Evolution of Deep Neural Network Architecture Using Particle Swarm Optimization to Improve the Performance in Determining the Friction Angle of Soil

Abstract

This study focuses on the use of deep neural network (DNN) to predict the soil friction angle, one of the crucial parameters in geotechnical design. Besides, particle swarm optimization (PSO) algorithm was used to improve the performance of DNN by selecting the best structural DNN parameters, namely, the optimal numbers of hidden layers and neurons in each hidden layer. For this aim, a database containing 245 laboratory tests collected from a project in Ho Chi Minh city, Vietnam, was used for the development of the proposed hybrid PSO-DNN model, including seven input factors (soil state, standard penetration test value, unit weight of soil, void ratio, thickness of soil layer, top elevation of soil layer, and bottom elevation of soil layer) and the friction angle was considered as the target. The data set was divided into three parts, namely, the training, validation, and testing sets for the construction, validation, and testing phases of the model. Various quality assessment criteria, namely, the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE), were used to estimate the performance of PSO-DNN models. The PSO algorithm showed a remarkable ability to find out an optimal DNN architecture for the prediction process. The results showed that the PSO-DNN model using 10 hidden layers outperformed the DNN model, in which the average correlation improvement increased R2 by 1.83%, MAE by 5.94%, and RMSE by 8.58%. Besides, a global sensitivity analysis technique was used to detect the most important inputs, and it showed that, among the seven input variables, the elevation of top and bottom of soil played an important role in predicting the friction angle of soil.

Application of Feedforward Neural Network and SPT Results in the Estimation of Seismic Soil Liquefaction Triggering

“…To avoid the overfitting problem of the model to the entire dataset, many techniques have been proposed to apply, such as using validation set [ 18 , 19 ], K-fold CV [ 26 ], etc. In particular, the K-fold CV technique is commonly used in machine learning fields when the dataset size is limited.…”

“…In a machine learning environment, optimization algorithms are indispensable to enhance performance or find the best model. The family of optimization algorithms can be divided into several categories, such as gradient descent algorithms [ 27 ], evolutionary algorithms [ 18 , 28 ], swarming algorithms [ 19 , 29 ], and random or grid search algorithms [ 30 ]. Among the above optimization techniques, the random search (RS) technique gives simple and good enough efficiency [ 30 , 31 ].…”

Developing random forest hybridization models for estimating the axial bearing capacity of pile

“…However, it must be said that ML models have a very complex architecture, including many hyperparameters. These hyperparameters are particularly sensitive and greatly affect the model’s forecast results [ 11 , 30 – 32 ]. The above studies did not show that how the model architecture model is selected to predict the pile load capacity.…”

Prediction of California Bearing Ratio (CBR) of Stabilized Expansive Soils with Agricultural and Industrial Waste Using Light Gradient Boosting Machine

“…Especially, the ML model has high generality and accuracy when the model uses large samples in training the model. Therefore, ML models have been applied to solve many problems in civil engineering such as determination of pile bearing capacity [5], [6], unconfined compressive strength of stabilized soil [7], compressive strength of concrete [8], [9], etc. Therefore, the ML models have been developed in determining the CBR of stabilized expansive soils.…”