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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳政忠(Tsung-Tsong Wu) | |
| dc.contributor.author | Chun-Yi Hong | en |
| dc.contributor.author | 洪浚譯 | zh_TW |
| dc.date.accessioned | 2021-06-17T09:07:01Z | - |
| dc.date.available | 2019-12-26 | |
| dc.date.copyright | 2019-12-26 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-12-16 | |
| dc.identifier.citation | [1] J. S. Popovics and J. L. Rose, “A survey of developments in ultrasonic NDE of concrete,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 41, no. 1, pp. 140-143, 1994.
[2] J. R. Leslie and W. J. Cheesman, “An Ultrasonic Method of Studying Deterioration and Cracking in Concrete Structures,” ACI J. Proc., 46(1), pp.17, 1945. [3] T. Shiotani, M. Shigeishi, and M. Ohtsu, “Acoustic emission characteristics of concrete-piles,” Construction and Building Materials, vol. 13, no. 1-2, pp. 73-85, 1999. [4] Y. Shigenori, O. Takahisa, S. Mitsuhiro, and O. Masayasu, “Quantitative evaluation and visualization of cracking process in reinforced concrete by a moment tensor analysis of acoustic emission,” Materials Evaluation, vol. 53, no. 6, 1995. [5] M. Janků, I. Březina, and J. Grošek, “Use of Infrared Thermography to Detect Defects on Concrete Bridges,” Structural and Physical Aspects of Construction Engineering, vol.190, pp. 62-69, 2017. [6] Y. Sakata and M. Ohtsu, “Crack Evaluation in Concrete Members Based on Ultrasonic Spectroscopy,” Materials Journal, vol. 92, no. 6, pp. 686-698, 1995. [7] T. M. Proctor, “An improved piezoelectric acoustic emission transducer,” Acoustical Society of America , vol. 71, no. 5, 1982. [8] T. M. Proctor, “More recent improvement on the NBS conical transducer,” Journal of Acoustic Emission , vol. 5, pp.134-142, 1986. [9] M. Sansalone and N. J. Carino, “Impact-Echo Method: Detecting Honeycombing, the Depth of Surface-opening Cracks, and Ungrouted Ducts,” Concrete International, vol. 10, No. 4, pp. 38–46, 1988. [10] N. J. Carino and M. Sansalone, “Pulse-Echo Method for Flaw Detection in Concrete,” National Bureau of Standards, 1984 [11] C.-C Cheng and M. Sansalone, “Determining the minimum crack width that can be detected using the impact-echo method Part 1: Experimental study,” Materials and Structures , vol. 28, no. 176, pp. 74-82, 1995. [12] C.-C Cheng and M. Sansalone, “Determining the minimum crack width that can be detected using the impact-echo method. Part II: Numerical fracture analyses,” Materials and Structures , vol. 28, pp. 74-82, 1995. [13] Y. Lin and M. Sansolone, “Detecting Flaws in Concrete Beams and Columns Using the Impact-Echo Method,” Materials Journal, vol. 89, no. 4, pp. 394-405, 1992. [14] 吳政忠, 方金壽, “暫態彈性波在混凝土品質與裂縫偵測之應用,” 台灣大學應用力學所, 1996. [15] T.-T. Wu, J.-S. Fang, and P.-L. Liu, “Detection of the depth of a surface-breaking crack using transient elastic waves,” The Journal of the Acoustical Society of America, vol. 97, no. 3, pp. 1678–1686, 1995. [16] T.-T. Wu, J.-S. Fang, G.-Y. Liu, and M.-K. Kuo, “Determination of elastic constants of a concrete specimen using transient elastic waves,” The Journal of the Acoustical Society of America, vol. 98, no.4, pp. 2142–2148, 1995. [17] A. M. Turing, “Computing Machinery and Intelligence,” Mind 49, no.236, pp. 433-460, 1950. [18] C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp.273-297, 1995. [19] J. A. K. Suykens and J. Vandewalle, “Least Squares Support Vector Machine Classifiers,” Neural Processing Letters, vol. 9, no. 3, pp. 293-300, 1999. [20] G. E. Hinton and R. R. Salakhutdinov, “Reducing the Dimensionality of Data with Neural Networks,” Science, vol. 313, no. 5786, pp. 504-507, 2006. [21] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems 25, pp. 1097-1105, 2012. [22] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going Deeper with Convolutions,” The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-9, 2015. [23] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Computer Vision and Pattern Recognition, pp. 1-14, 2015. [24] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, 2016. [25] D. Silver et al., “Mastering the game of Go with deep neural networks and tree search,” Nature, vol. 529, no. 7587, pp. 484-489, 2016. [26] D. Silver et al., “Mastering the game of Go without Human Knowledge,” Nature, vol. 550, no. 7676, pp. 354-359, 2017. [27] F. Milletari, N. Navab, and S.