Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Hybrid and optimized neural network models to estimate the elastic modulus of recycled aggregate concrete
In the present study, several hybrids and coupled forms of machine learning algorithms were developed to provide accurate elastic modulus of recycled aggregate concrete’s (ERAC) estimation, called multilayer perceptron neural networks (MLPNN). For this gain, a comprehensive dataset was collected from literature containing 400 rows of samples. Networks with different hidden layer numbers were used to develop MLPNN (from one layer to three layers). The accuracy of MLPNN deeply related to neuron numbers of each hidden layer, wherein the present paper, three optimization algorithms; the arithmetic optimization algorithm (AOA), equilibrium optimizer (EO), and flow direction algorithm (FDA); are merged with MLPNN to obtain the ideal value of neuron numbers. Considering all models, all developed models have the acceptable ability for predicting the ERAC by the coefficient of determination of at least 0.9306 for the learning stage and 0.9411 for the examining stage. All in all, the MLPNN model with two numbers of hidden layers with a structure of 17-14-1 optimized with AOA can be proposed as the most appropriate model. The created MLPNN models may optimize material selection and quality control in building processes, reducing dependence on natural aggregates.
Hybrid and optimized neural network models to estimate the elastic modulus of recycled aggregate concrete
In the present study, several hybrids and coupled forms of machine learning algorithms were developed to provide accurate elastic modulus of recycled aggregate concrete’s (ERAC) estimation, called multilayer perceptron neural networks (MLPNN). For this gain, a comprehensive dataset was collected from literature containing 400 rows of samples. Networks with different hidden layer numbers were used to develop MLPNN (from one layer to three layers). The accuracy of MLPNN deeply related to neuron numbers of each hidden layer, wherein the present paper, three optimization algorithms; the arithmetic optimization algorithm (AOA), equilibrium optimizer (EO), and flow direction algorithm (FDA); are merged with MLPNN to obtain the ideal value of neuron numbers. Considering all models, all developed models have the acceptable ability for predicting the ERAC by the coefficient of determination of at least 0.9306 for the learning stage and 0.9411 for the examining stage. All in all, the MLPNN model with two numbers of hidden layers with a structure of 17-14-1 optimized with AOA can be proposed as the most appropriate model. The created MLPNN models may optimize material selection and quality control in building processes, reducing dependence on natural aggregates.
Hybrid and optimized neural network models to estimate the elastic modulus of recycled aggregate concrete
Mingke Zheng (Autor:in) / Jinzhao Yin (Autor:in) / Lei Zhang (Autor:in) / Lihua Wu (Autor:in) / Hao Liu (Autor:in)
2025
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
Using artificial neural networks for predicting the elastic modulus of recycled aggregate concrete
| British Library Online Contents | 2013
Using artificial neural networks for predicting the elastic modulus of recycled aggregate concrete
| Online Contents | 2013
A Practical Equation for the Elastic Modulus of Recycled Aggregate Concrete
| DOAJ | 2022
| DOAJ | 2021
Resilient Modulus of Recycled Asphalt Pavement and Recycled Concrete Aggregate
| British Library Conference Proceedings | 2012