臺灣博碩士論文加值系統

台灣中小學校舍建築大多以鋼筋混凝土結構物為主，加上台灣環境高溫潮濕，容易造成混凝土發生中性化現象，導致混凝土破壞，進而加速鋼筋腐蝕，降低結構耐震能力。在校舍耐震能力評估流程中，詳細評估檢測混凝土抗壓強度及混凝土中性化程度，現行採用現場鑽心取樣方法，但此方法會破壞結構物，然而，目前尚無非破壞性檢測方法直接量測混凝土中性化深度。
因此，本研究先利用非破壞性檢測方法求得混凝土抗壓強度，再由混凝土抗壓強度來推測混凝土中性化深度，但非破懷性檢測仍存在誤差過大的疑慮，所以應用生物共生演算法最小平方差支持向量機（SOS-LSSVM），建立混凝土抗壓強度推論模式及混凝土中性化程度推論模式。
此外，依照校舍耐震評估流程，校舍經過初步評估後未達到耐震指標，則須進入詳細評估，詳細評估所花費的時間與金額相當可觀，運用演化式非破壞性混凝土中性化程度推論方法，過濾篩選掉一些不必要進入詳細評估之案例，不僅能降低初步評估錯誤個數，如此亦能減少在詳細評估所需鑽心取樣數量，及節省所耗費的金額。

School Building is usually reinforced concrete structure in Taiwan. Because Taiwan environment is high temperature and wet weather, the concrete occurs the neutral phenomenon. The concrete failure causes corrosion of reinforcing steel and reduces seismic capacity assessment of structures. According to the process of seismic performance of school building, detection of the compressive strength of concrete and the neutralization of concrete depth in the detailed assessment. Currently the detailed assessment used the cylindrical specimen of concrete, but this mothed causes the structures be damage. However, there is no non-destructive testing directly measured the neutralization of concrete depth.
Therefore, this research used non-destructive testing methods to obtain the compressive strength of concrete, and inferenced the neutralization of concrete depth by the compressive strength of concrete. Because non-destructive testing methods had a large error, this research applied Symbiotic Organisms Search –Hybrid Least Squares Support Vector Machine (SOS-LSSVM) to establish the compressive strength of concrete inference model and the neutralization of concrete depth inference model.
In addition, in the process of seismic performance of school building, the school building must pass through the preliminary assessment to decide whether it should processed to go on the detailed assessment which cost much money and took much time. So this research used the evolutionary non-destructive neutralization of concrete depth inference method to filter the unnecessary cases to enter the detailed assessment. This method can reduce the cases which are unnecessary to enter the preliminary assessment, but also can reduce the number of the cylindrical specimen of concrete which cost much money and took much time in the detailed assessment.

摘要 I
Abstract II
誌謝 IV
目錄 VI
圖目錄 VIII
表目錄 IX
第一章 緒論 1
1.1研究動機 1
1.2研究目的 3
1.3研究流程 4
1.4論文架構 6
第二章 文獻回顧 8
2.1 非破壞性檢測 8
2.1.1 反彈錘試驗法 9
2.1.2 電子式反彈錘Silver Schmidt 10
2.2 混凝土中性化 11
2.2.1 鋼筋混凝土中性化腐蝕過程 12
2.2.2 混凝土中性化深度預測公式 13
2.3 校舍耐震評估 15
2.3.1 初步評估 16
2.3.2 詳細評估 17
2.4 人工智慧 19
2.4.1 倒傳遞類神經網路(BPNN) 20
2.4.2 支持向量機(SVM) 21
2.4.3 最小平方差支持向量機(LS-SVM) 23
2.4.4 演化式支持向量機 (ESIM) 24
2.4.5 演化式最小平差支持向量機(ELSIM) 26
2.4.6 生物共生演算法最小平方差支持向量機(SOS-LSSVM) 28
2.5 θ-means演算法 33
第三章 演化式非破壞性混凝土中性化程度推論方法 36
3.1 混凝土抗壓強度推論模式 39
3.1.1 確認混凝土抗壓強度影響因子 41
3.1.2 建立混凝土抗壓強度案例資料庫 41
3.1.3 建立混凝土抗壓強度推論模式 45
3.1.4 預測混凝土抗壓強度 49
3.1.5 混凝土抗壓強度推論模式訓練與測試 50
3.1.6 不同模組比較 51
3.2 混凝土中性化程度推論模式 53
3.2.1 確認混凝土中性化程度影響因子 55
3.2.2 建立混凝土中性化程度案例資料庫 55
3.2.3 建立混凝土中性化程度推論模式 58
3.2.4 預測混凝土中性化程度 60
3.2.5 混凝土中性化程度推論模式訓練與測試 60
3.2.6 不同模組比較 66
3.2.7 與理論公式比較 68
第四章 推論方法之應用 69
4.1 案例描述 69
4.2 案例分析 69
第五章 結論與建議 72
5.1 結論 72
5.2 建議 73
參考文獻 74

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