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Experimental Study and Machine Learning Aided Modelling of the Mechanical Behaviour of Rammed Earth by Navid Kardani; Annan Zhou; Xiaoshan Lin; Majidreza Nazem is a Engineering article available to read on EtoBox.

What is Experimental Study and Machine Learning Aided Modelling of the Mechanical Behaviour of Rammed Earth about?

metrics including the root mean squared error, variance accounted for and R-squared value (R 2 ) are used to measure the performance of the applied model. Comparisons between simulations and experiments show that the developed XGBoost algorithm is a promising alternative in modelling key mechanical properties and entire stress-strain relationships for rammed earth. For stress-strain relationships calculated R-squared value for the training set is 0.978 and that for the testing dataset is 0.908. The key factor that most significantly affects the peak strength, residual strength, initial modulus, secant modulus and entire stress-strain relationships for rammed earth can be identified by using the developed soft computing method.

Who reads Experimental Study and Machine Learning Aided Modelling of the Mechanical Behaviour of Rammed Earth?

Author

Navid Kardani; Annan Zhou; Xiaoshan Lin; Majidreza Nazem

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Engineering (Physical Sciences)