-A. Ahmad, “V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation,” Fourth International Conference on 3D Vision (3DV), 2016. [28] A. Conneau, H. Schwenk, Y. LeCun, and L. Barrault, “Very deep convolutional networks for text classification,” Computer Science, vol. 10, no. 1, pp. 1107-1116, 2016. [29] Z, Ying et all., “Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks,” Proc. Interspeech, pp. 410-414, 2016. [30] A. Nayebi and M. Vitelli, “GRUV : Algorithmic Music Generation using Recurrent Neural Networks,” 2015. [31] S. Venugopalan et all., “Translating videos to natural language using deep recurrent neural networks,” Association for Computational Linguistics (ACL), no. 11, pp. 1494-1504, 2014. [32] Q. Wu et all., “Image Captioning and Visual Question Answering Based on Attributes and External Knowledge,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 6, pp. 1367-1381, 2017. [33] S. J. Song and L. W. Schmerr, “Ultrasonic flaw classification in weldments using probabilistic neural networks,” Journal of Nondestructive Evaluation, vol. 11, no. 2, pp. 69-77, 1992. [34] M. Lorenz and T. S. Wielinga, “Ultrasonic characterization of defects in steel using Multi-SAFT imaging and neural networks,” NDT & E International, vol. 26, no. 3, pp. 127-133, 1993. [35] A. A. Shah, S. H. Alsayed, H. Abbas, and Y. A. Al-Salloum, “Predicting residual strength of non-linear ultrasonically evaluated damaged concrete using artificial neural network,” Construction and Building Materials, vol. 29, pp. 42-50, 2012. [36] T.-T. Wu and J.-S. Fang, “A new method for measuring in situ concrete elastic constantsusing horizontally polarized conical transducers,” The Journal of the Acoustical Society of America , vol. 98, no. 4, pp. 2142-2148, 1995. [37] 吳政忠, 童建樺, “彈性波混凝土品質檢測系統之研製與應用,” 台灣大學應用力學所, 2001. [38] Z. Alterman and F. C. Karal, “Propagation of elastic waves in layered media by finite difference methods,” Bulletin of the Seismological Society of America, 58, pp. 367-378, 1968. [39] L. D. Bertholf, “Numerical Solution for Two-Dimensional Elastic Wave Propagation in Finite Bars,” J. Appl. Mech., vol. 34, no. 3, pp. 725-734, 1957. [40] R. M. Alford, K. R. Kelly, and D. M. Boore, “Accuracy of finite-difference modeling of the acoustic wave equation,” Geophysics, vol.39, no. 6, pp. 834-842, 1974 [41] 吳政忠, 朱彥達, “以人工智慧預測混凝土之彈性波波速,” 台灣大學應用力學所, 2019. [42] 吳政忠, 朱慶樺, “混凝土裂縫量測系統之研製,” 台灣大學應用力學所, 1997. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74756 | - |
| dc.description.abstract | 以彈性波為基礎之土木非破壞檢測技術可有效檢測建築物之混凝土品質，其中時間域暫彈性波法可易於直接現地量測，但需要配合專業人員對訊號進行判讀，才可得到精準的波傳時間進行下一步分析。因此本論文將針對此技術提出改善方法，使用人工智慧輔助時間域暫態彈性波法偵測混凝土結構之裂縫位置。
深度學習之類神經網路為人工智慧目前發展最快的領域，其中深度類神經網路與卷積類神經網路為本論文所使用的類神經網路模型。為產生類神經網路模型數據庫，研究中以時間域有限差分法計算大量理論訊號當作數據庫，並將其波動訊號正規化後再輸入到類神經網路模型中進行訓練與測試，實現加快梯度下降求最佳解及提升模型的收斂速度。再逐步探討類神經網路中的隱藏層層數與神經元數量、卷積層與池化層分布及濾鏡大小與數量對模型的影響，設計適合本論文數據庫的最佳類神經網路模型。 最後，以所設計的最佳類神經網路模型進行實驗預測混凝土結構之裂縫。由結果顯示對實驗訊號進行擷取視窗的前處理，可以解決時間原點與雜訊對模型的影響；增加新的感測器資料後，模型得到更多的特徵有助於模型辨識實驗量測訊號，使模型預測裂縫長度與角度誤差由0.51cm與10.79度降低至0.17與4.23度。 總結來說，本研究成功實現以深度學習之類神經網路判讀測混凝土結構之裂縫資訊，不僅提供操作者可以更即時的對建築品質進行監控與評估，亦可以讓沒有專業背景的人進行操作。而條件多樣的訓練數據庫可讓實際量測時有更大的量測範圍，應用上會更為便利。 | zh_TW |
| dc.description.abstract | Nondestructive testing (NDT) on the basis of elastic waves as a perfect solution to monitoring and testing of the quality of reinforced concrete (RC) structures, the transient elastic wave system with the strength of in-situ measurement requires a specialist in charge of signals reading on the basis of precise travel time of wave velocity before further assays can be done. Accordingly, this study proposes a new approach to improve the transient elastic wave system and the author utilized artificial intelligence (AI) to support the transient elastic wave system that detects where surface breaking cracks are in RC structures.
