現行多數量測儀器僅能針對局部區域進行資料獲取，無論是接觸式量測還是非接觸式量測皆受儀器運作原理限制，較難以完整獲取全域資訊。相較之下，利用光達進行點雲資料蒐集，能克服此類限制而快速且完整獲取整體結構物外部幾何資訊。本研究針對利用點雲資料獲取結構物受地震影響前後之外觀幾何變化，建立出一套點雲數據演算流程，從原始點雲資料逐步進行座標轉換、座標系統對齊、邊界點雲提取、邊界點雲分群，最終依據分群結果進行最小二乘法擬合，推測結構體受地震影響後之整體外觀變化與局部構件變形量等資訊，並以國震中心台南實驗室之七層樓縮尺結構物振動台試驗進行實驗數據比對。
對於結構物變形量之觀測，資料座標系統的一致性對於量測結果影響甚大，本研究嘗試不設定遠端參考點，而改以待測物之局部特徵為一致性標準，為確保不同次掃描獲取之點雲資料有共同原點，本研究以迭代最近點演算法(iterative closest point, ICP)設定固定參考點進行座標系統對齊。為獲取待測物之邊界幾何資訊，本研究之邊界點雲分別採用主成分分析(principal component analysis, PCA)、手動過濾以及曲面交線等三種方式進行邊界幾何資訊提取，並比較三種演算法之差異；其中以資料點幾何特徵判定邊界之提取方式進行時因資料量龐大而出現演算時間過於冗長之情形，本研究為探討降低程式硬體需求及演算時間之方式，比較將原始資料進行切割後進行演算法處理對於演算時間及演算成果之影響，結果顯示將點雲資料先進行分割再計算能大幅降低演算處理時間且成果仍具完整性，顯示點雲資料雖具有耦合特徵，演算過程之解耦處理似乎不造成明顯影響。根據本研究之成果與拉線式位移計(string potentiometers, String Pots)以及動作捕捉系統(Motion Capture)於實驗中量測結果進行比較，顯示本研究所提出之點雲特徵演算流程在獲取結構物受地震作用後之幾何變化，於各不同方向之位移資料皆相當具有代表性，且能夠對整體結構物所有構件進行量測，而無須進行標的物黏貼或量測位置選定等事前作業，對於實務應用上更為有利。此外利用本研究之流程亦能針對結構物各構件之斷面積進行量測，並重建整體結構物在事件前後之外觀幾何，允許使用者或管理者在事件前後觀察整體結構外型，以及各構件不同位置之斷面尺寸變化，此為其他量測方法所無法準確獲取之幾何資訊。

Current most measuring instruments used in structures deformation determine can only acquire data for a local area. In this study, we present a procedure for measuring the displacement of the structure by analyzing the point cloud data. In this way, we can get the global information of the deformed structure efficiently. This procedure contains a set of algorithmic processes including coordinate conversion, coordinate system alignment, boundary point cloud extraction, and point cloud clustering. Iterative closet point (ICP) algorithm is used to ensure that the coordinate system of two sets of point cloud data scanned by the structure data before and after the earthquake excited is the same. In the point cloud boundary extraction step, three methods of principal component analysis (PCA) algorithm, manual filtering, and surface fitting to find the edge of intersection are used to compare the results. Besides, we try to segment the data of the PCA algorithm for effective analysis. The results show that segment the data can greatly reduce the calculation processing time and the results are still in high degree of integrity. By comparing the displacements of structural columns obtained from our procedure with the results measured from String Potentiometer and Motion Capture, we verified our procedure is representative. In addition, using our procedure can also measure the cross-sectional area of each component of the structure, which can’t be accurately obtained by other measurement methods.

[1] 詹智翔(2013)，「使用車載光達進行路面平整度偵測可行性研究」，國立成功大學土木工程研究所碩士論文。
[2] 蔡家修(2016)，「點雲分析應用於物件辨識與地震災後勘查」，國立成功大學土木工程研究所碩士論文。
[3] 吳承晏(2018)，「由散亂點雲自動辨識結構基本物件–辨識率與精確度」，國立成功大學土木工程研究所碩士論文。
[4] Adams, R., Bischof, L., 1994, Seeded region growing. IEEE Transactions on pattern analysis and machine intelligence, 16(6), 641-647.
[5] Antonarakis, A. S., Richards, K. S., Brasington, J., 2008, Object-based land cover classification using airborne LiDAR. Remote Sensing of environment, 112(6), 2988-2998.
[6] Armesto-González , J., Riveiro-Rodríguez, B., González-Aguilera, D., Rivas-Brea, M. T., 2010, Terrestrial laser scanning intensity data applied to damage detection for historical buildings. Journal of Archaeological Science, 37(12): p. 3037-3047.
