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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 黃維信 | zh_TW |
| dc.contributor.advisor | Wei-Shien Hwang | en |
| dc.contributor.author | 劉涼祺 | zh_TW |
| dc.contributor.author | Liang-Chi Liu | en |
| dc.date.accessioned | 2025-08-21T16:06:56Z | - |
| dc.date.available | 2025-08-22 | - |
| dc.date.copyright | 2025-08-21 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-07-31 | - |
| dc.identifier.citation | [1] 王仲宇. 人行吊橋之橋梁安全監測. 土木水利, 43(1):27–33, 2016.
[2] Lixiao Zhang and Guoyang Qiu. Structural health monitoring methods of cables in cable-stayed bridge: A review. Measurement, 168:108343, 08 2020. [3] Ammar Zalt and Vijay Meganathan. Evaluating sensors for bridge health monitoring. IEEE International Conference on Electro/Information Technology, pages 368–372, 2007. [4] Andrea Bergamini. Nondestructive testing of stay cables. IABSE Symposium Report, pages 16–23, 2000. [5] Hiroshi Zui and Tohru Shinke. Practical formulas for estimation of cable tension by vibration method. Journal of Structural Engineering, 122(6):651–656, 1996. [6] Zhi Fang and Jian qun Wang. Practical formula for cable tension estimation by vibration method. Journal of Bridge Engineering, 17(1):161–164, 2012. [7] 巫文勝. 以軸力梁理論提昇預力鋼纜傳統評估公式實用性之研究. Master’s thesis, 朝陽科技大學營建工程系碩士論文, 2014. [8] E. Pierro and E. Mucchi. On the vibro-acoustical operational modal analysis of a helicopter cabin. Mechanical Systems and Signal Processing, 23(4):1205–1217,2009. [9] Zhongru Yu and Shuai Shao. Cable tension identification based on near field radiated acoustic pressure signal. Measurement, 178:109354, 2021. [10] 楊立安. 應用聲音訊號進行纜索張力值估算. Master’s thesis, 國立臺灣大學工程科學及海洋工程學系學位論文, 2023. [11] S.W. Doebling, Charles Farrar, Michael Prime, and D.W. Shevitz. Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review. Technical Report No. LA-13070-MS, 30, 05 1996. [12] Ziemowit Dworakowski and Kajetan Dziedziech. Damage detection in plates with the use of laser-measured mode shapes. Shock and Vibration, 4:1–20, 10 2020. [13] Tongtong Gai and Dehu Yu. An optimization neural network model for bridge cable force identification. Engineering Structures, 286:116056, 2023. [14] Y.F. Xu and W.D. Zhu. Operational modal analysis of a rectangular plate using noncontact excitation and measurement. Journal of Sound and Vibration, 332(20):4927–4939,2013. [15] Jann N. Yang and Ying Lei. System identification of linear structures based on hilbert–huang spectral analysis. part 1: normal modes. Earthquake Engineering & Structural Dynamics, 32(9):1443–1467, 2003. [16] Frank Rosenblatt. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6):386–408, 1958. [17] Hinton G. Williams R. Rumelhart, D. Learning representations by back-propagating errors. Nature, 323:533–536, 1986. [18] Christopher M Bishop. Neural networks for pattern recognition. Oxford university press, 1995. [19] A. Hajnayeb and A. Ghasemloonia. Application and comparison of an ann-based feature selection method and the genetic algorithm in gearbox fault diagnosis. Expert Systems with Applications, 38(8):10205–10209, 2011. [20] L. S. Dhamande and M. B. Chaudhari. Compound gear-bearing fault feature extraction using statistical features based on time-frequency method. Measurement,125:63–77, 2018. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99029 | - |
