Machine learning for strength prediction and optimal design of sustainable concrete formulas
Given the large environmental impact of the concrete industry, which represents 8- 9% of global CO₂ emissions, the design of concrete mixes with low carbon footprints that still meet structural performance requirements will be an essential part of global decarbonization efforts. In this work, we bui...
Main Author: | Pfeiffer, Olivia |
---|---|
Other Authors: | Olivetti, Elsa A. |
Format: | Thesis |
Published: |
Massachusetts Institute of Technology
2022
|
Online Access: | https://hdl.handle.net/1721.1/144609 |
Similar Items
-
Prediction of Compressive Strength of Sustainable Foam Concrete Using Individual and Ensemble Machine Learning Approaches
by: Haji Sami Ullah, et al.
Published: (2022-04-01) -
Prediction of compressive strength of recycled aggregate concrete using machine learning and Bayesian optimization methods
by: Xinyi Zhang, et al.
Published: (2023-02-01) -
Compressive strength prediction and composition design of structural lightweight concretes using machine learning methods
by: Artemy S. Balykov, et al.
Published: (2023-04-01) -
Prediction of the Compressive Strength of Vibrocentrifuged Concrete Using Machine Learning Methods
by: Alexey N. Beskopylny, et al.
Published: (2024-02-01) -
Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete
by: Rongchuan Cao, et al.
Published: (2022-03-01)