A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Bayesian design of concrete with amortized Gaussian processes and multi-objective optimization
Abstract Here, we present a computational framework, combining machine learning models with inverse optimization, which can accelerate and optimize concrete mix design with respect to climate impact and/or cost. Our approach leverages a novel amortized Gaussian process (GP) model trained on a large industry dataset to predict concrete strength based on mix proportions. The resulting GP model has an R 2 value, RMSE, and MAPE of ∼0.88, ∼909 psi (6.3 MPa), and ∼10.8 %, respectively. We integrated the GP model with an inverse optimization scheme to predict optimal mix designs that minimize cost and/or climate impact. The results show that this integrated framework can generate reasonable concrete mixes that offer up to ∼30 % and ∼60 % reductions in cost and climate impact, respectively, compared with industry mixes with similar 28-day strength. This study highlights the potential environmental and economic benefits of data-driven approaches to designing and optimizing concrete mixes.
Bayesian design of concrete with amortized Gaussian processes and multi-objective optimization
Abstract Here, we present a computational framework, combining machine learning models with inverse optimization, which can accelerate and optimize concrete mix design with respect to climate impact and/or cost. Our approach leverages a novel amortized Gaussian process (GP) model trained on a large industry dataset to predict concrete strength based on mix proportions. The resulting GP model has an R 2 value, RMSE, and MAPE of ∼0.88, ∼909 psi (6.3 MPa), and ∼10.8 %, respectively. We integrated the GP model with an inverse optimization scheme to predict optimal mix designs that minimize cost and/or climate impact. The results show that this integrated framework can generate reasonable concrete mixes that offer up to ∼30 % and ∼60 % reductions in cost and climate impact, respectively, compared with industry mixes with similar 28-day strength. This study highlights the potential environmental and economic benefits of data-driven approaches to designing and optimizing concrete mixes.
Bayesian design of concrete with amortized Gaussian processes and multi-objective optimization
Pfeiffer, Olivia P. (author) / Gong, Kai (author) / Severson, Kristen A. (author) / Chen, Jie (author) / Gregory, Jeremy R. (author) / Ghosh, Soumya (author) / Goodwin, Richard T. (author) / Olivetti, Elsa A. (author)
2023-12-05
Article (Journal)
Electronic Resource
English
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