Damage detection on cooling tower shell based on model textures





Cooling tower, texture, diffuse, normal, displacement, concrete damage, CNN


Ensuring the structural integrity of cooling towers is paramount for safety and efficient operation. This paper presents a novel approach for detecting damage on cooling tower shells, utilising textures derived from laser scanning and close-range photogrammetry. The proposed method delves beyond the limitations of solely relying on colour information by harnessing the rich details embedded in various textures, including diffuse, normal, displacement, and occlusion. The study demonstrates the efficacy of this approach for identifying significant concrete damage. A Convolutional Neural Network (CNN) trained on diffuse textures successfully detects high damage instances with minimal misdetection. However, accurately pinpointing low damage, often manifesting as subtle cracks, and mimicking other patterns like air pores, ribbing, and colour variations, presents a formidable challenge. To tackle this challenge, the authors introduce a novel "composed raster layer" that merges information from multiple textures. This pre-processed layer amplifies the visual cues associated with low damage, facilitating its differentiation from similar patterns. While the current implementation employing multi-resolution segmentation and rule-based classification exhibits promising results, further optimization is acknowledged to refine the accuracy of low damage detection. The successful application of textures commonly used in rendering techniques underscores their remarkable potential for enhancing damage detection in civil engineering applications. While acknowledging limitations such as the analysis of a single cooling tower and the reliance on specific software for damage detection, the study proposes future research directions. This research holds significant implications for the field of civil engineering by offering a promising approach for automated and efficient damage detection on cooling tower shells.


Download data is not yet available.


E. Asadzadeh and M. Alam, “A survey on hyperbolic cooling towers.”, International Journal of Civil, Environmental, Structural, Construction and Architectural Engineering 8, 1079-1091 (2014).

Gupta, A. K., & Maestrini, S. (1986). Investigation on hyperbolic cooling tower ultimate behaviour. Engineering Structures, 8(2), 87-92.

W. B. Kraetzig, et al. “From large natural draft cooling tower shells to chimneys of solar upwind power plants.” Symposium of the International Association for Shell and Spatial Structures (2009). 3. T. Hara and P. L. Gould, “Local–global analysis of cooling tower with cutouts.”, Computers & structures 80.27-30, 2157-2166 (2002).

S. Kulkarni and A. V. Kulkarni, “Case study of seismic effect on hyperbolic cooling towers.” Civil Environ. Res 6.11, 85-94 (2014).

S. Kulkarni and A. V. Kulkarni, “Case study of seismic effect on hyperbolic cooling towers.” Civil Environ. Res 6.11, 85-94 (2014).

K. Zdanowicz, “Geodetic monitoring of hyperbolic cooling towers deformation”, Civil Engenering, Wyd. Pol Krak, 3 108 (2011)

T. Głowacki, P. Grzempowski, E. Sudoł, J. Wajs, M. Zając, “The assessment of the application of terrestrial laser scanning for measuring the geometrics of cooling towers”, Geomatics, Landmanagement and Landscape, 4, 49–57 (2016).

M. Bajtala, et al. “Exploitation of Terrestrial Laser Scanning in Determining of Geometry of a Factory Chimney.”, Proceedings of the 5th International Conference on Engineering Surveying (INGEO 2011). Brijuni, Croatia, September. (2011).

J. Pandžić, et al. “Tls in determining geometry of a tall structure.” Engineering geodesy for construction works, industry and research, proceedings of the international symposium on engineering geodesy. (2016).

R. Kocierz, M. Rębisz, and Ł. Ortyl, “Measurement point density and measurement methods in determining the geometric imperfections of shell surfaces.”, Reports on Geodesy and Geoinformatics 105 (2018).

Zahradník, D. (2023, September). Cooling tower measurement by laser scanner and close-range photogrammetry. In AIP Conference Proceedings (Vol. 2928, No. 1). AIP Publishing.

M. Makuch and P. Gawronek, “3D point cloud analysis for damage detection on hyperboloid cooling tower shells”, Remote Sensing 12.10 1542 (2020).

T. Głowacki and Z. Muszyński, “Analysis of cooling tower’s geometry by means of geodetic and thermovision method”, IOP Conference Series: Materials Science and Engineering. Vol. 365. No. 4. IOP Publishing (2018).

N. W. T. Chisholm, “Photogrammetry for cooling tower shape surveys.” The Photogrammetric Record 9.50 173-191 (1977).

Ioannidis, C., Valani, A., Soile, S., Tsiligiris, E., & Georgopoulos, A. (2007). Alternative techniques for the creation of 3D models for finite element analysis—Application on a cooling tower. Proc. Opt.

Castiglioni, C. A., Rabuffetti, A. S., Chiarelli, G. P., Brambilla, G., & Georgi, J. (2017, September). Unmanned aerial vehicle (UAV) application to the structural health assessment of large civil engineering structures. In Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017) (Vol. 10444, pp. 355-370). SPIE.

Fujita, Y., Mitani, Y., & Hamamoto, Y. (2006, August). A method for crack detection on a concrete structure. In 18th International Conference on Pattern Recognition (ICPR'06) (Vol. 3, pp. 901-904). IEEE.

Nnolim, U. A. (2020). Automated crack segmentation via saturation channel thresholding, area classification and fusion of modified level set segmentation with Canny edge detection. Heliyon, 6(12).

Prasanna, P., Dana, K. J., Gucunski, N., Basily, B. B., La, H. M., Lim, R. S., & Parvardeh, H. (2014). Automated crack detection on concrete bridges. IEEE Transactions on automation science and engineering, 13(2), 591-599.

Dung, C. V. (2019). Autonomous concrete crack detection using deep fully convolutional neural network. Automation in Construction, 99, 52-58.

Zhang, L., Shen, J., & Zhu, B. (2021). A research on an improved Unet-based concrete crack detection algorithm. Structural Health Monitoring, 20(4), 1864-1879.

Liu, Z., Cao, Y., Wang, Y., & Wang, W. (2019). Computer vision-based concrete crack detection using U-net fully convolutional networks. Automation in Construction, 104, 129-139.

A. Kwinta and J. Bac-Bronowicz, “Analysis of hyperboloid cooling tower projection on 2D shape”, Geomatics, Landmanagement and Landscape (2021).

Beshr, A. A., Basha, A. M., El-Madany, S. A., & El-Azeem, F. A. (2023). Deformation of High Rise Cooling Tower through Projection of Coordinates Resulted from Terrestrial Laser Scanner Observations onto a Vertical Plane. ISPRS International Journal of Geo-Information, 12(10), 417.

blender.org - Home of the Blender project - Free and Open 3D Creation Software. (n.d.). blender.org. https://www.blender.org/

(Agisoft Metashape: Agisoft Metashape, n.d.)

Texture Maps Explained | Poliigon Help Center. (n.d.). https://help.poliigon.com/en/articles/1712652-texture-maps-explained







How to Cite

Damage detection on cooling tower shell based on model textures. (2024). Stavební Obzor - Civil Engineering Journal, 33(1). https://doi.org/10.14311/CEJ.2024.01.0007