(3.227.0.150) 您好!臺灣時間:2021/05/08 10:53
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:陳芸岫
研究生(外文):Yun-Hsin chen
論文名稱:應用類神經網路模式預測鋼筋混凝土深樑之剪力強度
論文名稱(外文):Predicting Shear Strength of Reinforced Concrete Deep Beams by Artificial Neural NetworksArtificial Neural Networks
指導教授:潘煌鍟潘煌鍟引用關係湯兆緯湯兆緯引用關係
指導教授(外文):H.H.PamChao-Wei Tang
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:土木工程與防災科技研究所
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:99
中文關鍵詞:類神經網路倒傳遞網路鋼筋混凝土深樑
外文關鍵詞:Artificial Neural NetworkBack-Propagation NetworkRC Deep Beam
相關次數:
  • 被引用被引用:3
  • 點閱點閱:215
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
剪力強度為混凝土的重要力學性質之一,故在各種建築與橋樑設計規範中均將其納入考量。惟混凝土於剪力作用下的非線性行為相當複雜,其數理模式不易建立。有鑑於現今實驗資料蒐集的便利及資料分析技術的改善,研發容易、方便使用且準確的混凝土剪力強度預測方法將是一件有意義的事。本研究首先蒐集承受剪力作用之矩形斷面鋼筋混凝土深樑之剪力強度資料,以免除繁複的試驗工作;其次,建構預測鋼筋混凝土深樑剪力強度之多層倒傳遞類神經網路(Multilayer Perceptrons Networks,簡稱MLPN),以分析其極限剪力強度,並將所建構MLPN評估模式之預測值與現有鋼筋混凝土深樑剪力分析模式(即壓拉桿模式與軟化壓拉桿模式)之預測值作比較。
依據MLP 5-5-1模式之訓練範例、驗證範例及測試範例等分析結果,類神經網路預測模式的誤差均方根數值與相關係數數值均優於ACI 318-08、SSTM、及多元迴歸等分析模式,且在預測深樑剪力強度之輸入變數中,以混凝土抗壓強度、剪力跨度/有效深度、鋼筋量及斜桿角度較為重要;研究結果顯示,應用MLPN可有效預測鋼筋混凝土深樑的剪力強度,且其預測值的準確性也比既有經驗公式來得精確。
Shear strength is one of the major concrete mechanical properties that are indispensably used in different building and bridge design codes. However, the nonlinear behavior of concrete under shear force is very complicated and modeling its behavior is a hard task. Thus, it would be of interest to develop new methods that are easier, convenient, and more accurate than the existing methods in light of the availability of more experimental data and recent advance in the area of data analysis techniques. In this study, a database on shear failure of reinforced concrete deep beams with rectangular section subjected to shear force was retrieved from the existing literature for analysis instead of the practical and experimental data. Multilayer perceptrons networks (MLPN) were developed sequentially and the ultimate shear strength of each beams was determined from the MLPN model. Besides, the MLPN model’s predictions were also compared with those obtained using empirical equations (i.e. Strut-Tie Model and Soft Strut-Tie Model). It was found that the MLPN models could infer solutions from the data presented to them, capturing quite subtle relationships. In other words, the MLPN models give reasonable predictions of the ultimate shear strength of RC deep beams. The results also show that the MLPN models provide better accuracy than the existing parametric models.
目錄

摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 VII
符號說明 VIII
第一章 緒論 1
1.1 研究背景 1
1.2 研究目的 3
1.3 研究方法及流程 4
第二章 壓拉桿模式發展背景 6
2.1 壓拉桿理論 6
2.1.1 桁架模式 6
2.1.2 壓拉桿理論的基本概念 8
2.1.3 壓桿 9
2.1.4 拉桿 12
2.1.5 節點 13
2.2 ACI 318-08附錄A-壓拉桿模式 (Strut and Tie Model) 14
2.3 軟化拉壓桿模式簡算法 19
第三章 類神經網路之理論及方法 24
3.1 類神經網路簡介 24
3.1.1 生物神經元網路 24
3.1.2 人工神經元網路 25
3.2 類神經網路之功能 30
3.3 類神經網路之發展 31
3.4 類神經網路分類 32
3.4.1 依學習策略分類 32
3.4.2 依網路架構分類 33
3.5 倒傳遞類神經網路 34
3.5.1 倒傳遞類神經網路基本架構 34
3.5.2 最陡坡降法 36
3.5.3 倒傳遞網路學習過程 38
3.5.4 倒傳遞網路之優點與缺點 39
3.6 交叉驗證法(Cross-Validation) 39
3.7 多元迴歸分析 42
3.8 類神經網路與迴歸分析之比較 44
第四章 RC深樑剪力強度之分析與討論 46
4.1 RC深樑剪力強度之倒傳遞分析模式 46
4.2 K-fold交叉驗證法 51
4.3 多元迴歸分析模式 53
4.4 分析模式之比較 60
第五章 結論與建議 69
5.1 結論 69
5.2 建議 70
參考文獻 71
附錄 A 75
附錄B 80
口試照片 85
作者簡歷 86
1ACICommittee 318, Building Code Requirement for Reinforced Concrete (ACI 318-08) and Commentary (ACI 318 R-08), American Concrete Institute, Detroit, 2008.