[7] Almeida, D. R. A., Broadbent, E. N., Zambrano, A. M. A., Wilkinson, B. E., Ferreira, M. E., Chazdon, R., Brancalion, P. H. S., 2019, Monitoring the structure of forest restoration plantations with a drone-lidar system. International Journal of Applied Earth Observation and Geoinformation, 79, 192-198.
[8] Baik, A., 2017, From point cloud to jeddah heritage BIM nasif historical house–case study. Digital Applications in Archaeology and Cultural Heritage, 4,1-18.
[9] Bentley, J. L., 1975, Multidimensional binary search trees used for associative searching. Communications of the ACM, 18(9), 509-517.
[10] Besl, P. J., McKay, N. D., 1992, Method for registration of 3-D shapes. In Sensor fusion IV: control paradigms and data structures (Vol. 1611, pp. 586-606). SPIE.
[11] Bezdek, J.C., Coray, C., Gunderson, R., Watson, J., 1981, Detection and characterization of cluster substructure i. linear structure: Fuzzy c-lines. SIAM Journal on Applied Mathematics, 40(2): p. 339-357.
[12] Bazazian, D., Casas, J. R., Ruiz-Hidalgo, J., 2015, Fast and robust edge extraction in unorganized point clouds. In Digital Image Computing: Techniques and Applications (DICTA), 2015 International Conference on (pp. 1-8). IEEE.
[13] Che, E., Jung, J., Olsen, M. J., 2019, Object recognition, segmentation, and classification of mobile laser scanning point clouds: A state of the art review. Sensors, 19(4), 810.
[14] Dunn, J. C., 1973, A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters.
[15] Dubayah, R. O., Drake, J. B., 2000, Lidar remote sensing for forestry. Journal of forestry, 98(6), 44-46.
[16] Ester, M., Kriegel, H. P., Sander, J., Xu, X., 1996, A density-based algorithm for discovering clusters in large spatial databases with noise. in Kdd.
[17] Fischler, M. A., Bolles, R. C., 1981, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381-395
[18] Ferrara, R., Virdis, S. G., Ventura, A., Ghisu, T., Duce, P., Pellizzaro, G., 2018, An automated approach for wood-leaf separation from terrestrial LIDAR point clouds using the density based clustering algorithm DBSCAN. Agricultural and forest meteorology, 262, 434-444.
[19] Gikas, V., 2012, Three-dimensional laser scanning for geometry documentation and construction management of highway tunnels during excavation. Sensors, 12(8), 11249-11270
[20] Hough, P. V., 1962, U.S. Patent No. 3,069,654. Washington, DC: U.S. Patent and Trademark Office.’Method and means for recognizing complex patterns’.
[21] H.S. Park, H.M. Lee, H. Adeli, I. Lee, 2007, A new approach for health monitoring of structures: terrestrial laser scanning. Computer-Aided Civil and Infrastructure Engineering, 22 (1) , pp. 19-30.
[22] He, Y., Liang, B., Yang, J., Li, S., He, J., 2017, An iterative closest points algorithm for registration of 3D laser scanner point clouds with geometric features. Sensors, 17(8), 1862.
[23] Jo, Y. H., Hong, S., 2019, Three-dimensional digital documentation of cultural heritage site based on the convergence of terrestrial laser scanning and unmanned aerial vehicle photogrammetry. ISPRS International Journal of Geo-Information, 8(2), 53.
[24] Kim, M. K., Cheng, J. C., Sohn, H., Chang, C. C., 2015, A framework for dimensional and surface quality assessment of precast concrete elements using BIM and 3D laser scanning. Automation in Construction, 49, 225-238.
[25] Kashani, A. G., Crawford, P. S., Biswas, S. K., Graettinger, A. J., Grau, D., 2015, Automated tornado damage assessment and wind speed estimation based on terrestrial laser scanning. Journal of Computing in Civil Engineering, 29(3), 04014051.
[26] Klokov, R., Lempitsky, V., 2017, Escape from cells: Deep kd-networks for the recognition of 3d point cloud models. In Proceedings of the IEEE International Conference on Computer Vision (pp. 863-872).
[27] Kuo, K., Itakura, K., Hosoi, F., 2019, Leaf segmentation based on k-means algorithm to obtain leaf angle distribution using terrestrial LiDAR. Remote Sensing, 11(21), 2536.
[28] Lercari, N., 2019, Monitoring earthen archaeological heritage using multi-temporal terrestrial laser scanning and surface change detection. Journal of Cultural Heritage, 39, 152-165.
