• Korean Journal of Construction Engineering and Management
• /
• v.23 no.2
• /
• pp.3-15
• /
• 2022

In this study, we develop a simulation model for Supply Chain Management (SCM) of modular construction based steel bridge. To this end, first, Factory Production/Site Construction system data for the steel bridge construction were collected, and supply chain, entities, resources, processes were defined based on the collected data. After that, a steel bridge supply chain simulation model was developed by creating data, flowchart, and animation modules using Arena software. Finally, verification and validation of the model were performed by using animation check, extreme condition check, average value test, Little' s law test, and actual case value test. As a result, the developed simulation model appropriately expressed the processes and characteristics of the steel bridge supply chain without any logical errors, and provided accurate performance evaluation values for the target system. In the future, we expect that the model will faithfully play a role as a performance evaluation platform in developing management techniques for optimally operating the steel bridge supply chain.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.23 no.6
• /
• pp.393-402
• /
• 2021

The first TBM introduced in Korea was the gripper TBM, which was applied to the Gudeok Waterway Tunnel in 1985. In the initial stage of the introduction of the gripper TBM, many applications were mainly focused on waterway tunnels (Tunnel Mechanized Construction Design, 2008). Currently, the construction range of gripper TBM in Korea is widely applied to not only waterway tunnels, but also subways, railway tunnels, and TBM+NATM expansion. Overseas, gripper TBM is generally applied, and even when NATM tunnel is applied, it is applied as an exploration tunnel because of the excellent advance rate of gripper TBM and used as an evacuation tunnel after completion. Due to the fast excavation speed, the application of the gripper TBM in the rock section of weathered rock or higher can minimize the environmental and civil complaints caused by creating a large number of work areas when planning long tunnels or mountain tunnels. In this study, the work process of the general gripper TBM was analyzed by analyzing the construction cycle and the gripper TBM with a diameter of 2.6~5.0 m, which was applied the most in Korea. Downtime was investigated and analyzed.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.86-86
• /
• 2023

최근에 들어 환경보전과 지속가능한 하천관리의 중요성이 대두되고 있으며, 통합물관리에 있어 수리량과 수질을 연계한 통합 모니터링의 필요성이 커지고 있다. 수리량과 수질 분야에 대한 모니터링 기술은 지속적인 연구가 이루어져 왔으나, 각 분야의 개별적 연구로 인해 수리량과 수질을 통합하여 모니터링 하는 기술 개발은 미흡한 수준이다. 또한 수질 측정은 수질오염공정시험기준에 있는 채수 기준에 따라 채수하여 측정하고 있으며, 채수 지점은 하천의 수심별로 달리하여 정해진다. 수리 측정은 현장계측을 통한 2차원적 계측으로 진행하고 있어 수질 측정 시 채수지점과 수리 측정지점은 일치하지 않는다. 동일 지점에서의 수질과 수리량을 동시에 고려하고 있지 못한 모니터링은 본류와 지류의 혼합거동이 많은 국내 하천 특성을 반영하지 못한다. 또한 현재의 수질·수리 모니터링은 ADCP나 다항목수질측정기 같은 고가의 장비를 운영하며, 홍수기와 같은 고위험 계측 조건에서 인력을 통해 측정하고 있기에 고비용의 장비운영비와 인명 피해를 야기시키고 있다. 따라서 무인 원격 기술을 적용한 하천 모니터링 기술과 수질과 수리량의 데이터 연계를 통한 3차원 모니터링 기술의 확보는 하천관리에 있어 매우 필수적이다. 본 연구에서는 수중 무인 원격이동체인 ROV와 무인 원격이동체(USV)를 활용한 3차원 수질·수리 모니터링 기술 개발에 관한 연구를 수행하였다. 국내 하천 특성을 고려한 혼합거동을 분석하기 위해 ROV에 수중 GPS 장비와 수질센서를 부착시켜 수중 내 2차원으로 측정되는 수리량과 동일한 좌표를 가지는 수질자료를 계측하여 하천의 연직 분포와 수평적 분포를 통해 화학적 수리적 거동을 분석하여 하천의 3차원 혼합거동 양상을 판단할 수 있었다. 이와 같은 무인 원격이동체를 통한 3차원 수질·수리 모니터링 기술은 하천의 3차원 분석에서 수질·수리량 보간 자료로 활용 가능하며, 효율적인 모니터링을 통하여 하천 전반 및 통합물관리에 있어 크게 기여할 것이라 사료된다.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.42 no.5
• /
• pp.639-650
• /
• 2022

