• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.400-400
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• 2022

관로 내 빈번히 발생하는 수격압의 발생은 관망 구조물에 피로가 누적되고 관벽에 손상을 발생시켜, 관로 내 누수가 다양한 형태로 생성된다. 관 내 누수가 발생되는 경우 관 내부의 수격압의 발생 시 생성되는 부압으로 인하여 외부 물질이 관으로 흡수되거나 혼합되어 스케일과 미생물의 생성되는 등 관 내의 수질에 악영향을 끼치며 마찰을 증가시켜 통수능이 감소하고 관리에 추가적인 비용을 발생시킨다. 이러한 영향을 방지하기 위해 관 내에서 생성되는 누수를 탐지하기 위하여 수격압을 발생시켜 압력파를 분석하거나 추적을 수행하는 여러 가지 연구들이 수행되었다. 본 연구에서는 현장 관망과 연결된 100A 대구경 관로에 관로 수압 발생장치를 연결하여 기존의 수격압을 발생시켜 분석하는 방법 대신 안전하고 용이한 방법인 압력파를 주입하여 실험을 수행하였다. 실험을 통해 획득한 데이터를 시간상에서 분석하고 Fourier 변환을 통한 빈도상 분석과 Wavelet 분석으로 신호주기에서 누수가 미치는 영향을 파악하였다. 실험 결과에서는 누수에 의한 영향으로 반사파가 직접적으로 변형되는 형태보다 시스템 전체에서 반영되어 수두가 감쇠되는 형태로 나타났다. Fourier 변환을 통해 무누수 조건과 누수조건의 비교에서 누수의 유무에 따른 신호의 형태가 차이를 보였다. 앞선 연구들에서의 누수의 특정한 위치를 찾아내는 형태 대신 신호처리 후 분석을 통해 시스템 전체에서 일어나는 감쇠를 통해 누수 존재 유무를 판별하고자 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.963-971
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• 2022

Water pipeline network in local and metropolitan area is buried underground, by which it is hard to know the degree of pipe aging and leakage. In this study, assuming various sensor combinations installed in the water pipeline network, the optimal algorithm was derived by predicting the water flow rate and pressure through artificial intelligence algorithms such as linear regression and neuro fuzzy analysis to examine the possibility of detecting pipe leakage according to the data combination. In the case of leakage detection through water supply pressure prediction, Neuro fuzzy algorithm was superior to linear regression analysis. In case of leakage detection through water supply flow prediction, flow rate prediction using neuro fuzzy algorithm should be considered first. If flow meter for prediction don't exists, linear regression algorithm should be considered instead for pressure estimation.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.270-270
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• 2017

스마트워터그리드와 같은 첨단 정보통신기술을 활용한 물 관리 기술의 도입으로 수도운영사업에서도 누수와 같은 이상사건인지 목적의 효율적 빅 데이터 분석기법의 중요성이 증대되고 있다. 국내외적으로 누수인지를 위한 다양한 연구기법, 범위, 계측항목, 샘플링 주기 등이 제시된 바 있으나, 이상감지시스템(Event Detection System, EDS)은 대상지역 특정적 특성을 가지고 있어 범용적인 모델을 구축하는 데는 어려움이 있다. 본 연구에서는 소블럭 단위의 유량자료 분석을 통한 이상감지시스템의 적용가능여부를 판별하고 적합 모델구축자료 방안을 제시하는 K-EDS 모델을 개발하였다. 모델분석의 절차는 자료획득, 자료 전처리, 탐색적 자료해석, 그리고 각 기법 평가로 진행된다. 개발된 모델을 다양한 특성을 가지는 실제 지방상수도시스템에 적용하여 분석하였으며, 최종적으로 모델적용 가능성과 영향인자 등을 도출하였다. 개발된 모델은 소블럭별 현장계측자료 기반의 이상감지모델 적용 적합도 판별에 활용될 수 있으며, 향후 누수 인지 및 누수지속시간 감소를 위한 SW로 개발이 가능하다.