2李宏仁、黃世建,「鋼筋混凝土結構不連續區域之剪力強度評估-軟化壓拉桿模型簡算法之實例應用」,結構工程,第十一卷,第四期,第53-70頁,2002。
3呂文堯、黃世建、林英俊,「鋼筋混凝土樑開榫端之抗剪強度評估」,中國土木水利工程學刊,第十五卷,第一期,第13-21頁,2003。
4Foster S. J. and Malik A. R., “Evaluation of Compression Failures in RC Non-Flexural Members,” School of Civil and Environmental Engineering, July, 2001.
5葉怡成,「類神經網路模式應用與實作」,儒林圖書公司,1998年。
6Goh, A.T.C., “Neural Networks for evaluating CPT calibration chamber test data. Microcomputers in Civil Engineering,” Vol. 10, pp. 147-151, 1995.
7Eldin, N.N., Senouci, A.B., “A pavement condition-rating model using back propagation neural networks. Microcomputers in Civil Engineering,” Vol. 10, pp. 433-441, 1995.
8Bulsari, A.B., Saxen. H., “Application of artificial networks for filtering, smoothing and prediction for a biochemical process,” Expert Systems, Vol. 11, No. 3, pp. 159-166, 1994.
9Proctor, R.A., “An expert system to aid in staff selection: a neural network approach,” International Journal of Manpower, Vol. 12, pp. 18-21, 1991.
10Hajela, P., Berke. L., “Neurobiological computational models in structural analysis and design,” Computers & Struct., Vol. 41, No. 4, 657-667, 1991.
11Elkordy, M.F., Chang, K.C., “Lee GC. Neural networks trained by analytically simulated damage states,” J. Computing in Civil Engrg., ASCE, Vol. 7, No. 2, pp. 130-145, 1993.
12Mukherjee, A., Deshpande, J.M., “Anmada J. Prediction of buckling load of columns using artificial neural networks,” Journal of Structural Engineering, ASCE, Vol. 122, No. 11, pp. 1385-1387, 1996.
13Chen, H.M., Tsai, K.H., Qi, G.Z., Yang, J.C.S., “Neural networks for structural control,” J. Computing in Civil Engrg., ASCE, Vol. 9, No. 2, pp. 168-176, 1995.
14Goh, A.T.C., “Seismic liquefaction potential assessed by neural networks,” Journal of Geotech. Engrg., ASCE, Vol.120, No. 9, pp. 1467-1480, 1994.
15Tang, C. W., Chen, H. J., and Yen T., “Modeling the confinement efficiency of reinforced concrete columns with rectilinear transverse steel using artificial neural networks.” Journal of Structural Engineering, ASCE, Vol. 129, No. 6, pp. 775-783, 2003.
16Tang, C. W., Lin, Y., and Kuo, S. F., “Investigation on correlation between pulse velocity and compressive strength of concrete using ANNs,” Computers & Concrete, Vol. 4, No. 6, pp. 437–456, 2007.
17Zhao, Z., and Ren, L., “Failure criterion of concrete under triaxial stresses using neural networks.” Computer-Aided Civil and Infrastructure Engineering, Vol. 17, No. 1, pp. 68-73, 2002.
18Yeh, I. C., “Design of high-performance concrete mixture using neural networks and nonlinear programming.” Journal of Computing in Civil Engineering, ASCE, Vol. 13, No. 1, pp. 36-42, 1999.
19Jain, A., Jha, S. K., and Misra, S., “Modeling and analysis of concrete slump using artificial neural networks.” Journal of Materials in Civil Engineering, ASCE, Vol. 20, No. 9, pp. 628-633, 2008.
20Ghaboussi, J., Garrett, J. H., and Wu, X., “Knowledge-based modeling of material behavior with neural networks.” Journal of Engineering. Mech., ASCE, Vol. 117, No. 1, 129-134, 1991.
21Ritter, W.,” The Hennebiqe Design Method (Die Bauweise Hennebuque),” Schweizerische Bauzeitung, Vol. 33, No. 7, pp. 59-61, February, 1899.
22Morsch, E., Concrete-Steel Construction, Translation of the 3rd German Edition by E. P. Goodrich, McGraw-Hill Book Co., New York, 1909.
23Clark, A. P., “Diagonal tension in reinforced concrete beams,” ACI Journal, Vol. 48, No. 10, Oct., pp. 145-15, 1951.
