音射檢測法（acoustic emission testing, AET）為一種非破壞檢測，利用試體破壞釋放的彈性波，轉化電子訊號後，紀錄為各項音射參數，此方法可以有效判斷混凝土試體破壞的機制，並依據訊號變化推測混凝土內部孔隙維結構的分布。為使音射感測器貼合試體表面，本研究針對10*10*10cm方形混凝土試體經過四種不同目標溫度（125℃、250℃、400℃與600℃）的高溫延燒後，其殘餘的抗壓強度，再把試體放分別入三種不同再養護條件（高溫水中、常溫水中與常溫常濕），觀察混凝土試體利用本身未水化水泥顆粒進行自癒合的再水化反應，求得混凝土試體經過修補後力學行為的改變。實驗於試體抗壓時同時進行音射訊號收集，除了常見的音射訊號分析方式外，本研究亦使用統計分群模型－高斯混合模型（gaussian mixture model, GMM），對音射訊號提供更客觀的分析方法。
　　研究結果顯示：混凝土試體於高溫延燒至400℃時，抗壓強度仍會提升，直到600℃才有明顯下降；高溫水中與常溫水中再養護會造成離子析出，微裂縫產生，使抗壓強度下降；常溫常濕環境再養護由於水分自然的散失，導致試體些微乾縮，亦造成抗壓強度下降。音射參數中的累積撞擊次數與能量的歷時變化，其訊號量隨著延燒溫度提升而增加，且經過再養護的試體其累積撞擊次數與能量皆比直接抗壓低，代表試體微裂縫擴張，訊號能量較小。GMM分群分析可以有效將音射訊號分成張力破壞與剪力破壞，並發現GMM分析參數中的張力權重於高溫延燒至400℃時變化不大，於600℃才有明顯下降；不論再養護條件為何，張力權重的變化皆相似，且張力權重皆大於0.5，推測試體抗壓產生的訊號有一半以上皆為張力裂縫。

The purpose of this study is to investigate the acoustic emission signatures and compressive strength after concrete specimens subjecting to temperature elevation and rehydration. High temperature as in fire would make concrete structures cracking, spalling and reduce the strength causing irreversible damage. Concrete materials have the ability of self-healing when the unhydrated cement particles contact with water motivating the rehydrating process and producing more C-S-H gel to fill the cracks. The acoustic emission (AE) testing is one of the non-destructive test method, it can detect the AE signal of materials during loading and distinguish the failure mode of the material. The statistical analysis- gaussian mixture model can provide smooth approximations to fit the AE data distribution, then divide the AE data into two categories: tensile and shear.
The results showed that the compressive strength of concrete specimens increased when the temperature rising to 400℃, and decreased at 600℃. Because of the ionic concentration differences in water and shrinkage in air-dry condition, the compressive strength even lower at the temperature125℃, 250℃ and 400℃. The amount of AE signals increased with rising the temperature, but less at the rehydration specimens due to the micro-cracks propagation. The gaussian mixture model gave every AE signals tensile and shear probability which provided an objective method to discriminate the AE data. The tensile weights of the gaussian mixture model at 600℃ were lower than other temperatures which meant that 600℃ causes specimens generating more sliding failure.

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