• Journal of the Korean earth science society
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• v.31 no.6
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• pp.563-572
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• 2010

Both multi-channel temperature monitoring and geophysical electric survey were performed together for an embankment to assess the leakage zone. Temperature variation according to space and time on the inner parts of engineering constructions (e.g.: dam and slope) can be basic information for diagnosing their safety problem. In general, as constructions become superannuated, structural deformation (e.g.: cracks and defects) could be generated by various factors. Seepage or leakage of water through the cracks or defects in old dams will directly cause temperature anomaly. This study shows that the position of seepage or leakage in dam body can be detected by multi-channel temperature monitoring using thermal line sensor. For that matter, diverse temperature monitoring experiments for a leakage physical model were performed in the laboratory. In field application of an old earth fill dam, temperature variations for water depth and for inner parts of boreholes located at downstream slope were measured. Temperature monitoring results for a long time at the bottom of downstream slope of the dam showed the possibility that temperature monitoring can provide the synthetic information about flowing path and quantity of seepage of leakage in dam body. Geophysical data by electrical method are also added to help interpret data.

• Economic and Environmental Geology
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• v.46 no.2
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• pp.105-121
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• 2013

This study introduces integrated approach on detection of a leakage in a sea wall based on remote sensing, electric resistivity survey, electromagnetic survey, and borehole survey for the Seosan A-Region sea wall. The satellite temperature distribution from Landsat ETM+ data identifies water leakage distribution and period by analyzing temperature mixing patterns between sea water and fresh water. Electric resistivity survey provides both horizontal and vertical anomaly distributions over the sea wall showing below average electric resistivity. Electromagnetic survey(electrical conductivity survey) reveals the potential possible leakage areas with minimal background impact by comparing electrical conductivity values between high and low tides. Borehole image processing system confirmed the locations of anomalies identified from the other survey methods and distributions of vertical fracture zones. The integrated approach identified 41.7% of the sea wall being the most probable area vulnerable to water leakage and effectively approximated both horizontal and vertical distribution of water leakage. The integrated analysis of remote sensing, electric resistivity survey, electromagnetic survey and borehole survey is considered to be an optimal method in identifying water leakage distribution, period, and extent of fractures knowledged from the boreholes.

• Journal of the Korean Recycled Construction Resources Institute
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• v.4 no.3
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• pp.322-329
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• 2016

This study is about the method to control the quality of material used to repair leakage and crack of concrete structure and suggests the "Temperature Stability Test Method" as a follow-up study. In the result of performance evaluation for 45 samples of 15 types in 5 series, the temperature stability test showed different material changes including rolling down, volume change, and color change as they are frozen and melt repeatedly in the somewhat extreme conditions at low($-20^{\circ}C$) and high($60^{\circ}C$) temperatures, where 13 samples (approx. 29%) and 32 samples (approx. 71%) showed leakage, respectively, in the permeability test to evaluate leakage. This result shows the enough importance of setting the quality control criteria of leakage repair material currently used to maintain concrete structures considering the temperature conditions, and proves the applicability of the Temperature Stability Test Method as a standard test method to ensure long-term durability of concrete structure.

• Journal of Industrial Technology
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• v.28 no.B
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• pp.39-45
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• 2008

Temperature variation according to space and time on the inner parts of engineering constructions(e.g.: dam, slope) can be a basic information for diagnosing their safety problem. In general, as constructions become superannuated, structural deformation(e.g.: cracks, defects) could be occurred by various factors. Seepage or leakage of water through these cracks or defects in old dams will directly cause temperature anomaly. Groundwater level also can be easily observed by abrupt change of temperature on the level. This study shows that the position of seepage or leakage in dam body can be detected by multi-channel temperature monitoring using thermal line sensor. For this, diverse temperature monitoring experiments for a leakage physical model were performed in the laboratory. In field application of an old earth fill dam, temperature variations for water depth and for inner parts of boreholes located at downstream slope were measured. Temperature monitoring results for a long time at the bottom of downstream slope of the dam showed the possibility that temperature monitoring can provide the synthetic information about flowing path and quantity of seepage of leakage in dam body.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.97-97
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• 2023

