Enhancement of external wall decoration material for the building in safety inspection method

    Nai-Hsin Pan Affiliation
    ; Ching-Hsiang Tsai Affiliation
    ; Kuei-Yuan Chen Affiliation
    ; Jessie Sung Affiliation


As buildings wear out, external wall tiles or attachments will usually fall off, sometimes causing human injuries. At present, the method employed for middle-high rise buildings is mainly the method of visual inspection. The inspection results in using this method are affected by the factors of subjectivity, safety and cost. This study aims to provide a lowercost and more efficient evaluation method for inspecting the status of buildings’ external walls. This proposed method implements Forward Looking Infrared (FLIR) technology and high-resolution photographic equipment on Unmanned Aerial Vehicle (UAV) which can improve the image recording of the detection process, as well as the overall visual detection technology, and solve the existing visual detection problem of inspectors. Also, the images detected by visual inspection and UAV high-resolution video are used to develop a suitable visual evaluation process and test table for external walls. Through the test results of several cases, the deterioration status and needs for maintenance are taken into account according to the degree of performance indicators. The findings of the study is that the proposed mechanism is more efficient and lower cost for the detection of external walls or ancillary structures’ abnormal status, which is easy to use in practice.

Keyword : UAV, infrared thermal imager, external walls, buildings, maintenance management, detection method

How to Cite
Pan, N.-, H., Tsai, C.-, H., Chen, K.-, Y., & Sung, J. (2020). Enhancement of external wall decoration material for the building in safety inspection method. Journal of Civil Engineering and Management, 26(3), 216-226.
Published in Issue
Mar 10, 2020
Abstract Views
PDF Downloads
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


BeckMan, G. H., Polyzois, D., & Cha, Y. J. (2019). Deep learning-based automatic volumetric damage quantification using depth camera. Automation in Construction, 99, 114–124.

Bošnak, M., Matko, D., & Blažič, S. (2012). Quadrocopter control using an on-board video system with off-board processing. Robotics and Autonomous Systems, 60(4), 657–667.

Cha, Y. J., Choi, W., & Buyukozturk, O. (2017). Deep learningbased crack damage detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 32(5), 361–378.

Cha, Y. J., Choi, W., Suh, G., Mahmoudkhani, S., & Buyukozturk, O. (2018). Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Computer-Aided Civil and Infrastructure Engineering, 33(9), 731–747.

Chen, Y., Shen, Y., Liu, X., & Zhong, B. (2015). 3D object tracking via image sets and depth-based occlusion detection, Signal Processing, 112, 146–153.

Chiao, M., & Kao, Y. W. (2015). Discussion on testing method of bond strength of exterior wall decorative bricks. Sichuan Building Materials, 1, 97–98.

de Freitas, S. S., de Freitas, V. P., & Barreira, E. (2014). Detection of facade plaster detachments using infrared thermography – A nondestructive technique. Construction and Building Materials, 15, 80–87.

Edis, E., Flores-Colen, I., & de Brito, J. (2013). Thermographic inspection of adhered ceramic claddings: Limitation and conditioning factors. Journal of Performance of Constructed Facilities, 6(27), 737–747.

Engel, J., Sturm, J., & Cremers, D. (2014). Scale-aware navigation of a low-cost quadrocopter with a monocular camera. Robotics and Autonomous Systems, 62(11), 1646–1656.

Fonstad, M. A., Dietrich, J. T., Courville, B. C., Jensen, J. L., & Carbonneau, P. E. (2013). Topographic structure from motion: a new development in photogrammetric measurement. Earth Surface Processes and Landforms, 38, 421–430.

Green, S., Bevan A., & Shapland, M. (2014). A comparative assessment of structure from motion methods for archaeological research. Journal of Archaeological Science, 46(1), 173–181.

Hashim, K. A., Ahmad, A., Samad, Abd. M., Nizam Tahar, K., & Udin, W. S. (2012). Integration of low altitude aerial & terrestrial photogrammetry data in 3D heritage building modelling. In 2012 IEEE Control and System Graduate Research Colloquium, ICSGRC (pp. 225–230), Shah Alam, Malaysia.

Hehn, M., & D’Andrea, R. (2014). A frequency domain iterative learning algorithm for high-performance, periodic quadrocopter maneuvers. Mechatronics, 24(8), 954–965.

Huang, S. M., Chiang, L. W., & Chen, C. H. (2010). The method by visual inspection of the building siding public security research. Journal of Property Management, 1(1), 35–44.

Kang, D., & Cha, Y. J. (2018). Autonomous UAVs for structural health monitoring using deep learning and an ultrasonic beacon system with geo-tagging. Computer-Aided Civil and Infrastructure Engineering, 33(10), 885–902.

Koutsoudis, A., Vidma, B., Ioannakis, G., Arnaoutoglou, F., Pavlidis, G., & Chamzas, C. (2014). Multi-image 3D reconstruction data evaluation. Journal of Cultural Heritage, 15(1), 73–79.

Lee, L. C. (2015). Introduction to infrared camera inspection application. In Taipei Professional Civil Engineering Association, 69, 9–10.

Lee, Y., Liu, Y. F., Chou, H., Bai, L., & Chiang, W. (2014). Application of infrared thermography to detecting bonding defects of exterior wall decorative bricks. Residence Technology, 1, 52–54.

Lin, K. T. (2014). A feasibility study on infrared thermal imaging technology to inspect defects in exterior wall tile system interface (Master’s thesis). Institute of Materials Engineering National Taiwan Ocean University, R.O.C, Taiwan.

Mancini, F., Dubbini, M., Gattelli, M., Stecchi, F., Fabbri, S., & Gabbianelli, G. (2013). Using unmanned aerial vehicles (UAV) for high-resolution reconstruction of topography: the structure from motion approach on coastal environments. Remote Sensing, 5(12), 6880–6898.

Moore, J., Tadonada, H., Kirsche, K., Perry, J., Remen, F., & Tse, Z. T. H. (2018). Facility inspection using UAVs: a case study in the University of Georgia campus. International Journal of Remote Sensing, 39(21), 7189–7200.

Nex, F., & Remondino, F. (2014). UAV for 3D mapping applications: A review. Applied Geomatics, 6(1), 1–15.

Tai, P. Y. (2008): Nondestructive testing of exterior wall tiles by tap tone method (Master’s thesis). Graduate Institute of Urban Development and Architecture, National University of Kaohsiung, R.O.C., Taiwan.

Technews. (n.d.).

Turner, D., Lucieer, A., & Watson, C. (2012). An automated technique for generating georectified mosaics from ultra-high resolution Unmanned Aerial Vehicle (UAV) imagery, based on Structure from Motion (SFM) point clouds. Remote Sensing, 4(5), 1392–1410.

Yeh, L. Y. (2014). Technical application of structural health monitoring system for super-tall buildings. Guangdong Architecture Civil Engineering, 2, 62–64.

Yen, J. C. (2013). Testing the degradation of the exterior wall tiles of school buildings by tap tone method (Master’s thesis). Department of Civil Engineering, Feng Chia University, R.O.C., Taiwan.