Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Prediction of compressive and flexural strengths of jarosite mixed cement concrete pavements using artificial neural networks
In this paper, an attempt has been made to apply and compare the prediction capability of two variants of Artificial Neural Networks: feed-forward neural network (FFNN) and the radial basis function network (RBFN) for modelling the flexural and compressive strengths of jarosite mixed cement concrete for pavements. The compressive strength and the flexural strength are dependent upon a total of eight inputs. Their values are experimentally determined from the specimens containing 0%, 10%, 15%, 20% and 25% of the jarosite as a partial replacement to cement in M40 concrete mix. These specimens had undergone a curing for 3, 7, 28, 90, 180 and 365 days providing the inputs–outputs experimental data for the 90 observations. The simulation experiments showed that the performance of FFNN is found to be better than that of the RBFN model as the former model provided lesser values of the performance indicators such as Mean Average Error and Mean Squared Error. Further, the FFNN model required a lesser number of parameters to be tuned during the training as compared to the RBFN model.
Prediction of compressive and flexural strengths of jarosite mixed cement concrete pavements using artificial neural networks
In this paper, an attempt has been made to apply and compare the prediction capability of two variants of Artificial Neural Networks: feed-forward neural network (FFNN) and the radial basis function network (RBFN) for modelling the flexural and compressive strengths of jarosite mixed cement concrete for pavements. The compressive strength and the flexural strength are dependent upon a total of eight inputs. Their values are experimentally determined from the specimens containing 0%, 10%, 15%, 20% and 25% of the jarosite as a partial replacement to cement in M40 concrete mix. These specimens had undergone a curing for 3, 7, 28, 90, 180 and 365 days providing the inputs–outputs experimental data for the 90 observations. The simulation experiments showed that the performance of FFNN is found to be better than that of the RBFN model as the former model provided lesser values of the performance indicators such as Mean Average Error and Mean Squared Error. Further, the FFNN model required a lesser number of parameters to be tuned during the training as compared to the RBFN model.
Prediction of compressive and flexural strengths of jarosite mixed cement concrete pavements using artificial neural networks
Gupta, Tanvi (Autor:in) / Sachdeva, S.N. (Autor:in)
Road Materials and Pavement Design ; 22 ; 1521-1542
03.07.2021
22 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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