최근 재해분야에서 인공신경망(ANN), 기계학습(ML), 딥러닝(DL) 등 AI 기술이 활용성이 점차 증가하고 있으며, 센싱정보와 연계한 시설물 안전관리, 원격탐사와 연계한 재해감시(녹조, 산사태, 산불 등), 수문시계열(수위, 유량 등) 예측, 레이더·위성강수 자료의 보정과 예측, 상하수도 관망누수예측 등 다양한 분야에서 AI 기술이 적용되고 그 활용성이 검증된 바 있다. 본 연구에서는 ML, DL, 물리기반신경망(Pysics-informed Neural Networks, PINNs)을 이용한 다양한 재해분석 사례를 소개하고, 그 활용성과 한계에 대해서 논의하고자 한다. 주요사례로는 (1) SAR영상과 기계학습을 이용한 재해피해지역(울진 산불) 감지, (2) 국가 디지털 정보를 이용한 산사태 위험지역 판별(인제 산사태) (3) 기계학습 및 딥러닝 기법을 이용한 위성강수 자료의 보정·예측 및 유출해석, (4) 수리해석을 위한 수치해석분야에서의 PINNs의 적용성(1차원 Saint-Venant 식 해석) 평가 연구결과를 공유한다. 특히, 자료의 입·출력 자료만으로 학습된 인공신경망 모형 대신 지배방정식(물리방정식)을 만족하도록 강제한 PINNs의 경우, 인공신경망 모형보다 우수한 모의능력을 보여주었으며, 향후 복잡한 수리모델링 등 수치해석분야에서 그 활용가능성이 매우 높을 것으로 판단된다.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.41-47
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• 2024

When there were disparities in performance between models trained in the time and frequency domains, even after conducting an ensemble, we observed that the performance of the ensemble was compromised due to imbalances in the individual model performances. Therefore, this paper proposes a leakage detection technique to enhance the accuracy of pipeline leakage detection through a step-wise learning approach that extracts features from both the time and frequency domains and integrates them. This method involves a two-step learning process. In the Stage 1, independent model training is conducted in the time and frequency domains to effectively extract crucial features from the provided data in each domain. In Stage 2, the pre-trained models were utilized by removing their respective classifiers. Subsequently, the features from both domains were fused, and a new classifier was added for retraining. The proposed transfer learning-based feature fusion technique in this paper performs model training by integrating features extracted from the time and frequency domains. This integration exploits the complementary nature of features from both domains, allowing the model to leverage diverse information. As a result, it achieved a high accuracy of 99.88%, demonstrating outstanding performance in pipeline leakage detection.

• Journal of Korean Tunnelling and Underground Space Association
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• v.18 no.5
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• pp.453-467
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• 2016

Overall risks that can occur in a shield TBM tunnelling are studied in this paper. Both the potential risk events that may occur during tunnel construction and their causes are identified, and the causal relationship between causes and events is obtained in a systematic way. Risk impact analysis is performed for the potential risk events and ways to mitigate the risks are summarized. Literature surveys as well as interviews with experts were made for this purpose. The potential risk events are classified into eight categories: cuttability reduction, collapse of a tunnel face, ground surface settlement and upheaval, spurts of slurry on the ground, incapability of mucking and excavation, and water leakage. The causes of these risks are categorized into three areas: geological, design and construction management factors. Bayesian Networks (BN) were established to systematically assess a causal relationship between causes and events. The risk impact analysis was performed to evaluate a risk response level by adopting an Analytic Hierarchy Process (AHP) with the consideration of the downtime and cost of measures. Based on the result of the risk impact analysis, the risk events are divided into four risk response levels and these levels are verified by comparing with the actual occurrences of risk events. Measures to mitigate the potential risk events during the design and/or construction stages are also proposed. Result of this research will be of the help to the designers and contractors of TBM tunnelling projects in identifying the potential risks and for preparing a systematic risk management through the evaluation of the risk response level and the migration methods in the design and construction stage.