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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 呂良正(Liang-Jenq Leu) | |
| dc.contributor.author | Ying-Jun Chen | en |
| dc.contributor.author | 陳穎君 | zh_TW |
| dc.date.accessioned | 2022-11-23T09:07:16Z | - |
| dc.date.available | 2021-09-17 | |
| dc.date.available | 2022-11-23T09:07:16Z | - |
| dc.date.copyright | 2021-09-17 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-09-01 | |
| dc.identifier.citation | Akande, K. O., Owolabi, T. O., Twaha, S., Olatunji, S. O. (2014). Performance Comparison of SVM and ANN in Predicting Compressive Strength of Concrete. IOSR Journal of Computer Engineering, 16(5), 88–94. Ballal, T. M. A., Sher, W. D. (2003). Artificial Neural Network for the Selection of Buildable Structural Systems. Engineering, Construction and Architectural Management, 10(4), 263–271. Berrais, A. (2005). A Knowledge-Based Expert System for Earthquake Resistant Design of Reinforced Concrete Buildings. Expert Systems with Applications, 28(3), 519–530. Box, G. E., Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society: Series B (Methodological), 26(2), 211-243. Breiman, L. (1996). Bagging predictors. Machine learning, 24(2), 123-140. Breiman, L., Friedman, J., Stone, C. J., Olshen, R. A. (1984). Classification and regression trees: CRC press. Chou, J.-S., Chiu, C.-K., Farfoura, M., Al-taharwa, I. (2011). 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(2016). Deep learning with Python. New York: Manning Publications Company. Freund, Y., Schapire, R. E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), 119–139. Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, 29(5), 1189–1232. Harirchian, E., Lahmer, T., Kumari, V., Jadhav, K. (2020). Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings. Energies, 13(13), 3340. Harirchian, E., Lahmer, T., Rasulzade, S. (2020). Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network. Energies, 13(8), 2060. Hsuan-Tein, L. (2017a). Decision tree [Powerpoint slides]. Retrieved from https://www.csie.ntu.edu.tw/~htlin/course/mltech17spring/ Hsuan-Tein, L. (2017b). Random Forest [Powerpoint slides]. 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Retrieved from: https://scikit-learn.org/stable/user_guide.html Siddique, R., Aggarwal, P., Aggarwal, Y. (2011). Prediction of Compressive Strength of Self-Compacting Concrete Containing Bottom Ash Using Artificial Neural Networks. Advances in Engineering Software, 42, 780–786. Stone, H. (2017). Exposure and Vulnerability for Seismic Risk Evaluations. PH.D. Thesis, University College London, United Kingdom. UCI Machine Learning Repository. Concrete Compressive Strength Data Set. Retrieved from http://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength Vakharia, V., Gujar, R. (2019). Prediction of Compressive Strength and Portland Cement Composition Using Cross-Validation and Feature Ranking Techniques. Construction and Building Materials, 225, 292–301. Yeh, I.-C. (1998). Modeling of Strength of High-Performance Concrete Using Artificial Neural Networks. Construction and Building Materials, 28(12), 1797–1808. Young, B. A., Hall, A., Pilon, L., Gupta, P., Sant, G. (2019). Can the Compressive Strength of Concrete Be Estimated from Knowledge of the Mixture Proportions?: New Insights from Statistical Analysis and Machine Learning Methods. Cement and Concrete Research, 115, 379–388. 中華民國國家標準CNS 3090預拌混凝土。經濟部標準檢驗局(民國104年1月13日)。 建築物耐震設計規範及解說。內政部營建署(民國100年1月19日)。 財團法人臺灣營建研究院(民國109年7月)。中華民國預拌混凝土廠驗證：優質混凝土(GRMC)驗證申請作業手冊。檢自https://drive.google.com/file/d/1_vSIXs_-oNh1lngx1N6iJbWRJuUCxMlS/view | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79678 | - |
