The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index.
Computational design optimization of concrete mixtures: A review
Open accessAbstract
Abstract A comprehensive review of optimization research concerning the design and proportioning of concrete mixtures is presented herein. Mixture design optimization is motivated by an ever-increasing need for designers and decision-makers to proportion concrete mixtures that satisfy multiple – oftentimes competing – performance requirements, including cost, workability, mechanical properties, durability, and environmental sustainability. In this review, we first discuss common mathematical problem formulations, decisions, objectives, and constraints pertaining to concrete mixture design optimization. Subsequently, we examine the types of models employed to approximate properties of concrete, which include a variety of linear combination, statistical, machine learning, and physics-based models that are required to optimize the proportions of a mixture. We then review and discuss computational methods used to optimize concrete mixtures in the context of surveyed literature. Finally, we highlight and discuss current trends and opportunities for advancing the field of concrete mixture design optimization in context of the current state of the art.
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Cites
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Cited by
Rissman, J., Bataille, C., Masanet, E., Aden, N., Morrow, W. R., Zhou, N., Elliott, N., Dell, R., Heeren, N., Huckestein, B., et al. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.114848 · Cited by 1,040
Baduge, S. K., Thilakarathna, S., Perera, J. S., Arashpour, M., Sharafi, P., Teodosio, B., Shringi, A., & Mendis, P. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104440 · Cited by 1,007
“These objective functions generally include cost, performance criteria such as compressive strength, shear strength and environmental criteria such as embodied carbon and embodied emissions [51].”
BackgroundMethodMachine Learning for Structural Engineering: A State-of-the-Art Review
Thai, H.-T. (2022) · Structures · doi:10.1016/j.istruc.2022.02.003 · Cited by 748
Machine learning in concrete science: applications, challenges, and best practices
Li, Z., Yoon, J., Zhang, R., Rajabipour, F., Srubar III, W. V., Dabo, I., & Radlińska, A. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00810-x · Cited by 388
Liu, Q.-F., Iqbal, M. F., Yang, J., Lu, X.-Y., Zhang, P., & Rauf, M. (2021) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2020.121082 · Cited by 259
Metadata
- Authors
- DeRousseau, M. A., Kasprzyk, J. R., & Srubar, W. V.
- Venue
- Cement and Concrete Research
- Publisher
- Elsevier BV
- Published
- 2018-07
- Volume
- 109
- Pages
- 42-53
- ISSN
- 0008-8846 (ISSN-L)
- OpenAlex
- W2799921980
- Cited by
- 359
- References
- 71
- FWCI
- 18.64
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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