• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.5
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• pp.20-29
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• 2024

The chloride diffusion coefficient is a critical indicator for assessing the durability of concrete marine substructures. This study develops a prediction model for the chloride diffusion coefficient using data from concrete bridges located in marine exposure zones (atmospheric, splash, tidal), an aspect that has not been considered in previous studies. Chloride profile data obtained from these bridge substructures were utilized. After data preprocessing, machine learning models, including Random Forest (RF), Gradient Boosting Machine (GBM), and K-Nearest Neighbors (KNN), were optimized through hyperparameter tuning. The performance of these models was developed and compared under three different variable sets. The first model uses six variables: water-to-binder (W/B) ratio, cement type, coarse aggregate volume ratio, service life, strength, and exposure environment. The second model excludes the exposure environment, using only the remaining five variables. The third model relies on just three variables: service life, strength, and exposure environment factors that can be obtained from precision safety diagnostics. The results indicate that including the exposure environment significantly enhances model performance for predicting the chloride diffusion coefficient in concrete bridges in marine environments. Additionally, the three variable model demonstrates that effective predictions can be made using only data from precision safety diagnostics.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.1
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• pp.133-141
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• 2020

Korea Infrastructure Safety and Technology Corporation (KISTEC) specifies that the safety inspection and precise safety diagnosis of concrete structures shall be conducted in accordance with the 'Special Law on Safety Management of Infrastructure'. The detailed safety inspection and precise safety diagnosis guidelines presented by KISTEC, however, gives only the grade of members and structures, and thus it is impossible to quantify remaining service life (RSL) of the structures and to quantitatively reflect the effect of differential settlements on the RSL. Therefore, this study aims to develop a RSL evaluation model considering the differential settlements. To this end, a simple equation was proposed based on the correlations between differential settlements and angular distortion, by which the angular distortion of structures was then reflected in nominal strengths of structural members. In addition, the effects of the differential settlements on the RSL of structures were analyzed in detail by using the safety diagnosis results of actual concrete structure.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.2
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• pp.113-128
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• 2024

Items of road tunnel PISD (Precise Inspection for Safety and Diagnosis) were reviewed and analyzed using newly enhanced MMS (Mobile Mapping System) technology. Possible items with MMS can be visual inspection, survey and non-destructive test, structural analysis, and maintenance plan. The resolution of 3D point cloud decreased when the vehicle speed of MMS is too fast while the calibration error increased when it is too slow. The speed measurement of 50 km/h is determined to be effective in this study. Although image resolution by MMS has a limit to evaluating the width of crack with high precision, it can be used as data to identify the status of facilities in the tunnel and determine whether they meet disaster prevention management code of tunnel. 3D point cloud with MMS can be applicable for matching of cross-section and also possible for the variation of longitudinal survey, which can intuitively check vehicle clearance throughout the road tunnel. Compared with the measurement of current PISD, number of test and location of survey is randomly sampled, the continuous measurement with MMS for environment condition can be effective and meaningful for precise estimation in various analysis.

• Journal of the Korea Institute of Building Construction
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• v.21 no.6
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• pp.665-676
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• 2021

As the number of deteriorated buildings increases, the importance of safety diagnosis and maintenance of buildings has been rising. Existing visual investigations and building safety diagnosis objectivity and reliability are poor due to their reliance on the subjective judgment of the examiner. Therefore, this study presented the limitations of the previously conducted appearance investigation and proposed 3D Point Cloud data to increase the accuracy of existing detailed inspection data. In addition, this study conducted a calculation of an objective building safety grade using a Deep-Neural Network(DNN) structure. The DNN structure is generated using the existing detailed inspection data and precise safety diagnosis data, and the safety grade is calculated after applying the state evaluation data obtained using a 3D Point Cloud model. This proposed process was applied to 10 deteriorated buildings through the case study, and achieved a time reduction of about 50% compared to a conventional manual safety diagnosis based on the same building area. Subsequently, in this study, the accuracy of the safety grade calculation process was verified by comparing the safety grade result value with the existing value, and a DNN with a high accuracy of about 90% was constructed. This is expected to improve economic feasibility in the future by increasing the reliability of calculated safety ratings of old buildings, saving money and time compared to existing technologies.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.493-493
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• 2023

매설배관의 피복손상부 탐측법은 CIPS법, DCVG법 등 여러 방법이 있으며, 그중 우리나라에서는 DCVG법이 가장 많이 사용되고 있다. 피복손상탐측기술(DCVG)은 매설된 관로에 대하여 직류 전류로 인해 배관주변에 발생하는 전위구배를 측정하여 비굴착상태에서 관로의 피복손상부를 찾아내는 기술이다. 본 기술을 광역상수도 정밀안전진단 및 성능평가에 적용하였으며, 탐측된 위치에 대하여 위험도(%IR)를 예측하였다. 또한 손상의심부에 대한 굴착을 통해 피복손상부를 확인하였다. 본 기술의 신뢰성 및 상수도 분야에 큰 활용성을 확인하였다. 관경, 현장여건 등에 따른 조사 및 굴착에 일부 한계점을 보였으나, 향후 축적된 데이터를 바탕으로 매설된 관로의 손상을 사전에 확인하여 관로사고를 미연에 방지할 기술로 판단된다.

