PSI - Issue 64

A. Di Benedetto et al. / Procedia Structural Integrity 64 (2024) 2254–2262 Author name / Structural Integrity Procedia 00 (2019) 000 – 000

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Figure 7 shows the point cloud of box No. 9 with overlaid classified maps of the detected distress. Three classes were used for roughness values (Figure 7a), while three qualitative classes were used for intensity values (Figure 7b). Comparing panels (a) and (b) in Figure 7, a relationship between the two families of distress can be observed. Areas with very low intensity values in panel (b) correspond to highly deformed areas, probably indicating deep cracks in the wall that allow the passage of water filtering from filtering motions on the surface. The shapes visible in panel (a) are typical of water accumulations and are hardly associated with other types of material superimposed on the surface with intensity values congruent with those observed.

Fig. 7. Point cloud of box No. 9 with overlaid classified maps of detected distress : (a) Water leakages; (b) Spalling.

5. Conclusion In this work, we aim to demonstrate the potential of LiDAR technology, particularly the MLS technique, as one of the most promising and continuously evolving remote sensing methods, for assessing the "degradation state" of a tunnel intrados. This technique has proven effective in providing data for constructing a 3-D model of the infrastructure surface. In addition to geometric reconstruction, radiometric data can be utilized to analyze the chemical and physical characteristics of the surveyed surface. Specifically, intensity values indicative of water presence can aid in segmentation to detect water leakages. However, prior radiometric correction of the data is critical in these scenarios. As the application of this technique to infrastructure surveying is relatively recent, there are still many critical aspects to be evaluated. Currently available commercial software offers only limited functionality tailored to professional needs, thus failing to fully satisfy the scientific community. To address these limitations, we analyzed issues encountered in road surveying applications and developed algorithms implemented in proprietary software (using the MATLAB environment). The results obtained with our proposed method depend largely on input parameter values provided to the implemented algorithms, such as the search radius of the moving sphere used for roughness calculation or the thresholds for qualitative classification of intensity values.