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Damage Modelling and Sustainable Man...

Zirps, Melissa Arin.

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Damage Modelling and Sustainable Management of Reinforced Concrete.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Damage Modelling and Sustainable Management of Reinforced Concrete./
作者:	Zirps, Melissa Arin.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	114 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-04, Section: A.
Contained By:	Dissertations Abstracts International85-04A.
標題:	Corrosion tests. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30614643
ISBN:	9798380482523

Damage Modelling and Sustainable Management of Reinforced Concrete.
Zirps, Melissa Arin.

Damage Modelling and Sustainable Management of Reinforced Concrete. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 114 p.

Source: Dissertations Abstracts International, Volume: 85-04, Section: A.

Thesis (Ph.D.)--Stanford University, 2023.

This item must not be sold to any third party vendors.

The cement industry has been growing rapidly over the last several decade. Portland cement is a desirable building material due to: accessibility, the raw material of cement is limestone, which can be found globally; affordability, the process of manufacturing cement is simple and inexpensive; and workability, cement when mixed with water can flow, allowing the cement paste to take on many different shapes. However, cement is not a sustainable material, contributing significantly to greenhouse gas emissions. To reduce cement related emissions, cement use must be minimized. The most common application for cement is in reinforced concrete. Therefore, to minimize the environmental impact of cement, reinforced concrete use must be reduced.To reduce the environmental impact of a reinforced concrete structure, its cumulative impact throughout the lifespan of a structure must be estimated during the design phase. This can be done using a probabilistic performance-based tool, which takes into account the impact of construction and repairs as well as the frequency of repairs based on the durability of the structure. Though probabilistic performance-based tools are able to capture cumulative emissions of a structure during the design phase, they are complex and computationally demanding, which often deters industry professionals from using them. Additionally, to accurately implement a probabilistic performancebased tool to calculate the cumulative emissions of a structure, the user must be able to accurately quantify the probabilistic distributions of impact of construction and repairs as well as the durability of the structure and how it will deteriorate over time. The main deterioration mechanism in reinforced concrete is corrosion of the steel reinforcement and the subsequent cracking of the concrete matrix. Many numerical models have been developed to predict the durability of reinforced concrete under corrosion; however, these models do not fully capture the complex nature of the corrosion induced cracking process. Additionally, as these models advance to more closely capture the corrosion induced cracking process, the larger the models become challenged by long runtimes.This dissertation introduces advancements to probabilistic performance-based tools for sustainable design. The goal of these advancements is to make probabilistic performance-based tools more accurate by improving the models used to predict the durability of a reinforced concrete structure and accessible to industry professionals by introducing more user friendly methods to implement these tools and accelerating the runtime of durability models. First, a method to increase the accessibility of probabilistic performance-based tools is presented. It is demonstrated how this method can be implemented using a case study. A probabilistic three dimensional coupled lattice and finite element model is introduced to improve the predictions of the durability of a reinforced mortar structure. This model is validated using experimental results. Finally, a deep learning model is created as a surrogate model for the probabilistic three dimensional coupled lattice and finite element model. This deep learning model addresses the long runtimes associated with numerical models while maintaining the accuracy of the numerical model used for training.

ISBN: 9798380482523Subjects--Topical Terms:

3560295
Corrosion tests.

Damage Modelling and Sustainable Management of Reinforced Concrete.
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520 $a The cement industry has been growing rapidly over the last several decade. Portland cement is a desirable building material due to: accessibility, the raw material of cement is limestone, which can be found globally; affordability, the process of manufacturing cement is simple and inexpensive; and workability, cement when mixed with water can flow, allowing the cement paste to take on many different shapes. However, cement is not a sustainable material, contributing significantly to greenhouse gas emissions. To reduce cement related emissions, cement use must be minimized. The most common application for cement is in reinforced concrete. Therefore, to minimize the environmental impact of cement, reinforced concrete use must be reduced.To reduce the environmental impact of a reinforced concrete structure, its cumulative impact throughout the lifespan of a structure must be estimated during the design phase. This can be done using a probabilistic performance-based tool, which takes into account the impact of construction and repairs as well as the frequency of repairs based on the durability of the structure. Though probabilistic performance-based tools are able to capture cumulative emissions of a structure during the design phase, they are complex and computationally demanding, which often deters industry professionals from using them. Additionally, to accurately implement a probabilistic performancebased tool to calculate the cumulative emissions of a structure, the user must be able to accurately quantify the probabilistic distributions of impact of construction and repairs as well as the durability of the structure and how it will deteriorate over time. The main deterioration mechanism in reinforced concrete is corrosion of the steel reinforcement and the subsequent cracking of the concrete matrix. Many numerical models have been developed to predict the durability of reinforced concrete under corrosion; however, these models do not fully capture the complex nature of the corrosion induced cracking process. Additionally, as these models advance to more closely capture the corrosion induced cracking process, the larger the models become challenged by long runtimes.This dissertation introduces advancements to probabilistic performance-based tools for sustainable design. The goal of these advancements is to make probabilistic performance-based tools more accurate by improving the models used to predict the durability of a reinforced concrete structure and accessible to industry professionals by introducing more user friendly methods to implement these tools and accelerating the runtime of durability models. First, a method to increase the accessibility of probabilistic performance-based tools is presented. It is demonstrated how this method can be implemented using a case study. A probabilistic three dimensional coupled lattice and finite element model is introduced to improve the predictions of the durability of a reinforced mortar structure. This model is validated using experimental results. Finally, a deep learning model is created as a surrogate model for the probabilistic three dimensional coupled lattice and finite element model. This deep learning model addresses the long runtimes associated with numerical models while maintaining the accuracy of the numerical model used for training.
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