In areas where large-scale sediment transport occurs, it is important to apply appropriate countermeasure method because the phenomenon tends to accelerate by time duration. Among the various countermeasure methods applied so far, beach nourishment needs to be reviewed as an erosion prevention measure because the erosion pattern is mitigated and environmentally friendly depending on the particle size. In the case of beach nourishment. a detailed review is required to determine the size, range, etc., of an appropriate particle diameter. In this study, we investigated the characteristics of the related topographic change using the change in the particle size of nourishment materials, the application of partial area, and the condition under the coexistence of waves and wind as variables because those factors are hard to be analyzed and interpreted within results and limitation of that the existing numerical models are not able to calculate and result out so that it is required that phenomenon or efforts are reviewed at the same time through physical model experiments, field monitoring and etc. So we attempt to reproduce the tendency of beach erosion and deposition and predict possible phenomena in the future using machine learning techniques for phenomena that it is not able to be interpreted by numerical models. we used the hydraulic experiment results for the training data, and the accuracy of the prediction results according to the change in the training method was simultaneously analyzed. As a result of the study it was found that topographic changes using machine learning tended to be similar to those of previous studies in short-term predictions, but we also found differences in the formation of scour and sandbars.

• Journal of the Korean Geotechnical Society
• /
• v.23 no.7
• /
• pp.17-25
• /
• 2007

Various geotechnical problems due to insufficient bearing capacity or excessive settlement are likely to occur when constructing roads or large complexes on soft ground. Accurate predictions of the magnitude of settlement and the consolidation time provide numerous options of ground improvement methods and, thus, enable to save time and expense of the whole project. Asaoka's method is probably the most frequently used one for settlement prediction and the empirical formulae such as Hyperbolic method and Hoshino's method are also often used. To find an elaborate method of predicting the embankment settlement, two recurrent type neural network models, such as Jordan model and Elman-Jordan model, are adopted. The data sets of settlement measured at several domestic sites are analyzed to obtain the most suitable model structures. It was shown from the comparison between predicted and measured settlements that Jordan model provides better predictions than Elman-Jordan model does and that the predictions using CPT results are more accurate than those using SPT results. It is believed that RNN using cone penetration test results can be a highly efficient tool in predicting settlements if enough field data can be obtained.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.28 no.3
• /
• pp.10-18
• /
• 2024

Vehicle loading is one of the main causes of bridge deterioration. Although WiM (Weigh in Motion) can be used to measure vehicle loading on a bridge, it has disadvantage of high installation and maintenance cost due to its contactness. In this study, a non-contact method is proposed to estimate the vehicle loading history of bridges using deep learning and CCTV images. The proposed method recognizes the vehicle type using an object detection deep learning model and estimates the vehicle loading based on the load-based vehicle type classification table developed using the weights of empty vehicles of major domestic vehicle models. Faster R-CNN, an object detection deep learning model, was trained using vehicle images classified by the classification table. The performance of the model is verified using images of CCTVs on actual bridges. Finally, the vehicle loading history of an actual bridge was obtained for a specific time by continuously estimating the vehicle loadings on the bridge using the proposed method.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
• /
• 2010.09a
• /
• pp.59-61
• /
• 2010

