Improving deep neural network using hyper-parameters tuning in predicting the bearing capacity of shallow foundations

Ultimate bearing capacity is one of the most important parameters in designing shallow foundations. This study focused on developing a hybrid model using Random Search (RS) technique and Deep Neural Network (DNN) to predict the maximum bearing capacity of shallow foundations in sandy soil. The data...

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Autores principales:	Tuan Anh Pham, Huong-Lan Thi Vu, Hong-Anh Thi Duong
Formato:	article
Lenguaje:	EN
Publicado:	Tamkang University Press 2021
Materias:	deep neural network hyperparameters tuning shallow foundations sensitive analysis Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Physics QC1-999
Acceso en línea:	https://doaj.org/article/b5cb7580777d46e1ab8d2385aea55ed7
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id	oai:doaj.org-article:b5cb7580777d46e1ab8d2385aea55ed7
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spelling	oai:doaj.org-article:b5cb7580777d46e1ab8d2385aea55ed72021-11-23T17:29:23ZImproving deep neural network using hyper-parameters tuning in predicting the bearing capacity of shallow foundations10.6180/jase.202204_25(2).00122708-99672708-9975https://doaj.org/article/b5cb7580777d46e1ab8d2385aea55ed72021-11-01T00:00:00Zhttp://jase.tku.edu.tw/articles/jase-202204-25-2-0012https://doaj.org/toc/2708-9967https://doaj.org/toc/2708-9975Ultimate bearing capacity is one of the most important parameters in designing shallow foundations. This study focused on developing a hybrid model using Random Search (RS) technique and Deep Neural Network (DNN) to predict the maximum bearing capacity of shallow foundations in sandy soil. The data included 97 load tests on the steps that were used to train and test the model. This data is divided into two parts of the training data set (7%) and the testing set (30%) to build and validate the corresponding models. The performance of the final DNN model is comprehensively assessed with a random hyper-parameters DNN model developed independently using the same data. The values of performance evaluation measures such as R-squared (R2), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and the Variance Accounted For (VAF) are used to determine to get the performance of the DNN model in predicting the ultimate bearing capacity of shallow foundations. In addition, a parallel coordinate plot is utilized to show and evaluate the effect of hyperparameters combination on the performance of DNN model. Besides, a global sensitivity analysis technique was deployed to detect the most important input variables in predicting the ultimate bearing capacity of shallow foundations. This study can provide an effective tool to identify the ultimate bearing capacity of shallow foundations.Tuan Anh PhamHuong-Lan Thi VuHong-Anh Thi DuongTamkang University Pressarticledeep neural networkhyperparameters tuningshallow foundationssensitive analysisEngineering (General). Civil engineering (General)TA1-2040Chemical engineeringTP155-156PhysicsQC1-999ENJournal of Applied Science and Engineering, Vol 25, Iss 2, Pp 261-273 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep neural network hyperparameters tuning shallow foundations sensitive analysis Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Physics QC1-999
spellingShingle	deep neural network hyperparameters tuning shallow foundations sensitive analysis Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Physics QC1-999 Tuan Anh Pham Huong-Lan Thi Vu Hong-Anh Thi Duong Improving deep neural network using hyper-parameters tuning in predicting the bearing capacity of shallow foundations
description	Ultimate bearing capacity is one of the most important parameters in designing shallow foundations. This study focused on developing a hybrid model using Random Search (RS) technique and Deep Neural Network (DNN) to predict the maximum bearing capacity of shallow foundations in sandy soil. The data included 97 load tests on the steps that were used to train and test the model. This data is divided into two parts of the training data set (7%) and the testing set (30%) to build and validate the corresponding models. The performance of the final DNN model is comprehensively assessed with a random hyper-parameters DNN model developed independently using the same data. The values of performance evaluation measures such as R-squared (R2), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and the Variance Accounted For (VAF) are used to determine to get the performance of the DNN model in predicting the ultimate bearing capacity of shallow foundations. In addition, a parallel coordinate plot is utilized to show and evaluate the effect of hyperparameters combination on the performance of DNN model. Besides, a global sensitivity analysis technique was deployed to detect the most important input variables in predicting the ultimate bearing capacity of shallow foundations. This study can provide an effective tool to identify the ultimate bearing capacity of shallow foundations.
format	article
author	Tuan Anh Pham Huong-Lan Thi Vu Hong-Anh Thi Duong
author_facet	Tuan Anh Pham Huong-Lan Thi Vu Hong-Anh Thi Duong
author_sort	Tuan Anh Pham
title	Improving deep neural network using hyper-parameters tuning in predicting the bearing capacity of shallow foundations
title_short	Improving deep neural network using hyper-parameters tuning in predicting the bearing capacity of shallow foundations
title_full	Improving deep neural network using hyper-parameters tuning in predicting the bearing capacity of shallow foundations
title_fullStr	Improving deep neural network using hyper-parameters tuning in predicting the bearing capacity of shallow foundations
title_full_unstemmed	Improving deep neural network using hyper-parameters tuning in predicting the bearing capacity of shallow foundations
title_sort	improving deep neural network using hyper-parameters tuning in predicting the bearing capacity of shallow foundations
publisher	Tamkang University Press
publishDate	2021
url	https://doaj.org/article/b5cb7580777d46e1ab8d2385aea55ed7
work_keys_str_mv	AT tuananhpham improvingdeepneuralnetworkusinghyperparameterstuninginpredictingthebearingcapacityofshallowfoundations AT huonglanthivu improvingdeepneuralnetworkusinghyperparameterstuninginpredictingthebearingcapacityofshallowfoundations AT honganhthiduong improvingdeepneuralnetworkusinghyperparameterstuninginpredictingthebearingcapacityofshallowfoundations
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Improving deep neural network using hyper-parameters tuning in predicting the bearing capacity of shallow foundations

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