A completely automatic and accurate detection algorithm for the delamination on tunnel concrete lining surfaces from laser 3D point cloud data is proposed. A Mobile Mapping System (MMS), which mounts laser sensors and a positioning system, is utilized to measure the geometry of tunnel lining surfaces highspeed. The proposed algorithm consists of 4 steps: removal of tunnel profile components, detection of peaks of anomalies, localization of anomaly areas, classification of delamination and appendages. On tunnel linings, there are many appendages such as cables, lights, signs, and water guides to mask the features of delamination. In the article, a novel 3D feature was introduced to realize the accurate classification. An automatic SVM algorithm was developed using real tunnel lining data and manual inspection results, showing an accurate delamination map.

The increasing length of the subsurface pipe causes overlapping, accumulating, and sometimes the old pipe layout is not available. Consequently, accidents, damages, time delay and financial losses are occurred during construction of new structures or expanding the roadway or installing of new pipes. Therefore, the depth, diameter, material of the existing pipe as well as the map of the pipe are indispensable to know for appropriate construction planning and development work. Using this algorithm and genetic algorithm optimization, the radius of the field pipe was assessed with 83, 67 and 89% accuracy, the depth was estimated with 95, 95 and 98% accuracy considering the effect of the pipe radius. The effect of the pipe radius should be considered to calculate the pipe depth with higher accuracy. The material of the field pipe was successfully determined using the evaluated relative permittivity of the pipe. A 3D map of the field pipe was developed by applying the tracing algorithm and linear regression to the estimated depth.

Road cracks are an important concern of administrators. Visual inspection is labor-intensive and subjective, while previous algorithms detecting cracks from optical camera images were not accurate. Furthermore, the actual length and thicknesses of a crack cannot be estimated only from images. Light Detection and Ranging (Lidar) is a standard feature introduced on the latest smartphones. In this research, for completely automatic, accurate and quantitative road crack evaluation using smartphones, an up-to-date segmentation technique, U-Net with morphology transform adopting data augmentation was proposed. Lidar 3D point cloud data of smartphones is linked to color data obtained from cameras. By registering images to Lidar data, geometrical relationships were estimated to calculate the length and thicknesses. The proposed algorithm was validated by a standard database of road cracks and dataset constructed by the authors, showing 95% length accuracy and 0.98 coefficient of determination for thickness estimation irrespective of various crack shapes and asphalt pavement color patterns.