Microbiologically Influenced Corrosion (MIC) occurring in underground buried pipes of API 5L X65 steel was investigated. MIC is a corrosion phenomenon caused by microorganisms in soil; it affects steel materials in wet atmosphere. The microstructure and mechanical properties resulting from MIC were analyzed by OM, SEM/EDS, and mapping. Corrosion of pipe cross section was composed of ① surface film, ② iron oxide, and ③ surface/internal microbial corrosive by-product similar to surface corrosion pattern. The surface film is an area where concentrations of C/O components are on average 65 %/ 16 %; the main components of Fe Oxide were measured and found to be 48Fe-42O. The MIC area is divided into surface and inner areas, where high concentrations of N of 6 %/5 % are detected, respectively, in addition to the C/O component. The high concentration of C/O components observed on pipe surfaces and cross sections is considered to be MIC due to the various bacteria present. It is assumed that this is related to the heat-shrinkable sheet, which is a corrosion-resistant coating layer that becomes the MIC by-product component. The MIC generated on the pipe surface and cross section is inferred to have a high concentration of N components. High concentrations of N components occur frequently on surface and inner regions; these regions were investigated and Na/Mg/Ca basic substances were found to have accumulated as well. Therefore, it is presumed that the corrosion of buried pipes is due to the MIC of the NRB (nitrate reducing bacteria) reaction in the soil.

PURPOSES : This study primarily aims to develop and evaluate a Smart Station - a novel underground pipeline measure system - to overcome the challenges of conventional surveying methods. METHODS : This study built two prototypes of the Smart Station. By reflecting issues revealed through the field tests of the first prototype, this study produced the second Smart Station prototype. The organization of the hardware units in the second prototype was reconfigured to maximize its usability for operators in the field. Furthermore, by developing the ‘Digital Twin X’, an integrated Smart Station management software suite, the second prototype was capable of 1) producing a digital workbook for field operators, 2) managing underground pipeline information, and 3) displaying 3-dimensional maps in and around an underground pipeline. The applicability of the second prototype was examined through three field tests conducted in one open space location, where no urban valley effects were expected, and two locations in a downtown area, with urban valley effects. Given the actual field installation of underground pipelines, this study collected data via both conventional surveying methods and the Smart Station to evaluate the performance of the Smart Station. Analyzing the field data, this study examined the data collection time and position accuracy of an underground pipeline measured by the Smart Station. RESULTS : The field test results revealed that both the conventional surveying method and the Smart Station produced similar performances in data collection time and measurement accuracy in the open space test location. However, in the case of downtown locations affected by urban valley effects, the Smart Station achieved 100 % measurement accuracy while the conventional surveying method achieved 93 % accuracy. It was also observed during the field test that no data were collected due to the constraints of the work schedule and various field conditions (e.g., weather and/or traffic congestion). The data collection times at the open space locations were 10 s for both the conventional surveying method and the Smart Station. However, the data collection times at the downtown locations appeared to be 10 s and 360 s by the Smart Station and the conventional surveying methods, respectively, thereby proving that the Smart Station outperforms the conventional method in its measurement efficiency. CONCLUSIONS : It is envisioned that the Smart Station produces higher work efficiency for field operators as it enables them to collect high accuracy data in a timely and quick manner and not only build a database for the collected data but also vividly visualize it in the field. In the future, it is necessary to conduct additional field tests under various conditions for the in-depth investigation of a Smart Station. In addition, it is expected that the Smart Station will be enhanced by coupling augmented reality (AR) technologies.