結構物建造完畢後若缺乏週期性維護與檢測，當結構物或其構件有損傷或缺陷產生時，結構物的安全性及耐久性將受到影響，因此在土木工程領域中，對於結構物損傷及缺陷之探傷檢測乃為相當重要的一環。吾人透過對結構物進行定期檢測以辨識其損傷缺陷，並對損傷缺陷進行評估的過程稱為結構健康檢測(structural health monitoring, SHM)。近年來隨著光學感測技術的進步，雷射掃描(laser scanning)的發展已逐漸成熟，學界及業界已有許多專家、學者與工程師應用地面雷射掃描(terrestrial laser scanning, TLS)擷取結構物詳細且完整的外觀資訊(facade information)，然掃描儀所擷取之資訊皆為大量且散亂之點雲資料(point cloud data, PCD)。點雲資料的後處理，包括資料擷取(acquisition)、資料結合(coupling)、及資料重建(reconstruction)等皆為目前以光學感測應用於結構健康檢測相關研究之主要發展方向。據此，本研究嘗試使用數種數據演算法對結構物與其構件之點雲資料進行後處理，以探討各類演算法對於不同結構物件點雲資料之適用性，以期使光學感測於結構健康檢測之應用提出些許貢獻。本研究核心主題有二：其一為使用資料分群演算法(data clustering algorithms)對結構表面材料破壞或劣化區域之點雲資料進行量化損傷辨識，具體包括模糊c均值法(Fuzzy c-means, FCM)、k均值法(k-means, KM)、減法聚類法(subtractive clustering method, SCM)以及具噪密度基礎分群法(density-based spatial clustering of applications with noise, DBSCAN)，再依各演算法之分析結果提出較適用於結構表面損傷辨識之資料分群演算法；其二為整合邊界提取(edge extraction)演算法及最小二乘法(least mean square, LMS)對實尺寸鋼筋混凝土梁、版等構件進行變行曲線擬合，以期獲得物件之全域幾何變化，並與具較高量測精確度之線性可變差動變壓器(linera variable differential transformer, LVDT)所得到之結果進行比較，以探討其於單點精度之表現。由本研究之結果可得知，使用模糊c均值法對結構表面損傷之反射強度(reflection intensity)資料進行資料分群(data clustering)能夠獲得最佳辨識成果，所需計算資源亦較其他演算法少，執行快速；另外結合邊界提取演算法及最小二乘法所得之鋼筋混凝土構件變形資訊較不受構件表面平整度不佳的影響，經與LVDT之單點量測值比較，其分析結果相當準確，且使用點雲資料亦具全域量測優勢。

After the construction of the structure, the safety and durability of the structure may be affected by the damage or defects of the structure or its components due to the lack of periodic maintenance and monitoring. Therefore, detection of structural damage and defects is a very important part in civil engineering. The process of identification and assessment of structural damage and defects through the regular inspection is called structural health monitoring. In recent years, laser scanning technology has gradually become fully developed due to the progress of optical sensing technology, many experts, academics and engineers use terrestrial laser scanner to capture detailed and complete facade information of structures. There are two major topics in this study. First, applying data clustering algorithms to point cloud data to identify and quantify the material damage and deterioration of structural surface, then compare each result analyzed by algorithms to find the most suitable algorithm. The algorithms are fuzzy c-means, k-means, subtractive clustering method and density-based spatial clustering of applications with noise. Secondly, applying edge extraction algorithm and least mean square method to fit the overall deformed curves of components, then compare results with deflection obtained by LVDT. The results showed that applying fuzzy c-means to reflection intensity data can obtain the best identification result, and applying edge extraction algorithm and least mean square method to fit the deformed curve is pretty precise and can reduce the effect of surface roughness.

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