姓名 |
楊竣傑(Chun-Chieh
Yang) |
電子郵件信箱 |
jjjyng@pchome.com.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 |
91學年第2學期 |
論文名稱(中) |
應用類神經網路預測混凝土受高溫影響之強度折減 |
論文名稱(英) |
Prediction of
Residual Strength of Heated Concrete Based on Artificial Neural
Networks |
檔案 |
本電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。
|
論文使用權限 |
校內外完全公開 |
論文語文/頁數 |
中文/124 |
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高溫延時作用後強度以及脈波波速折減程度之行為,並整理出溫度-延時-強度折減三者之間的關係,並對高溫延時實驗結果導入類神經網路觀念,建立以溫度、延時,水灰比或殘餘脈波波速預測混凝土殘餘強度的網路架構,並與現有之溫時分析法加以比較。 研究結果發現火害溫度於320~800℃且延時30分鐘以上混凝土強度和脈波波速折減較為明顯,同時火害溫度550℃以下使用殘餘強度函數來預測火害後強度折減較為準確;而在600℃以上則以類神經網路較為準確。 |
摘要(英) |
Concrete is usually
considered endurable against high temperatures. The strength of
concrete, however, is decreased due to exposure to elevated temperatures
over extended period of time. The effect of transient temperature on
residual strength of heated concrete is nonlinear and is need of
advanced research. Current study employs neural network analysis to
construct a non-parametric relationship between transient temperature
and residual strength. Two artificial neural networks are proposed and
trained based on experimental data and other published data. The inputs
of the networks include the maximum exposure temperature, the exposure
time, and the water-cement ratio (or residual pulse velocity). The
residual strengths predicted by the neural networks agree reasonably
well with known experimental results in the literature. Comparison is
also made between the predictions of the neural networks and those of
the time-temperature analysis. It is concluded that the neural network
performs better than the time-temperature analysis at temperatures
between 6000 and 800 degree Celsius. |
關鍵字(中) |
殘餘脈波波速
殘餘強度
火害混凝土
類神經網路 |
關鍵字(英) |
residual ultrasonic pulse velocity
residual compressive strength
heat concrete
Artificial Neural Network |
指導教授 |
江支弘
|