24Bresler, B., and MacGregor, J. G., “Review of concrete beams failing in shear,” Journal of Structural Division, ASCE, 93, ST1, pp. 343-372, 1967.
25Joint ACI-ASCE Committee 426, ”The Shear Strength of Reinforced Concrete Members,” Journal of Structural Division, ASCE, Vol. 99, No. ST6, pp.1091-1187, June, 1973.
26Fu, C. C., Ph.D., P.E, “The Strut and Tie Model of Concrete Structure,” The BEST Center University of Maryland, August 21, 2001.
27連建民,「跨深比較大的鋼筋混凝土深之行為」,國立台灣科技大學,營建工程系碩士論文,民國94年6月。
28Hwang, S.J., and Lee, H. J., “Analytical Model for Predicting Shear Strengths of Interior Reinforced Concrete Beam-Column Joint for Seismic Resistance,” ACI Structural Journal, Vol. 97, No. 1, pp. 35-44, January-February 2000.
29Hwang, S.J., and Lu, W. Y., Lee, H. J., “Shear Strength Prediction for Deep Beams,” ACI Structural Journal, Vol. 97, No. 3, pp. 367-376, May-June 2000.
30Hwang, S.J., and Lu, W. Y., Lee, H. J., “Shear Strength Prediction for Reinforced Concrete Corbels,” ACI Structural Journal, Vol. 97, No. 4, pp. 543-552, July-August 2000.
31Hwang, S.J., and Lee, H. J., “Shear Strength Prediction for Discontinuity Regions Failing in Diagonal Compressions by Softened Strut-and-Tie Model,“ Tentatively Journal of Structural Engineering, ASCE, Vol. 128, No. 12, pp. 1519-1526, 2000.
32呂文堯、黃世建,「鋼筋混凝土深梁之抗剪強度評估」,中國土木水利工程學刊,第十二卷,第一期,第11−20 頁, 2000。
33葉怡成,「應用類神經網路」,儒林圖書有限公司,台北市,2001。
34羅華強,「類神經網路:MATLAB的應用」,清蔚科技,新竹市,2001。
35危家康,「以類神經網路模擬受純扭力作用下鋼筋混凝土之強度」,國立成功大學,土木工程研究所碩士論文,民國92年。
36陳信達,「以倒傳遞網路模型預測傳統模板基準生產力之研究」,高雄第一科技大學,營建工程所碩士論文,民國91年。
37Ricardo Gutierrez-Osuna, Intelligent Sensor Systems, Wright State University.
38Dudoit, S., Fridlyand, J., and Speed, T., “Comparison of Discrimination Methods for the Classification of Tumor Using Gene Expression Data,” Journal of the American Statistical Association, Vol. 97, pp.77-87, 2000.
39Ashu, J., Sanjeev, K. J., and Sudhir M., “Modeling and Analysis of Concrete Slump Using Artificial Neural Networks,” Journal of Materials in civil Engineering, Vol.20, No.9, Septermber, 2008.
40黃俊英,「多變量分析」,中國經濟企業研究所,2001。
41林真真,「實用統計學」,東華書局,2002。
42STATISTICA Neural Networks Release 4.0, StatSoft, Inc., USA.
43Oh, J. K., and Shin, S. W., “Shear Strength of Reinforced High-Strength Concrete Deep Beams,” ACI Structural Journal, Vol. 98, No. 2, pp. 164-173, 2001.
44Smith, K. N., and Vantsiotis, A. S., “Shear Strength of Deep Beams,” ACI Journal, Vol. 79, No. 9, pp. 458-468, September 1977.
45Kong, F. K., Robins, P. J., and Cole, D. F., “Web Reinforcement Effects on Deep Beams,” ACI Journal, Vol. 67, No. 12, pp. 1010-1017, 1970.
46Foster, S. J. and Gilbert, R. I., “The Design of Non-Flexural Members with Normal and High Strength Concretes,” ACI Structural Journal, Jan-Feb, pp. 3-10, 1996.
47Tan, K. H., Kong, F. K., Teng, S., and Guan, L., “High-Strength Concrete Deep Beams with Effective Span and Variations,” ACI Structural Journal, Vol. 92, No. 4, pp. 395-405, 1995.
48Foster, S. J., and Gilbert, R. L., “Experimental Studies on High-Strength Concrete Deep Beams,” ACI Structural Journal, Vol. 95, No. 4, pp. 382-390, 1998.
49Fang. I., Chen, J., and Hong, L., “Shear Behaviour of High Strength Concrete Deep Beams,” 5th East Asia-Pacific Conference on Structural Engineering and Construction, Gold Coast, Qld., July, pp. 1747-1752, 1995.
50Lewis, C. D., “Industrial and Business Forecasting Methods: A practical Guide to Exponential Smoothing and Curve Fitting,” Butterworth Scientific, London., 1982.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