研究生: |
Mai Thi Ngoc Dao Mai Thi Ngoc Dao |
---|---|
論文名稱: |
Artificial Intelligence-Integrated Approach to Optimize the Mixture Design of High Performance Concrete under Desired Properties Artificial Intelligence-Integrated Approach to Optimize the Mixture Design of High Performance Concrete under Desired Properties |
指導教授: |
鄭明淵
Min-Yuan Cheng |
口試委員: |
黃兆龍
Chao-Lung Hwang 曾惠斌 Hui-Ping Tserng 張陸滿 Luh-Maan Chang |
學位類別: |
碩士 Master |
系所名稱: |
工程學院 - 營建工程系 Department of Civil and Construction Engineering |
論文出版年: | 2019 |
畢業學年度: | 107 |
語文別: | 英文 |
論文頁數: | 81 |
中文關鍵詞: | High Performance Concrete 、Concrete Mixture 、Optimum Concrete Mixture 、SOS-LSSVM 、Compressive Strength 、Workability 、Durability |
外文關鍵詞: | High Performance Concrete, Concrete Mixture, Optimum Concrete Mixture, SOS-LSSVM, Compressive Strength, Workability, Durability |
相關次數: | 點閱:373 下載:0 |
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Concrete is the most widely applied material in the construction. In order to measure the performance of concrete mixtures in designing structures, compressive strength, workability and durability are used as the most common properties. Traditional method of mix design in laboratory is time and cost consuming; therefore, developing an Artificial Intelligence model to estimate concrete properties can provide the huge advantages for concrete industry. Numerous previous studies proposed different AI models for modelling concrete properties; however, most of them emphasized on two concrete properties namely compressive strength and workability, there have been no study considered the compressive strength, workability, durability properties and economic factor simultaneously. The aim of this research is to establish a model for obtaining the most economical mix design that meets those three concrete properties above using AI-Integrated approach namely SOS-LSSVM. The dataset for developing model are collected from various previous researches. The result shows that presented model produced the optimum mixture of High Performance Concrete, which has the lowest price and satisfy the desired concrete properties in term of Compressive Strength, Workability and Durability.
Concrete is the most widely applied material in the construction. In order to measure the performance of concrete mixtures in designing structures, compressive strength, workability and durability are used as the most common properties. Traditional method of mix design in laboratory is time and cost consuming; therefore, developing an Artificial Intelligence model to estimate concrete properties can provide the huge advantages for concrete industry. Numerous previous studies proposed different AI models for modelling concrete properties; however, most of them emphasized on two concrete properties namely compressive strength and workability, there have been no study considered the compressive strength, workability, durability properties and economic factor simultaneously. The aim of this research is to establish a model for obtaining the most economical mix design that meets those three concrete properties above using AI-Integrated approach namely SOS-LSSVM. The dataset for developing model are collected from various previous researches. The result shows that presented model produced the optimum mixture of High Performance Concrete, which has the lowest price and satisfy the desired concrete properties in term of Compressive Strength, Workability and Durability.
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