• Land and Housing Review
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• v.15 no.3
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• pp.177-188
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• 2024

This study aims to identify vulnerability factors through comprehensive safety diagnosis and to seek improvement measures for the safety and maintenance of facilities. In this study, the results of road tunnel inspections and diagnostics were converted into a database (DB). Using this data, we explored to identify vulnerable elements (NATM, ASSM) based on structural types and to develop efficient improvement measures. In this study, we analyzed 76 detailed safety diagnosis reports covering 45 different types of road tunnel facilities. In the detailed guidelines for comprehensive safety diagnosis, the database (DB) items for identifying vulnerable factors were selected by categorizing the basic information, such as the year of completion and damage items. In addition, AHP analysis was conducted separately through experts in related fields to analyze the correlation between damages. As a result, the primary vulnerability factors for NATM and ASSM were identified as cracks, leaks, insufficient lining thickness, and joint rear. ASSM was identified as relatively more susceptible to network cracks and material separation compared to NATM. In contrast, flaking and rebar exposure were interpreted as more significant vulnerabilities for NATM than for ASSM. In addition, the correlation between elements in NATM was found to be low, whereas in ASSM, the correlation between elements was high, indicating a more organic relationship.

• Korean Journal of Construction Engineering and Management
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• v.24 no.1
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• pp.31-39
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• 2023

To reduce facility management costs and safety concerns due to aging of facilities, it is important to estimate the future facilities' condition based on facility management data and utilize predictive information for management decision making. To this end, this study proposed a methodology to estimate facility elements' condition using XGBoost. To validate the proposed methodology, this study constructed sample data for road bridges and developed a model to estimate condition grades of major elements expected in the next inspection. As a result, the developed model showed satisfactory performance in estimating the condition grades of deck, girder, and abutment/pier (average F1 score 0.869). In addition, a testbed was established that provides data management function and element condition estimation function to demonstrate the practical applicability of the proposed methodology. It was confirmed that the facility management data and predictive information in this study could help managers in making facility management decisions.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.2
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• pp.1-8
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• 2024

The expansion joint is installed to offset the expansion of the superstructure and must ensure sufficient gap during its service life. In detailed guideline of safety inspection and precise safety diagnosis for bridge, damage due to lack or excessive gap is specified, but there are insufficient standards for determining the abnormal behavior of superstructures. In this study, a data-based maintenance was proposed by continuously monitoring the expansion-gap data of the same expansion joint. A total of 2,756 data were collected from 689 expansion joint, taking into account the effects of season. We have developed a method to evaluate changes in the expansion joint-gap that can analyze the thermal movement through four or more data at the same location, and classified the factors that affect the superstructure behavior and analyze the influence of each factor through deep learning and explainable artificial intelligence(AI). Abnormal behavior of the superstructure was classified into narrowing and functional failure through the expansion joint-gap evaluation graph. The influence factor analysis using deep learning and explainable AI is considered to be reliable because the results can be explained by the existing expansion gap calculation formula and bridge design.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.479-479
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• 2021

최근 기상이변으로 인한 자연재해 발생이 증가하고 있고, 그에 따라 도시의 재난 대응력 강화가 국내에서는 물론 국제적으로도 중요한 이슈가 되고 있다. 특히 쇠퇴지역은 재난재해 발생 시 인적·물적 피해가 일반 지역 보다 상대적으로 크며, 복구에도 많은 시간과 예산이 소요되므로 대응책 마련을 위한 도시재생지역의 정밀한 재난재해의 위험성 분석 기술이 필요하다. 이에 본 연구에서는 도시재생사업 대상지(311개)에 대한 재난재해 유형별 위험성 및 회복성을 종합적으로 분석하는 종합진단 기법을 개발하고, 이를 적용한 프로토타입 시스템을 개발하였다. 재난재해의 범위는 「재난 및 안전관리 기본법」을 준용하여 이에 도시재생사업 시행에 영향을 받아 재난재해 발생에 따른 위험정도가 변화할 가능성이 높은 자연재해 (폭우, 폭염, 폭설, 강풍, 지진)5종과 사회재난 (화재, 붕괴, 폭발) 3종 총 8종으로 정의하였다. 종합진단 기법은 기후변화에 관한 정부간 협의체(IPCC) 위험도 평가 방법을 준용하여 위험요소 (위해성·취약성·노출성)와 대비·대응요소 (회복성)로 구분하고, 전문가 자문회의를 거쳐 재난재해에 특히 취약한 쇠퇴지역의 특성을 반영할 수 있는 종합진단지수 산정식을 개발하였다. 또한 쇠퇴지역 재난재해 종합진단 시스템은 도시재생 업무를 수행하는 사용자가 신속히 정보를 분석하고 활용에 용이하도록 Web-GIS 기반으로 설계하였으며, 종합진단 기법에 의해 산정된 분석결과를 100m × 100m 격자 단위의 등급으로 가시화한다. 분석 결과는 지속적인 연구 개발을 통해 최적의 도시재생사업 의사결정 지원 서비스를 위한 기초 분석 자료로 연계하여 활용되며, 분석 DB는 클라우드 서비스 기반의 도시재생 데이터 플랫폼을 통해 공유된다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.107-116
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• 2022

Recently, research has been actively conducted on the technology of inspection facilities through image-based analysis assessment of human-inaccessible facilities. This research was conducted to study the conditions of deep learning-based imaging data on bridges and to develop an evaluation prototype program for bridges. To develop a deep learning-based bridge damage detection prototype, the Semantic Segmentation model, which enables damage detection and quantification among deep learning models, applied Mask-RCNN and constructed learning data 5,140 (including open-data) and labeling suitable for damage types. As a result of performance modeling verification, precision and reproduction rate analysis of concrete cracks, stripping/slapping, rebar exposure and paint stripping showed that the precision was 95.2 %, and the recall was 93.8 %. A 2^nd performance verification was performed on onsite data of crack concrete using damage rate of bridge members.