…A multilayer perceptron (MLP) neural network was employed to establish the relationship between the input and output parameters using a single hidden layer. MLP is the most commonly used network, among the diverse types of ANNs used by various researchers [49,50]. The MLP consists of three layers, namely input, hidden and output layers.…”

“…The tangent-sigmoid activation function is applied between the input and hidden layers given mathematically as [48]: It is noteworthy that data normalisation is important before feeding it to any machine learning model so that each variable receives the same attention [49]. Therefore, for this study, all parameters were normalised between −1 to 1 using the following relationship.…”

“…In this study, the widely used and well-established ratio of 80:20 was used to divide the data into training and testing subsets, respectively. As discussed earlier, the training data are used to construct the model network and the testing data are used for validating the performance of the network [49]. The network uses the training data to learn and map the hidden relationships.…”

“…For this study, three statistical indices were utilised to assess the accuracy of the developed model: (1) root mean square error (RMSE); (2) mean absolute per cent deviation (MAPD); and (3) coefficient of determination (R 2 ). All these indices are predominantly used for evaluating the accuracy of any data-driven modelling technique [49]. 12)…”

Rock Strain Prediction Using Deep Neural Network and Hybrid Models of ANFIS and Meta-Heuristic Optimization Algorithms

“…The performance of the developed models was assessed using eight statistical parameters, namely, determination coefficient (R 2 ), root mean square error (RMSE), variance account for (VAF), performance index (PI) Willmott's index of agreement (WI), mean absolute error (MAE), mean bias error (MBE) and mean absolute percentage error (MAPE) [50][51][52][53][54][55][56][57][58]. The mathematical expressions of the aforementioned indices can be given by:…”