Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Multi-objective optimization of concrete mixture proportions using machine learning and metaheuristic algorithms
https://orcid.org/0000-0001-9241-9367
https://orcid.org/0000-0003-4013-7215
Highlights BPNN has good prediction accuracy for UCS, while RF performs better in predicting slump. PSO is efficient in tuning hyperparameters of machine learning models. The Pareto front of the mixture optimization problem is obtained by MOPSO.
Abstract For the optimization of concrete mixture proportions, multiple objectives (e.g., strength, cost, slump) with many variables (e.g., concrete components) under highly nonlinear constraints need to be optimized simultaneously. The current single-objective optimization models are not applicable to multi-objective optimization (MOO). This study proposes an MOO method based on machine learning (ML) and metaheuristic algorithms to optimize concrete mixture proportions. First, the performances of different ML models in the prediction of concrete objectives are compared on data sets collected from the published literature. The winner is selected as the objective function for the optimization procedure. In the optimization step, a multi-objective particle swarm optimization algorithm is used to optimize mixture proportions to achieve optimal objectives. The results show that the backpropagation neural network has better performance on continuous data (e.g., strength), whereas the random forest algorithm has higher prediction accuracy on more discrete data (e.g., slump). The Pareto fronts of a bi-objective mixture optimization problem for high-performance concrete and a tri-objective mixture optimization problem for plastic concrete are successfully obtained by the MOO model. The MOO model can serve as a design guide to facilitate decision-making before the construction phase.
Multi-objective optimization of concrete mixture proportions using machine learning and metaheuristic algorithms
https://orcid.org/0000-0001-9241-9367
https://orcid.org/0000-0003-4013-7215
Highlights BPNN has good prediction accuracy for UCS, while RF performs better in predicting slump. PSO is efficient in tuning hyperparameters of machine learning models. The Pareto front of the mixture optimization problem is obtained by MOPSO.
Abstract For the optimization of concrete mixture proportions, multiple objectives (e.g., strength, cost, slump) with many variables (e.g., concrete components) under highly nonlinear constraints need to be optimized simultaneously. The current single-objective optimization models are not applicable to multi-objective optimization (MOO). This study proposes an MOO method based on machine learning (ML) and metaheuristic algorithms to optimize concrete mixture proportions. First, the performances of different ML models in the prediction of concrete objectives are compared on data sets collected from the published literature. The winner is selected as the objective function for the optimization procedure. In the optimization step, a multi-objective particle swarm optimization algorithm is used to optimize mixture proportions to achieve optimal objectives. The results show that the backpropagation neural network has better performance on continuous data (e.g., strength), whereas the random forest algorithm has higher prediction accuracy on more discrete data (e.g., slump). The Pareto fronts of a bi-objective mixture optimization problem for high-performance concrete and a tri-objective mixture optimization problem for plastic concrete are successfully obtained by the MOO model. The MOO model can serve as a design guide to facilitate decision-making before the construction phase.
Multi-objective optimization of concrete mixture proportions using machine learning and metaheuristic algorithms
https://orcid.org/0000-0001-9241-9367
https://orcid.org/0000-0003-4013-7215
Highlights BPNN has good prediction accuracy for UCS, while RF performs better in predicting slump. PSO is efficient in tuning hyperparameters of machine learning models. The Pareto front of the mixture optimization problem is obtained by MOPSO.
Abstract For the optimization of concrete mixture proportions, multiple objectives (e.g., strength, cost, slump) with many variables (e.g., concrete components) under highly nonlinear constraints need to be optimized simultaneously. The current single-objective optimization models are not applicable to multi-objective optimization (MOO). This study proposes an MOO method based on machine learning (ML) and metaheuristic algorithms to optimize concrete mixture proportions. First, the performances of different ML models in the prediction of concrete objectives are compared on data sets collected from the published literature. The winner is selected as the objective function for the optimization procedure. In the optimization step, a multi-objective particle swarm optimization algorithm is used to optimize mixture proportions to achieve optimal objectives. The results show that the backpropagation neural network has better performance on continuous data (e.g., strength), whereas the random forest algorithm has higher prediction accuracy on more discrete data (e.g., slump). The Pareto fronts of a bi-objective mixture optimization problem for high-performance concrete and a tri-objective mixture optimization problem for plastic concrete are successfully obtained by the MOO model. The MOO model can serve as a design guide to facilitate decision-making before the construction phase.
Multi-objective optimization of concrete mixture proportions using machine learning and metaheuristic algorithms
Zhang, Junfei (Autor:in) / Huang, Yimiao (Autor:in) / Wang, Yuhang (Autor:in) / Ma, Guowei (Autor:in)
13.04.2020
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Study on Nonlinear Multi-Objective Optimization for Concrete Mixed Proportions
| British Library Conference Proceedings | 2013