| dc.description.abstract | 本研究探討以麥克風量測振動纜索之聲壓訊號，應用於纜索張力估算與腐蝕狀態辨識之可行性。在張力估算方面，使用弦振動理論與雙振頻法進行反算，實驗結果顯示於各種結構條件下，其張力反算誤差普遍低於 3 %，驗證本方法具備良好的準確性與穩定性。後續進行整體與局部腐蝕模擬試驗，探討不同腐蝕條件下頻譜特徵之變化，並以多層感知器模型輸入頻譜特徵進行訓練與預測，驗證集與測試集的預測結果分別達到 R^2 為 0.9577 與 0.9387，顯示本方法在纜索腐蝕辨識上的發展潛力。 | zh_TW |
| dc.description.abstract | This thesis explores the feasibility of using a microphone to measure sound pressure signals from vibrating cables for cable tension estimation and corrosion identification. For tension estimation, string vibration theory and the dual-frequency method were employed, resulting in estimation errors generally below 3% under various structural conditions, verifying the accuracy and stability of the proposed method. Corrosion tests were conducted to examine variations in vibration spectral features under different corrosion conditions. The extracted features were used to train a multilayer perceptron model, which achieved R^2 values of 0.9577 and 0.9387 on the validation and test sets, demonstrating the potential of this method for cable corrosion identification. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-21T16:06:56Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-21T16:06:56Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 目次
口試委員審定書 i 致謝 ii 摘要 iii Abstract iv 目次 v 圖次 viii 表次 x 第一章 緒論 1 1.1 研究動機與目的 1 1.2 文獻回顧 2 1.3 論文架構 5 第二章 基本理論 6 2.1 由聲壓訊號獲得結構振動的響應 6 2.1.1 結構動力響應 6 2.1.2 振動結構附近之輻射聲壓 9 2.2 纜索張力值計算 14 2.2.1 弦振動理論推導 14 2.2.2 雙振頻法理論推導 16 第三章 實驗架構與方法 18 3.1 實驗架構 18 3.2 實驗儀器 21 3.3 實驗流程 23 3.4 腐蝕試驗 24 第四章 實驗結果與討論 27 4.1 張力反算模型設計與驗證 27 4.1.1 單根鐵線實驗結果 27 4.1.2 多根鐵線實驗結果 29 4.1.3 不同線徑鐵線之實驗結果 33 4.2 鏽蝕纜索檢測 37 4.2.1 整體腐蝕組實驗結果 37 4.2.2 局部腐蝕組實驗結果 44 第五章 應用多層感知器進行損傷預測 57 5.1 多層感知器基本概念 57 5.1.1 網路架構 58 5.2 模型架構與訓練流程 60 5.2.1 資料前處理 60 5.2.2 特徵擷取方法 61 5.2.3 模型訓練流程 62 5.2.4 模型架構 63 5.2.5 訓練及監控機制 64 5.3 模型預測結果 65 第六章 結論與未來展望 68 6.1 結論 68 6.2 未來展望 69 參考文獻 70 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 纜索張力 | zh_TW |
| dc.subject | 振動法 | zh_TW |
| dc.subject | 聲壓訊號 | zh_TW |
| dc.subject | 非破壞性檢測 | zh_TW |
| dc.subject | 多層感知器 | zh_TW |
| dc.subject | Sound Pressure Signal | en |
| dc.subject | Cable Tension | en |
| dc.subject | Multilayer Perceptron | en |
| dc.subject | Non-Destructive Testing | en |
| dc.subject | Vibration Method | en |
| dc.title | 應用聲音訊號與深度學習進行纜索張力估算與腐蝕辨識 | zh_TW |
| dc.title | Cable Tension Estimation and Corrosion Identification Using Acoustic Signals and Deep Learning | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 宋家驥;王昭男;張國鎮 | zh_TW |
| dc.contributor.oralexamcommittee | Chia-Chi Sung;Chao-Nan Wang;Kuo-Chun Chang | en |
| dc.subject.keyword | 纜索張力,振動法,聲壓訊號,非破壞性檢測,多層感知器, | zh_TW |
| dc.subject.keyword | Cable Tension,Vibration Method,Sound Pressure Signal,Non-Destructive Testing,Multilayer Perceptron, | en |
| dc.relation.page | 72 | - |
| dc.identifier.doi | 10.6342/NTU202502768 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2025-08-02 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 工程科學及海洋工程學系 | - |
| dc.date.embargo-lift | 2030-07-28 | - |
| 顯示於系所單位： | 工程科學及海洋工程學系 | |
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