[29] Lee, J., Lee, K. C., Lee, S., Lee, Y. J., Sim, S. H., 2019, Long‐term displacement measurement of bridges using a LiDAR system. Structural Control and Health Monitoring, 26(10), e2428.
[30] MacQueen, J., 1967, Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (Vol. 1, No. 14, pp. 281-297).
[31] Mukupa, W., Roberts, G. W., Hancock, C. M., Al-Manasir, K., 2017, A review of the use of terrestrial laser scanning application for change detection and deformation monitoring of structures. Survey review, 49(353), 99-116.
[32] Nilsson, M., 1996, Estimation of tree heights and stand volume using an airborne lidar system. Remote sensing of environment, 56(1), 1-7.
[33] Nuchter, A., Lingemann, K., Hertzberg, J., 2007, Cached kd tree search for ICP algorithms. In Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007) (pp. 419-426). IEEE.
[34] Pearson, K., 1901, Principal components analysis. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 6(2), 559.
[35] Pal, N. R., Bezdek, J. C., 1995, On cluster validity for the fuzzy c-means model. IEEE Transactions on Fuzzy systems, 3(3), 370-379.
[36] Pesci, A., Teza, G., Bonali, E., Casula, G., Boschi, E., 2013, A laser scanning-based method for fast estimation of seismic-induced building deformations. ISPRS journal of photogrammetry and remote sensing, 79, 185-198.
[37] Pan, S., Guan, H., Yu, Y., Li, J.,Peng, D, 2019, A comparative land-cover classification feature study of learning algorithms: DBM, PCA, and RF using multispectral LiDAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(4), 1314-1326.
[38] Rabbani, T., Van Den Heuvel, F., Vosselmann, G., 2006, Segmentation of point clouds using smoothness constraint. International archives of photogrammetry, remote sensing and spatial information sciences, 36(5), 248-253.
[39] Rusinkiewicz, S., Levoy, M., 2001, Efficient variants of the ICP algorithm. In Proceedings third international conference on 3-D digital imaging and modeling (pp. 145-152). IEEE.
[40] Singh, S. K., Raval, S., Banerjee, B, 2021, A robust approach to identify roof bolts in 3D point cloud data captured from a mobile laser scanner. International Journal of Mining Science and Technology, 31(2), 303-312
[41] Suzuki, T., Puranam, A. Y., Elwood, K. J., Lee, H. J., Hsiao, F. P., Hwang, S. J., 2021, Shake table tests of seven-story reinforced concrete structures with torsional irregularities: Test program and datasets. Earthquake Spectra, 87552930211016869.
[42] Vosselman, G., Dijkman, S., 2001, 3D building model reconstruction from point clouds and ground plans. International archives of photogrammetry remote sensing and spatial information sciences, 34(3/W4), 37-44..
[43] Vosselman, G., Gorte, B. G., Sithole, G., Rabbani, T, 2004, Recognising structure in laser scanner point clouds. International archives of photogrammetry, remote sensing and spatial information sciences, 46(8), 33-38.
[44] Vezočnik, R., Ambrožič, T., Sterle, O., Bilban, G., Pfeifer, N., Stopar, B., 2009, Use of terrestrial laser scanning technology for long term high precision deformation monitoring. Sensors, 9(12), 9873-9895.
[45] Walton, G., Delaloye, D., Diederichs, M. S., 2014, Development of an elliptical fitting algorithm to improve change detection capabilities with applications for deformation monitoring in circular tunnels and shafts. Tunnelling and Underground Space Technology, 43, 336-349.
[46] Wang, Q., Kim, M. K., Cheng, J. C., Sohn, H., 2016, Automated quality assessment of precast concrete elements with geometry irregularities using terrestrial laser scanning. Automation in Construction, 68, 170-182.
[47] Xu, B., Jiang, W., Shan, J., Zhang, J., Li, L., 2016, Investigation on the weighted ransac approaches for building roof plane segmentation from lidar point clouds. Remote Sensing, 8(1), 5.
[48] Yu, B., Liu, H., Wu, J., Lin, W. M., 2009, Investigating impacts of urban morphology on spatio-temporal variations of solar radiation with airborne LIDAR data and a solar flux model: a case study of downtown Houston. International Journal of Remote Sensing, 30(17), 4359-4385.
[49] Zeibak-Shini, R., Sacks, R., Ma, L., Filin, S, 2016, Towards generation of as-damaged BIM models using laser-scanning and as-built BIM: First estimate of as-damaged locations of reinforced concrete frame members in masonry infill structures. Advanced Engineering Informatics, 30(3), 312-326.
[50] Zhou, Z., Gong, J., Hu, X., 2019, Community-scale multi-level post-hurricane damage assessment of residential buildings using multi-temporal airborne LiDAR data. Automation in Construction, 98, 30-45.