• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.6
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• pp.120-129
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• 2023

To evaluate the vertical loads on railway bridges, conventional load tests are typically conducted. However, these tests often entail significant costs and procedural challenges. Railway conditions involve nearly identical load profiles due to standardized rail systems, which may appear straightforward in terms of load conditions. Nevertheless, this study aims to validate load tests conducted under operational train conditions by comparing the results with those obtained from conventional load tests. Additionally, static and dynamic structural behaviors are extracted from the measurement data for evaluation. To ensure the reliability of load testing, this research demonstrates feasibility through comparisons of existing measurement data with sensor attachment locations, train speeds, responses between different rail lines, tendency analysis, selection of impact coefficients, and analysis of natural frequencies. This study applies to the Dongho Railway Bridge and verifies the applicability of the proposed method. Ten operational trains and 44 sensors were deployed on the bridge to measure deformations and deflections during load test intervals, which were then compared with theoretical values. The analysis results indicate good symmetry and overlap of loads, as well as a favorable comparison between static and dynamic load test results. The maximum measured impact coefficient (0.092) was found to be lower than the theoretical impact coefficient (0.327), and the impact influence from live loads was deemed acceptable. The measured natural frequencies approximated the theoretical values, with an average of 2.393Hz compared to the calculated value of 2.415Hz. Based on these results, this paper demonstrates that for evaluating vertical loads, it is possible to measure deformations and deflections of truss railway bridges through load tests under operational train conditions without traffic control, enabling the calculation of response factors for stress adjustments.

• Korean Journal of Heritage: History & Science
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• v.56 no.4
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• pp.132-158
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• 2023

The five-story and seven-story stone pagodas at Cheongnyangsaji temple site in Gongju are located under the Sambulbong peak of Gyeryongsan mountain, and are known to have been built of the middle in Goryeo dynasty. As the two pagodas in which two types of Baekje stone pagoda coexist in one era, their historical and academic value are recognized. The seven-story pagoda was overturned by robbery in 1944, and as a result, the five-story pagoda was tilted. Although the two pagodas were restored in 1961, structural instability was continuously raised. In this study, measurement data accumulated from May 2021 to March 2022, and seasonal characteristics were reviewed, and the micro behavior of pagodas were analyzed according to temperature and precipitation during the same period. As a result, the micro thermoelastic behavior was repeated according to the daily temperature change in all sensors, and both the slope and the displacement showed microscale behavior. In the inclinometer, moisture containing the surface and inside of the stones repeated expansion and contraction due to temperature change, showing the micro movements. In particular, the upper part of the five-story pagoda moved up to 3.89° to the northwest, and the seven-story pagoda tilted up to 0.078° to the northeast. The maximum displacements were recorded as 0.127 and 0.149 mm in the five-story and the seven-story pagoda, respectively. These values tended to return to the original position at the end of the measurement, but did not recover completely, indicating a state requiring precise monitoring. The result obtained through the study can be used as basic data for the stable conservation of the two stone pagodas. Based on the behavioral characteristics considering various environmental factors should be analyzed, and the preventive conservation through the maintenance of measurement system built this time should be continued.