• Tunnel and Underground Space
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• v.20 no.6
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• pp.421-433
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• 2010

The digital images to be used for digital vision measurement like digital face mapping and photogrammetric monitoring in construction could be influenced by various conditions such as a kind of light, the intensity of radiation, camera set-up and so on. Because it is very difficult to assess the rock mass from the digital images acquired under different circumstances, some tests and analysis are carried out to modify the images to be suitable and consistent for the digital image optimization. As a result, the recommended conditions for the acquisition of optimized digital images are suggested.

• Smart Structures and Systems
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• v.23 no.6
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• pp.683-693
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• 2019

In August 2012, during the passage of the typhoon Haikui (1211), large amplitude vibrations were observed on long hangers of the Xihoumen suspension Bridge, which destroyed a few viscoelastic dampers originally installed to connect a pair of hanger ropes transversely. The purpose of this study is to identify the cause of vibration and to develop countermeasures against vibration. Field measurements have been conducted in order to correlate the wind and vibration characteristics of hangers. Furthermore, a replica aeroelastic model of prototype hangers consisting of four parallel ropes was used to study the aeroelastic behavior of hanger ropes and to examine the effect of the rigid spacers on vibration mitigation. It is shown that the downstream hanger rope experiences the most violent elliptical vibration for certain wind direction, and the vibration is mainly attributed to wake interference of parallel hanger ropes. Based on wind tunnel tests and field validation, it is confirmed that four rigid spacers placed vertically at equal intervals are sufficient to suppress the wake-induced vibrations. Since the deployment of spacers on hangers, server hanger vibrations and clash of hanger ropes are never observed.

• Geomechanics and Engineering
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• v.30 no.3
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• pp.273-280
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• 2022

A certain amount of random vibration excitation to subway track is caused by subway operation. This excitation is transmitted through track foundation, tunnel, soil medium, and ground building to the ground and ground structure, causing vibration. The vibration affects ground building. In this study, the results of ANSYS numerical simulation was used to establish back-propagation (BP) neural network model. Moreover, a back-propagation neural network model consisting of five input neurons, one hidden layer, 11 hidden-layer neurons, and three output neurons was used to analyze and calculate the vertical Z-vibration level of New Capital's ground buildings of Qingdao Metro phase I Project (Line M3). The Z-vibration level under different working conditions was calculated from monolithic roadbed, steel-spring floating slab roadbed, and rubber-pad floating slab roadbed under the working condition of center point of 0-100 m. The steel-spring floating slab roadbed was used in the New Capital area to monitor the subway operation vibration in this area. Comparing the monitoring and prediction results, it was found that the prediction results have a good linear relationship with lower error. The research results have good reference and guiding significance for predicting vibration caused by subway operation.

• Journal of the Korea institute for structural maintenance and inspection
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• v.10 no.4
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• pp.160-166
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• 2006

Optical Fiber Bragg Grating sensor has a good performance to measure microscopic displacement which can measure strain of lining concrete and cylindrical structure like high intensity containment building and it can present many advantages like a corrosion resistance from the durability point of view. Then it can measure plane geometrical displacement of cylindrical structures with two-way displacement FBG sensor module. According to the test result, measurement of FBG sensor is better performance than other electric sensor system and 2D-level measurement. As a test result, Resolution of the two-way displacement sensor module with FBG sensors are more 10 times than other LVDT or 2D surveying.

• Journal of Korean Tunnelling and Underground Space Association
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• v.16 no.5
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• pp.445-458
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• 2014

The displacement data monitored during tunnel construction play a crucial role in predicting the behaviour of ground around and ahead of excavation face. However, the management criteria for monitoring data are not well established especially for the reliable analysis on varying aspect of displacement data along with chainage. In this study, we evaluated the applicability of x-MR control chart method, which is kind of applied statistical management method, for the analysis of displacement monitoring data in terms of prediction of possible collapse or induced cracks. As a result, a possible abnormal behaviour could be predicted beforehand at 5 ~ 13 m ahead or on at least one day before it occurred by using x-MR control chart method. In addition, it is noted that the moving range for the x-MR control chart should be set to 5~10 for this purpose.

• The KIPS Transactions:PartD
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• v.13D no.6 s.109
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• pp.775-788
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• 2006

Many disaster monitoring systems are constantly studied to prevent disasters such as environmental pollution, the breaking of a tunnel and a building, flooding, storm earthquake according to the progress of wireless telecommunication, the miniaturization of terminal devices, and the spread of sensor network. A disaster monitoring system can extract information of a remote place, process sensor data with rules to recognize disaster situation, and provide work for preventing disaster. However existing monitoring systems are not enough to predict and prevent disaster, because they can only process current sensor data through utilizing simple aggregation function and operators. In this paper, we design and implement a disaster prevention system to predict near future dangerous area through using outside sensor network and spatial Information. The provided prediction technique considers the change of spatial information over time with current sensor data, and indicates the place that could be dangerous in near future. The system can recognize which place would be dangerous and prepare the disaster prevention. Therefore, damage of disaster and cost of recovery would be reduced. The provided disaster prevention system and prediction technique could be applied to various disaster prevention systems and be utilized for preventing disaster and reducing damages.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.1
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• pp.114-127
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• 2010

