Graduate Thesis Or Dissertation

 

Numerical Service Life Model of Chloride Induced Corrosion in Recycled Aggregate Concrete and Design Optimization of Sustainable and Durable Concrete Mixtures Public Deposited

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https://scholar.colorado.edu/concern/graduate_thesis_or_dissertations/76537169x
Abstract
  • Concrete mixture design is a multi-objective optimization problem that often involves the weighing of trade-offs between fresh-state properties, such as workability, flowability, and set time with hardened-state properties, such as strength, freeze thaw durability, and chloride durability. Substantial time and effort goes into designing, testing, and perfecting single mixture designs for optimal performance in some, but not all, of these properties. Additionally, considerations for mixture cost and environmental impacts, such as embodied energy, and embodied carbon are often included only as a secondary objective. The complex relationships between concrete mixture proportions and resulting constituent properties are investigated herein through two studies that explore mixture design tradeoffs.The first study presents the development, validation, and implementation of a 1D numerical service-life prediction model for reinforced recycled aggregate concrete exposed to internal and external sources of chlorides. The model accounts for the inclusion of supplementary cementitious materials (SCMs), namely (a) fly ash, (b) slag, (c) silica fume, and (d) metakaolin, and recycled aggregates (i) with and (ii) without initial chloride contamination from previous in-service exposure. The model is used to predict time to corrosion-induced cracking for reinforced recycled aggregate concrete in five case-study applications, namely structures in a marine splash zone (Zone I), a marine spray zone (Zone II), within 800 km of coastline (Zone III), within 1.5 km of coastline (Zone IV), and parking structures at locations greater than 1.5 km from the coastline (Zone V) in Los Angeles, California and Anchorage, Alaska. The effects of recycled aggregate size, replacement ratio, degree of aggregate pre-contamination with chloride from previous in-service exposure, water-to-cement (w/c) ratio, and SCMs on time-to-cracking of reinforced recycled aggregate concrete are elucidated herein. The potential for SCMs to improve the service life of recycled aggregate concrete is investigated by estimating theoretical additions required to meet a target service life of 50 years. Results indicate that, in addition to geographic location, temperature, and severity of chloride exposure, w/c ratio and aggregate replacement ratio exhibit the greatest impact on time to chloride-induced cracking in reinforced recycled aggregate concrete. Furthermore, initial aggregate chloride contamination and aggregate size impart minimal effects on expected service life. Finally, the results illustrate that the use of either fly ash or slag is most viable in achieving a 50-year service life for recycled aggregate concretes in chloride-laden environments.Broadening the work of the first study, the second study presents the development and implementation of a multi-objective concrete mixture design tool to evaluate tradeoffs of different mixture proportions on the physical, mechanical, and environmental performance of concrete. The model utilizes a multi-objective evolutionary algorithm (MOEA) that uses a search-based methodology to find a set of Pareto-optimal mixture designs. Mathematical relationships informing the MOEA consider the effect of cement content, water, supplementary cementitious materials (SCMs), namely (i) fly ash, (ii) silica fume, (iii) slag and (iv) metakaolin, sand, coarse aggregate, recycled aggregates, and air on fresh- and hardened-state properties, cost, and environmental impacts. Six objectives are used to determine optimality of mixtures: strength, workability, chloride induced corrosion resistance, embodied energy, embodied carbon, and cost. Objective properties are modeled using a suite of empirical and numerical methods that consider multiple decisions in their formulation. As demonstrated herein, the model can produce a suite of optimal mixtures for three case study applications, namely (1) a cubic meter of concrete, (
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  • 2017
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  • 2019-11-18
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