Currently, artificial neural networks (ANNs) of deep learning (DL) develop the fastest in the domain of AI where this study employed the ANNs model based on deep neural networks (DNNs) and convolutional neural networks (CNNs). Firstly, to generate the database of the ANNs model, this study obtained the database via the finite-difference methods (FDM) that computed considerable signals in theory. Secondly, normalized the signals and input them in the ANNs model for training and testing, and managed to speed up gradient descent for obtaining the optimum and promoting the convergence rate. Thirdly, how the ANN model was affected by the count of hidden layers and neurons, the distribution of convolution and pooling layers and the size and quantity of filters was gradually investigated. Fourthly, the author developed the optimal neural networks model fitted to the database in this study. Lastly, the ANN model applied to trials that predicted surface breaking cracks in RC structures. Based on the findings, the trial signals processed with windows snapshot helped tackle the impacts of time origin and noise on the model. After adding the new sensor data, the model obtained more characteristics helpful to identify the trial measurement signals. By doing so, the length and angle errors predicted by the model dropped from 0.51cm and 10.79 degrees to 0.17cm and 4.23 degrees. Overall, this study employed the ANNs model of DL to detect surface breaking cracks in RC structures, which not only makes the operator monitor and assess the quality of buildings in a real-time manner but also lets non-professionals operate such system. As the diversified database is broadened to a wider measurement scope for in-situ measurement, such model can be more useful and handy in terms of its application. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T09:07:01Z (GMT). No. of bitstreams: 1 ntu-108-R06543071-1.pdf: 3618809 bytes, checksum: 185284102e0bf43bed5278b4ebd2242a (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 致謝 i
中文摘要 ii ABSTRACT iii 目錄 v 圖目錄 viii 表目錄 xii 第一章 導論 1 1.1 研究動機 1 1.2 文獻回顧 2 1.3 章節介紹 5 第二章 暫態彈性波量測混凝土結構之裂縫 8 2.1 暫態彈性波波速量測 8 2.2 混凝土裂縫量測方法 10 2.3 有限差分模擬混凝土結構中之裂縫 11 2.3.1 彈性波動方程式 12 2.3.2 有限差分方程式 13 2.3.3 裂縫繞射波之模擬 16 第三章 深度學習之原理 28 3.1 深度學習網路架構 28 3.1.1 深度類神經網路 28 3.1.2 卷積類神經網路 30 3.2 深度學習參數介紹 32 3.2.1 激活函數 32 3.2.2 學習速率 33 3.2.3 最佳化方法 34 第四章 網路模型設計 41 4.1 模型架構 41 4.2 深度類神經網路 43 4.2.1 隱藏層與神經元數量分析 44 4.2.2 深度類神經網路模型架構 46 4.3 卷積類神經網路 47 4.3.1 卷積層與池化層分析 47 4.3.2 卷積濾鏡大小與數量 49 第五章 以人工智慧偵測混凝土結構之裂縫 67 5.1 混凝土實驗試體之製作與量測訊號 67 5.2 理論與實驗訊號前處理 68 5.2.1 訊號視窗抓取位置與數量 69 5.2.2 波源作用時間 70 5.3 以人工智慧預測混凝土結構中之裂縫 71 5.3.1 輸入原始訊號預測混凝土結構之裂縫 71 5.3.2 輸入另一個感測器之參數 72 第六章 結論與未來展望 90 6.1 結論 90 6.2 未來展望 91 參考文獻 94 | |
| dc.language.iso | zh-TW | |
| dc.title | 以人工智慧偵測混凝土結構之裂縫 | zh_TW |
| dc.title | Detection of a Crack in Concrete Structure Using Artificial Intelligence | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張瑞益,孫嘉宏,陳永裕 | |
| dc.subject.keyword | 非波壞檢測,暫態彈性波法,人工智慧,深度學習,類神經網路, | zh_TW |
| dc.subject.keyword | Nondestructive testing,Transient elastic wave,Artificial intelligence,Deep learning,Neural network, | en |
| dc.relation.page | 98 | |
| dc.identifier.doi | 10.6342/NTU201904118 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-12-17 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 應用力學研究所 | zh_TW |
| 顯示於系所單位： | 應用力學研究所 | |
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