混凝土為目前國內通用之建築材料，近年來爐石常用來添入混凝土以考量環保、工作性、經濟性與耐久性。火災則是常見災害之一，其中高溫常會造成混凝土結構物之損害，基於經劑性考量，部份結構構件經由適當的補強修復後繼續使用，因此對於高溫損傷程度之判斷相當重要，混凝土殘餘強度及其內部所受到之溫度為主要兩項用來判斷之指標。本研究中利用表面波譜法(SASW)來評估混凝土內部縱波(P-wave)波速以進一步推估混凝土殘餘強度，並利用支持向量機(SVM)具良好計算能力之特性進行評估混凝土內部所受到之溫度。
首先製作以單一水膠比(W/B=0.6)灌製不同爐石取代量(0, 10, 30, 40, 60, 80%)置於高溫爐中分別以不同溫度(25, 200, 400, 600, 800oC)加熱後進行試驗，結果顯示在低於600oC時，較佳爐石取代量為20-30%，且當混凝土受到600oC高熱作用後，爐石取代量超過60%，其抗壓強度與動彈性模數下降相當顯著，同時利用統計迴歸方式得到不同爐石取代量與不同高溫之混凝土抗壓強度與動彈性係數之預測公式。
另一方面，製作不同混凝土塊狀試體在單向度高溫作用後，以表面波譜法進行檢測。利用小波分析處理過濾實作檢測與數值模擬之接收器訊號後，可得到15~60 KHz之高頻訊號，進行頻散曲線繪製與內部縱波反算，與超音波速對照結果顯示，在400、600、800oC高溫作用後，經由實際檢測所計算出內部縱波波速平均誤差分別為10.0、5.5、12.0%，而數值模擬所計算出內部縱波波速平均誤差分別為8.7、8.7、10.6%。
接著鑽心切片試驗結果（劈裂強度與吸水率）與超音波速量測結果可組合成四種輸入資料，並利用支持向量機以建立評估混凝土內部溫度之模式。結果顯示，含有高溫受損後混凝土內部縱波之資料組合，在訓練資料比分別為1/4及1/3時，其利用SVM評估內部高溫之正確率為77.37~81.23%與82.94~89.80%，不含高溫受損後混凝土內部縱波之資料組合，在訓練資料比分別為1/4及1/3時之正確率為53.33與61.37%。故可得知，高溫受損後之混凝土縱波波速為評估內部高溫歷程之重要資料。

Concrete is one of the most commonly used construction materials in civil engineering. The slag is usually added to the concrete for considering its effects on the environment, workability, durability and economics in recent years. On the other hand, fire disaster is one of the familiar accidents which cause great damage of concrete building exposed to high temperature. For the economic consideration, some of these fire-damaged concrete buildings can be recovered to their usable functions through the repairing and retrofitting methods. Therefore, it is important to investigate the situation of damage induced by the exposure to high temperature. Both of the residual compressive strength and exposed temperature are usually used to evaluate the situation of damage. In this study, the spectral analysis of surface wave (SASW) is used to evaluate the internal P-wave velocity of concrete and further determine the compressive strength of concrete. In addition, the support vector machine (SVM) approach is used to evaluate the exposed temperature due to its advantage of computational efficiency.
First, the concrete cylinder specimens (W/B=0.6) were cast using several slag replacements (0, 10, 30, 40, 60, 80%) and tested after being placed in the furnace to exposed different temperatures (25, 200, 400, 600, 800oC), respectively. The results show that the residual engineering capability of concrete has an optimum replacement of 20-30% slag under 600oC and the engineering properties of concrete are reduced significantly at each elevated temperature for the GGBFS replacement greater than 60%. Moreover, the equation of compressive strength and Young’s modulus of concrete (W/B=0.6) at age of 28 days between different slag replacements and exposed temperatures are obtained by statisical regression equations.
Then, the concrete beam specimens were cast and exposed to one-sided temperature of 400, 600, 800oC, respectively. The spectral analysis of surface wave (SASW) method is used to evaluate the internal P-wave velocity of concrete from surface. In addition, the numerical simulation is also used to verify the results. Both of experimental and numerical signal are obtained the frequency range of 15-60 KHz by using the wavelet filter. The results show that the average error of experiment is 10.0, 5.5 and 12.0% with the concrete exposed to 400, 600, 800oC, respectively. The average error of numerical simulation is 8.7, 8.7 and 10.6% with the concrete exposed to 400, 600, 800oC, respectively.
The testing results of ultrasonic pulse velocity (UPV) and temperature from disk-shaped specimens are generated and used as the training and testing data for SVM. By using the data of UPV, the correctness of predicted temperatures with 1/4 and 1/3 of total data set is in the ranges of 77.37-81.23% and 82.94~89.80%,, respectively. Without UPV, the correctness of predicted temperatures is reduced to 53.33% and 61.37%, respectively. As a result, the SVM analysis shows that the most effective parameter to increase the correctness index of the predicted exposed temperature was identified as the ultrasonic pulse velocity of concrete. It also shows that the accuracy of estimation for the SVM analysis increases with the increases of number of effective parameters and the ratio of training data sets to total data sets being considered in the calculation of SVM modeling.

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