Computational design optimization of concrete mixtures: A review
By M. DeRousseau, Joseph R. Kasprzyk, W. Srubar. Cement and Concrete Research, 2018
326 citations recorded by the source corpus.
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.
Subfields: Building and Construction, Civil and Structural Engineering
View Paper: https://www.semanticscholar.org/paper/42d9af1ae1602b3e28075a5f1b089bc8145a445a
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In Civil and Structural Engineering
Citations in the corpus
- Technologies and policies to decarbonize global industry: Review and assessment of mitigation drivers through 2070 (2020, methodology): “The use of concrete mixture optimization [66,67], improved design of members or structures through use of high-performance con-”