Abstract
The safe operation and maintenance of the appropriate strength of hyperboloid cooling towers require special supervision and a maintenance plan that takes into consideration the condition of the structure. With three series of terrestrial laser scanning data, the paper presents an automatic inspection system for reinforced concrete cooling tower shells that ensures detection and measurement of damage together with the verification of the quality and durability of surface repairs as required by industry standards. The proposed solution provides an automatic sequence of algorithm steps with low computational requirements. The novel method is based on the analysis of values of the local surface curvature determined for each point in the cloud using principal component analysis and transformed using the square root function. Data segmentation into cloud points representing a uniform shell and identified defects was carried out using the region growing algorithm. The extent of extracted defects was defined through vectorisation with a convex hull. The proposed diagnostics strategy of reinforced concrete hyperboloid cooling towers was drafted and validated using an object currently under repair but in continuous service for fifty years. The results of detection and measurement of defects and verification of surface continuity at repaired sites were compared with traditional diagnostics results. It was shown that the sequence of algorithm steps successfully identified all cavities, scaling, and blisters in the shell recorded in the expert report (recognition rate—100%). Cartometric vectorisation of defects determined the scope of necessary shell repairs offering higher performance and detail level than direct contact measurement from suspended platforms. Analysis of local geometric features of repaired surfaces provided a reliable baseline for the evaluation of the repairs aimed at restoring the protective properties of the concrete surround, desirable especially in the warranty period.
Highlights
This paper proposes an original procedure for using 3D point clouds for automated identification of phenomena indicative of degradation of cooling tower shells or confirming the continuity of surface at repair sites as applied for a hyperboloid cooling tower shell under repair
The strategy for diagnosing the reinforced concrete shell of the cooling tower using terrestrial laser scanning (TLS) data was validated for three conditions of the structure: before repairs, after repairs, and after a winter, which was a time of trial for the multi-layered arrangement after repairs
Point clouds from ten stations with 0.002 m RMS orientation accuracy and mean distance between points of 0.0017 m that made up fully metric, quasi-continuous models of the cooling tower shell were used to determine the properties of the structure
Summary
Hyperboloid cooling towers are counted among the largest cast-in-place industrial structures. The reliability of the traditional methods for assessing the condition of the reinforced concrete structure depends on human, physical, environmental, and organisational factors [12] It is a particular effort and burden to record imperfections and defects of several hundred square meters of cooling tower shells without any permanent and clear identification marks [13]. Terrestrial laser scanners can record a complete, 3D condition of the object [22], which does away with the issue of subjective visual inspection [23], prevents omission of important information [24], and gives no room for free interpretation of reports and vague results of inspections [25] Another noteworthy feature of TLS is its remote-sensing nature [26]. TLS data were used to determine three conditions of the structure (before repairs, after repairs, and after a winter, which is destructive for the multilayered arrangement at repaired sites) and develop a sequence of algorithm steps for detection, localisation, and cartometric documentation of visible defects as well as assessment of the quality and durability of the repairs of the hyperboloid shell
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