• Korean Journal of Construction Engineering and Management
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• v.19 no.4
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• pp.43-51
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• 2018

As the inventory of aged apartments is expected to increase explosively, the importance of maintenance to improve the durability of concrete facilities is increasing. Concrete compressive strength is a representative index of durability of concrete facilities, and is an important item in the precision safety diagnosis for facility maintenance. However, existing methods for measuring the concrete compressive strength and determining the maintenance of concrete facilities have limitations such as facility safety problem, high cost problem, and low reliability problem. In this study, we proposed a model that can predict the concrete compressive strength through images by using deep convolution neural network technique. Learning, validation and testing were conducted by applying the concrete compressive strength dataset constructed through the concrete specimen which is produced in the laboratory environment. As a result, it was found that the concrete compressive strength could be learned by using the images, and the validity of the proposed model was confirmed.

• Journal of the Korean GEO-environmental Society
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• v.22 no.10
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• pp.5-12
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• 2021

The deterioration of facilities is an unavoidable phenomenon. For the management of aging facilities, cracks can be detected and tracked, and the condition of the facilities can be indirectly inferred. Therefore, crack detection plays a crucial role in the management of aged facilities. Conventional maintenances are conducted using the crack detection results. For example, maintenance activities to prevent further deterioration can be performed. However, currently, most crack detection relies only on human judgment, so if the area of the facility is large, cost and time are excessively used, and different judgment results may occur depending on the expert's competence, it causes reliability problems. This paper proposes a concrete crack detection framework based on machine learning to overcome these limitations. Fully automated concrete crack detection was possible through the proposed framework, which showed a high accuracy of 96%. It is expected that effective and efficient management will be possible through the proposed framework in this paper.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.4
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• pp.307-312
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• 2017

In this study, we proposed a method to generate a maintenance documents for railway track through Construction Operations Building information exchange(COBie) which is a subset of Industry Foundation Classes(IFC), a data model for Building Information Modeling(BIM). In order to define the items necessary for railway track maintenance document generation, we analyzed the guideline of maintenance and management to track by the Ministry of Land, Infrastructure and Transport(MLTM), and defined the way to refer to the information items in the COBie spreadsheet. The additional properties not supported in IFC, were created for generation of an Information model that reflecting maintenance information items of railway track by applying user-defined property set within the IFC framework. An IFC-based Information model reflecting the user-defined property was implemented through BIM software, and rail track maintenance information items were transferred to COBie spreadsheet according to the defined approach. It is tested that the information can be transferred from the IFC-based as-built model to the COBie spreadsheet, which can be used to generate the necessary documents for railway facility maintenance work.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.6
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• pp.411-417
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• 2020

To utilize building information modeling (BIM) technology at the bridge maintenance stage, it is necessary to integrate large quantities of bridge inspection and model data for object-oriented information management. This research aims to establish the benefits of utilizing the extended industry foundation class (IFC)-BIM and ontology for bridge inspection information management. The IFC entities were extended to represent the bridge objects, and a method of generating the extended IFC-based information model was proposed. The bridge inspection ontology was also developed by extraction and classification of inspection concepts from the AASHTO standard. The classified concepts and their relationships were mapped to the ontology based on the semantic triples approach. Finally, the extended IFC-based BIM model was integrated with the ontology for bridge inspection data management. The effectiveness of the proposed framework for bridge inspection information management by integration of the extended IFC-BIM and ontology was tested and verified by extracting bridge inspection data via the SPARQL query.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.325-327
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• 2003

형상관리가 소프트웨어 개발과 유지보수 단계에서 중요하게 사용되고 있다. 연구와 실제 구축을 거듭하면서 형상관리는 소프트웨어 개발의 성숙한 기술이 되었다. CBSD(Component Based Software Development)는 소프트웨어 개발의 새로운 패러다임으로 자리 잡고 있다. 즉. CBSD가 소프트웨어 재사용과 소프트웨어 컴포넌트 기술에 관한 연구로 시작되어 왔고. 소프트웨어 개발에 새로운 패러다임으로 인식되고 있다. 그러나 CBSD에 관한 형상관리 연구가 뒤따르지 못하였고, 관련 문헌도 상당히 미흡한 실정이다. 본 논문에서 설계한 모델은 CBSD를 더 효율적으로 지원하기 위하여 사용될 것이다. 또한 본 모델은 CBSD 개념을 이용한다. 이 모델은 전통적인 소프트웨어 형상관리(SCM ; Software Configuration Management)와 관련이 있고 이를 컴포넌트 환경을 지원하도록 개선시킨 모델이다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.533-542
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• 2016