| dc.description.abstract | "本研究利用Python程式語言，使用機器學習模型，與土木工程實務作結合，研究分成兩部分，第一部分應用於預測混凝土抗壓強度，以資料科學的角度給予預拌混凝土廠商進行混凝土配比設計上的建議。第二部分應用於RC建築的梁柱設計，提供工程師於建築結構設計時，能藉此取得初步的尺寸及鋼筋量設計建議，以利減少設計上的失誤。 第一部分的資料來源於台灣營建研究院推動的「中華民國預拌混凝土廠驗證」之優質混凝土標章(Good Ready-Mixed Concrete, GRMC)，針對國內合法預拌混凝土廠的詳細抽驗資料。首先說明資料前處理的過程，並分別採用隨機森林、XGBoost等機器學習模型學習資料的特性，分析各參數的重要程度，此外亦探討混凝土抽驗年份與預拌廠區域對於預測結果的影響。 第二部分的建案資料取自築遠工程顧問有限公司，本研究將之進行整理並創建成資料庫。採用隨機森林、XGBoost、AdaBoost等機器學習模型，分別針對RC建築的大梁跟柱進行尺寸及鋼筋量的預測模型建構，並整理及比較各自的重要參數，此外亦針對部分案例的預測結果進行誤差原因的探討。" | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T09:07:16Z (GMT). No. of bitstreams: 1 U0001-3008202115234901.pdf: 11640571 bytes, checksum: f22f010f2e97a384bfdeff5d9504bd16 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | "口試委員審定書 I 誌謝 III 中文摘要 V ABSTRACT VII 目錄 IX 圖目錄 XIII 表目錄 XVII 第一章 緒論 1 1.1 研究動機 1 1.2 文獻回顧 2 1.3 各章內容 3 第二章 機器學習介紹 5 2.1 前言 5 2.2 資料探勘 5 2.3 人工智慧、機器學習與深度學習 7 2.3.1 人工智慧 (Artificial Intelligence) 7 2.3.2 機器學習 (Machine Learning) 8 2.3.3 深度學習 (Deep Learning) 10 2.4 機器學習模型介紹 13 2.4.1 決策樹 (Decision Tree) 13 2.4.2 隨機森林 (Random Forest) 15 2.4.3 自適應提升 (Adaptive Boosting, AdaBoost) 16 2.4.4 梯度提升決策樹 (Gradient Boosting Decision Tree, GBDT) 17 2.4.5 極限梯度提升 (Extreme Gradient Boosting, XGBoost) 18 2.5 深度學習模型介紹 19 2.5.1 人工神經網路 (Artificial Neural Network, ANN) 19 2.6 機器學習模型流程 22 第三章 混凝土資料介紹 25 3.1 前言 25 3.2 台灣混凝土抽樣檢驗作業 25 3.3 GRMC資料庫內容介紹 28 第四章 混凝土抗壓強度預測模型 29 4.1 前言 29 4.2 資料前處理 29 4.2.1 參數篩選及擴增 29 4.2.2 刪除不符規範或極端資料 31 4.2.3 缺失值處理 32 4.2.4 數值參數與類別參數處理 33 4.3 數據統計 35 4.4 模型評估指標 37 4.5 預測模型建構 39 4.5.1 各模型預測結果 39 4.5.2 重要參數分析 44 4.5.3 模型訓練資料數量的影響 48 4.6 根據抽驗年份劃分資料訓練模型 49 4.6.1 新舊資料集的影響(例題一) 49 4.6.2 資料庫更新頻率評估(例題二) 51 4.7 根據混凝土預拌廠區域劃分資料訓練模型 54 4.7.1 依據預拌廠區域劃分成子資料庫的可行性 54 4.7.2 列入預拌廠區域為新的特徵參數 57 4.8 小結 58 第五章 RC建築資料庫創建 59 5.1 前言 59 5.2 RC建築資料庫建立假設 59 5.3 RC建築資料內容 62 5.4 RC建築資料蒐集 62 5.4.1 不規則性結構 62 5.4.2 中低樓層及高樓層選擇 64 5.4.3 RC建築案例說明 65 第六章 RC建築梁柱設計預測模型 79 6.1 前言 79 6.2 資料前處理 79 6.2.1 將xy向轉成結構的長短向 79 6.2.2 缺失值處理 80 6.3 數據統計 80 6.4 大梁設計預測模型 88 6.4.1 模型評估指標 88 6.4.2 模型預測結果 92 6.4.3 重要參數分析 101 6.4.4 特殊案例討論 111 6.5 柱設計預測模型 123 6.5.1 模型評估指標 123 6.5.2 模型預測結果 127 6.5.3 重要參數分析 140 6.5.4 特殊案例討論 148 6.6 小結 160 第七章 結論與未來展望 163 7.1 結論 163 7.1.1 混凝土抗壓強度預測研究結論 163 7.1.2 RC建築梁柱設計預測研究結論 163 7.2 未來展望 165 7.2.1 混凝土抗壓強度預測研究未來展望 165 7.2.2 RC建築梁柱設計預測研究未來展望 165 參考文獻 167 附錄一 GRMC資料庫項目列表 171 附錄二 RC建築資料庫項目列表 175" | |
| dc.language.iso | zh-TW | |
| dc.title | 應用機器學習於混凝土抗壓強度預測及RC建築梁柱設計 | zh_TW |
| dc.title | Application of Machine Learning in Predicting Compressive Strength of Concrete and Design of Beam and Column for RC Building | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 郭世榮(Hsin-Tsai Liu),宋裕祺(Chih-Yang Tseng),黃仲偉,張盈智 | |
| dc.subject.keyword | 混凝土抗壓強度預測,RC建築梁柱設計,機器學習,隨機森林,GRMC資料庫, | zh_TW |
| dc.subject.keyword | Compressive strength of concrete,Design of beam and column,Machine learning,Random forest,GRMC database,RC building, | en |
| dc.relation.page | 181 | |
| dc.identifier.doi | 10.6342/NTU202102850 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-09-02 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
| 顯示於系所單位： | 土木工程學系 | |
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|---|---|---|---|
| U0001-3008202115234901.pdf | 11.37 MB | Adobe PDF | 檢視/開啟 |
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