The purpose of this study is to find out cold and fresh air producing areas using climate-ecological functions in Changwon city, Gyeongsangnam-do, South Korea. The evaluation of climate-ecological functions were composed of the six factors: patch size with cold air generation and inflow functions, farmland and grassland ratio, mean slope degree, cross section types, mean slope length and roughness of bottom in valley. The analysis results of each evaluation factor in the study area were divided into 5 grades according to the capacity of cold air generation. The first-grade area with the highest factor values for cold air generation was take up 3.51% of the total study area, second grade was 13.48%, third grade was 31.65%, fourth grade was 27.28%, and fifth grade was 24.09%. According to the spatial distribution of cold air producing areas, the valleys around Mt. Bongnim, Changwon tunnel, and Anmin tunnel had higher evaluation grade. It will require the future research to establish the climate-ecological conservation areas and to construct the wind corridor based on the long-term microclimatic monitoring.

• Geotechnical Engineering
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• v.14 no.6
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• pp.17-32
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• 1998

The Bakun hydroelectric project includes the construction of a hydroelectric power plant with an installed capacity of 2,520MW and a power transmission system connecting to the existing transmission networks in Sarawak and Western Malaysia, The power station will consist of a 210m height concrete faced rockfill dam. During the construction of the dam and the power facilities the Balui river has to be diverted by three diversion tunnels with a length of some 1,400m each. The inner diameter of the tunnels is 12m and the tunnel width is 16m at the portal area. This paper describes the stability analysis and design methods for the open cut rock slopes in the inlet and outlet area of the diversion tunnels. The geotechnical parameters employed in stability calculations were given as a function of four. defined Rock Mass Types (RMT) which were based on RMR system from Bieniawski. The stability calculations procedure of the rock slopes are divided into two stages. In the first stage, it is calculated for the stability of each 'global' slope without any rock support and shotcrete system. In the second stage, it is calculated for each 'local'slope stability with berms and supported with rock bolts and shotcrete. The monitoring instrumentation was performed continuously and some of the design modification was carried out in order to increase the safety of failed area based on the unforeseen geological risks during the open cut excavation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.8
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• pp.8-16
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• 2016

This study presents the visualization of shape information based on Octree using 3D laser scanning. The process of visualization was established to construct the Octree structure from the 3D scan data. The scan data was converted to a 2D surface through the mesh technique and the surface was then converted to a 3D object through the Raster/Vector transformation. The 3D object was transmitted to the Octree Root Node and The shape information was constructed by the recursive partitioning of the Octree Root Node. The test-bed was selected as the steel bridge structure in Sungkyunkwan University. The shape information based on Octree was condensed into 89.3%. In addition, the Octree compressibility was confirmed to compare the shape information of the office building, a computer science campus in Germany and a New College in USA. The basis is created by the visualization of shape information for double-deck tunnel and it will be expected to improve the efficiency of structural health monitoring and maintenance.

• Wind and Structures
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• v.36 no.4
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• pp.237-247
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• 2023

The emergence of high-rise buildings has necessitated frequent structural health monitoring and maintenance for safety reasons. Wind causes damage and structural changes on tall structures; thus, safe structures should be designed. The pressure developed on tall buildings has been utilized in previous research studies to assess the impacts of wind on structures. The wind tunnel test is a primary research method commonly used to quantify the aerodynamic characteristics of high-rise buildings. Wind pressure is measured by placing pressure sensor taps at different locations on tall buildings, and the collected data are used for analysis. However, sensors may malfunction and produce erroneous data; these data losses make it difficult to analyze aerodynamic properties. Therefore, it is essential to generate missing data relative to the original data obtained from neighboring pressure sensor taps at various intervals. This study proposes a deep learning-based, deep convolutional generative adversarial network (DCGAN) to restore missing data associated with faulty pressure sensors installed on high-rise buildings. The performance of the proposed DCGAN is validated by using a standard imputation model known as the generative adversarial imputation network (GAIN). The average mean-square error (AMSE) and average R-squared (ARSE) are used as performance metrics. The calculated ARSE values by DCGAN on the building model's front, backside, left, and right sides are 0.970, 0.972, 0.984 and 0.978, respectively. The AMSE produced by DCGAN on four sides of the building model is 0.008, 0.010, 0.015 and 0.014. The average standard deviation of the actual measures of the pressure sensors on four sides of the model were 0.1738, 0.1758, 0.2234 and 0.2278. The average standard deviation of the pressure values generated by the proposed DCGAN imputation model was closer to that of the measured actual with values of 0.1736,0.1746,0.2191, and 0.2239 on four sides, respectively. In comparison, the standard deviation of the values predicted by GAIN are 0.1726,0.1735,0.2161, and 0.2209, which is far from actual values. The results demonstrate that DCGAN model fits better for data imputation than the GAIN model with improved accuracy and fewer error rates. Additionally, the DCGAN is utilized to estimate the wind pressure in regions of buildings where no pressure sensor taps are available; the model yielded greater prediction accuracy than GAIN.