| 姓名 | 石晏彰(Yen-chang Shih) | 電子郵件信箱 | s9311602@mail.cyut.edu.tw | |
| 畢業系所 | 營建工程系碩士班(Department and Graduate Institute of Constrction Engineering) | |||
| 畢業學位 | 碩士(Master) | 畢業時期 | 94學年第2學期 | |
| 論文名稱(中) | 火害後混凝土強度預測模式之比較研究 | |||
| 論文名稱(英) | Comparative Study on the Prediction of Residual Strength of Concrete after Fire Exposure | |||
| 檔案 |
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論文使用權限 | 校內外完全公開 | |
| 論文語文/頁數 | 中文/111 | |||
| 摘要(中) | 混凝土為一種具有良好耐火性的材料，但在受高溫延時作用後仍會造成材料性質的改變，其中以強度折減為工程上重要評估因素之一。故本研究主要針對混凝土受不同溫度作用下之波速與強度之關係，並整理出溫度、骨材含量、水灰比、波速及強度之間的關係，並對不同溫度、水灰比、骨材含量、波速與強度導入類神經網路觀念，分為訓練與測試二部分，模擬火害後之強度情況，並利用多變數回歸分析來預測火害後的強度的區間估計，建立類神經網路模型之可靠度及現地強度評估之價值。 本研究所選用之類神經網路為倒傳遞演算法，研究主要目的為探討類神經網路預測之值，是否與實際實驗所得之混凝土殘餘抗壓強度是否吻合。實驗分析發現火害溫度約在300℃~400℃且延時2小時，混凝土強度並不會折減太多，溫度達500℃之後因受到延時的影響因素會愈來愈高，使得火害後混凝土強度會有明顯的下降趨勢，但是溫度達到600℃以上且延時2小時的試體表面會有明顯的裂紋出現，使用類神經網路模擬超音波及敲擊回音之殘餘抗壓強度，其相關係數均達0.94以上，利用此方法能有助於提昇現地強度評估的品質，最後搭配訊號分析之技巧來判斷火害前後的頻譜變化關係，利用訊號處理判斷火害前後的強度變化，發現火害前的頻率是較大的，而火害後的頻率是較小的，可見火害後對混凝土強度的勁度損失有一定影響。 | |||
| 摘要(英) | Concrete is considered a construction material of good fire endurance. The strength reduction of concrete after fire exposure has been a very popular research topic for more than thirty years. The objective of the current study is to investigate the residual strength of concrete in further details and to contribute to the effectiveness of after-fire assessment. Factors explored include exposure temperature, water/cement ratio, content of coarse aggregate, and p-wave velocity. P-wave velocity was obtained using surface measurement techniques such as ultrasound pulse-velocity, dry-contact ultrasonic probes, and impact echo. Experimental programs were executed to gather residual strength and residual p-wave velocity of concrete specimens. Experimental results from another research project were also included in the data sets. The data variability in residual properties of concrete was analyzed both by statistical analysis and artificial neural networks. The correlation coefficients are 0.94 or higher between the predicted values and target values in all cases of normalized residual strength prediction using the artificial neural network. | |||
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