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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 ConcreteConcrete MixtureOptimum Concrete MixtureSOS-LSSVMCompressive StrengthWorkabilityDurability
外文關鍵詞: High Performance Concrete, Concrete Mixture, Optimum Concrete Mixture, SOS-LSSVM, Compressive Strength, Workability, Durability
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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.

    TABLE OF CONTENT TABLE OF CONTENT V LIST OF FIGURE VIII LIST OF TABLE IX ABBREVIATIONS AND SYMBOLS XI CHAPTER 1 : INTRODUCTION 1 1.1 Research Motivation 1 1.2 Research Objective 4 1.3 Research scope and assumption 4 1.4 Research Methodology 5 1.4.1 Research Introduction 7 1.4.2 Literature Review 7 1.4.3 Model Construction 8 1.4.4 Model validation and application 8 1.4.5 Conclusion and Recommendation 9 1.5 Study outline 9 CHAPTER 2 : LITERATURE REVIEW 10 2.1 High Performance Concrete 10 2.2 Method of proportioning concrete mixes 10 2.3 Important properties of High Performance Concrete 12 2.4 Previous works related to model concrete mixture 13 2.5 Symbiotic Organism Search- Least Squares Support Vector Machine 16 2.5.1 Symbiotic Organism Search 16 2.5.2 Least Squares Support Vector Machine (LSSVM) 17 2.6 Evolutionary Support Vector Machine Inference Model (ESIM) 21 2.7 Back Propagation Neural Networks (BPNN) 22 CHAPTER 3 : MODEL ARCHITECTURE 23 3.1 Prediction Model Architecture 24 3.2 Optimization Model Architecture 26 CHAPTER 4 : MODEL DEVELOPMENT 29 4.1 Develop prediction model 29 4.1.1 Identify input and output factor 29 4.1.2 Data Collection 31 4.2. Prediction Model Development 35 4.2.1. Development of SOS-LSSVM Prediction Model 35 4.2.2 Model Evaluation 40 4.3 Optimization Model Development 46 4.4 Optimization Result 50 CHAPTER 5 : CONCLUSIONS AND RECOMMENDATIONS 56 5.1 Research Conclusion 56 5.2 Research Recommendation 57 REFERENCES 59

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