The periodic maintenance of bridges is necessary once they have been constructed and its cost depends on various factors, such as their condition, environmental conditions and so on. To make a decision support system, it is essential to establish a basic reconstruction cost model. In this study, a regression model is suggested for calculating the reconstruction cost for typical cases and influential factors, depending on the type of bridge and its components, by analyzing the basic bridge specifications based on the data of the Bridge Management System (BMS). The details for each case were estimated in consideration of the cost calculation variables. The details for each case were estimated in consideration of the cost calculation variables. The cost model for the new construction of the superstructure, substructure and foundation and the temporary bridge construction and demolition costs were drawn from the regression analysis of the estimation results of typical cases according to the cost calculation variables. The reconstruction costs for different types of bridge were obtained using the cost model and compared with those in the literature. The cost model developed herein is expected to be utilized effectively in maintenance decision making.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.717-724
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• 2018

To prepare for the explosive increase in maintenance costs of bridges according to the aging of infrastructure, future maintenance costs of bridges should be predicted. For this purpose, the management status of bridges was investigated and modeled as the upper limit of the performance level and the target management level according to the life cycle. This paper proposes methodologies and procedures for estimating the bridge maintenance costs using two models and existing cost and performance prediction models that consist of unit repair cost model according to the safety score, performance degradation model of bridges, unit reconstruction cost, and average reconstruction time. To verify the applicability, future maintenance costs can be forecasted for specific management agency considering the number of bridges, degree of aging, and current management status. As a result, it is possible to obtain the maintenance cost and safety level of an individual bridge level for each year. In addition, by summing them up to the agency level, the average safety score, ratio of the safety level, inspection costs, repair costs, and reconstruction costs can be obtained. In a further study, the changes in maintenance costs can be analyzed according to the changes in the target management levels using the developed method. The optimal management level can be suggested by reviewing the results.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.802-809
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• 2021

To cope with the increasing maintenance costs due to aging, the maintenance cost was evaluated from the perspective of asset management. The maintenance cost can be predicted based on the condition of the bridge, and the life cycle cost is used as an index. In general, the condition of a bridge has a wide distribution characteristic depending on the deterioration, load, and material characteristics. In this paper, to evaluate the effect of the bridge conditions on the life cycle cost, condition prediction models were constructed considering the service life, deterioration rate, and inspection error, which are the main variables of the bridge condition and life cycle cost calculation. In addition, condition prediction models were constructed based on the distribution of the health index to estimate the upper and lower bounds of the life cycle costs that can occur in individual bridges. Life cycle cost analysis showed that the life cycle cost differed significantly according to the condition of the bridge. Accordingly, research will be needed to increase the reliability of predicting the life cycle cost of individual bridges.

• Korean Journal of Construction Engineering and Management
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• v.21 no.6
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• pp.46-55
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• 2020

The maintenance cost for highway pavement is gradually increasing due to the continuous increase in road extension as well as increase in the number of old routes that have passed the public period. As a result, there is a need for a method of minimizing costs through preventative grievance Preventive maintenance requires the establishment of a strategic plan through accurate prediction old Highway pavement. herefore, in this study, the XGBoost among machine learning classification-based models was used to develop a highway pavement damage prediction model. First, we solved the imbalanced data issue through data sampling, then developed a predictive model using the XGBoost. This predictive model was evaluated through performance indicators such as accuracy and F1 score. As a result, the over-sampling method showed the best performance result. On the other hand, the main variables affecting road damage were calculated in the order of the number of years of service, ESAL, and the number of days below the minimum temperature -2 degrees Celsius. If the performance of the prediction model is improved through more data accumulation and detailed data pre-processing in the future, it is expected that more accurate prediction of maintenance-required sections will be possible. In addition, it is expected to be used as important basic information for estimating the highway pavement maintenance budget in the future.

• Journal of Advanced Navigation Technology
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• v.20 no.2
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• pp.141-147
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• 2016

Stable and reliable air traffic service is required for users (aircraft pilot, air traffic controller, airlines and public) through enhancing capability related to airport operation and continuously improving air navigation system. ASMS (air navigation service provider (ANSP) safety maturity survey) is to determine the level of management and safety requirement such as organization, risk, policy, process, training and environment in Air traffic service field. We designed and developed the survey (26 study areas of management part and 23 study areas of safety part) considering global best practices (Eurocontrol and FAA) and customizing domestic circumstances with quantitative level assessment regarding management and safety issue of Air navigation system. The survey enables the performance of Air navigation system to enhance and prevents from occurring accident or incident. Therefore, we provides best information with users as well as high quality Air traffic service.