본 논문은 U-City의 지하시설물 관리 등 원격 지중시설물 계측 및 지반조사정보의 원격계측 등 지중 토목계측 서비스를 위해서 필요한 지중무선통신기술에 대한 기술적 특성에 대해 서술하고, 지중무선통신기술 중 가장 효과적인 자기장통신기술(Magnetic Field Area Network, MFAN)에 대한 분석과 이를 이용한 지중계측 서비스모텔에 대해 연구한 결과를 정리하였다. 향후, 시설물 관리, 방재, 에너지의 효율적 분배, 공사현장의 안전관리 등 다양한 용도로 지중무선통신이 활용될 것으로 예상되며, 지중무선통신을 통해 수집되는 데이터는 GIS와 연계를 통해 효과적인 지중모니터링 시스템이 개발될 수 있을 것이다.

• Proceedings of the Korea Contents Association Conference
• /
• 2017.05a
• /
• pp.407-408
• /
• 2017

최근 IoT, AI, Big-data 등 4차 산업혁명기술이 다양한 산업영역에 적용되고 있다. 건설산업에도 4차 산업혁명기술 도입을 위한 다양한 연구들이 진행되고 있으며, 토목 및 건축을 포함한 다양한 건설사업 초기단계에 적용되는 토공사의 자동화를 위한 연구가 국내외에서 활발히 진행되고 있다. 본 연구에서는 토공사 자동화를 위한 기초정보를 제공하기 위해 지반지형을 디지털화하고 이를 건설산업 초기 단계에 활용하기 위한 형태를 제시하고자 한다. 지반지형 디지털화를 위해서는 현장계측, 계측데이터 정합, 건설정보 입력, 분석단위 생성 등의 기능이 요구된다. 토공지형 디지털화 기술은 향후 토공사 자동화를 위한 건설장비군의 효율적 운영을 위해 활용될 수 있다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2008.11a
• /
• pp.645-648
• /
• 2008

최근 건설 분야의 정보화 기술이 발달함에 따라 건설생애(Life Cycle) 과정에서 파생되는 방대한 양의 정보를 수집·가공·축적·제공 하는 시스템이 급증하는 추세이다. 특히, 건설공사의 주요요소인 자재 정보를 제공하는 Web 기반 온라인 시스템만 현재 110여개 이상으로 토목·건축·설비·소방 분야에 이르기까지 매우 다양하다. 그러나 이러한 시스템에서 제공 중인 정보가 표준화 및 정형화 되지 않아 건설현장 실무자들의 정보 획득에 있어 많은 어려움을 겪고 있다. 이에 본 연구에서는 정형화되고 표준화된 건설자재정보를 제공하기 위해 건설자재 분류체계를 정의하고 건설자재 속성정보를 정형화하여 자재별 분류체계검색, 통합검색, 카테고리검색 뿐만 아니라 전자카탈로그로 변환 및 생성이 가능한 건설자재정보 관리 시스템을 설계 및 구현 하였다.

• Journal of the Korean Geotechnical Society
• /
• v.39 no.9
• /
• pp.13-24
• /
• 2023

To investigate the stability of temporary retaining walls during excavation, it is essential to develop reverse analysis technologies capable of precisely evaluating the properties of the ground and a learning model that can assess stability by analyzing real-time data. In this study, we targeted excavation sites where the C.I.P method was applied. We developed a Deep Neural Network (DNN) model capable of evaluating the stability of the retaining wall, and estimated the physical properties of the ground being excavated using a Differential Evolution Algorithm. We performed reverse analysis on a model composed of a two-layer ground for the applicability analysis of the Differential Evolution Algorithm. The results from this analysis allowed us to predict the properties of the ground, such as the elastic modulus, cohesion, and internal friction angle, with an accuracy of 97%. We analyzed 30,000 cases to construct the training data for the DNN model. We proposed stability evaluation grades for each assessment factor, including anchor axial force, uneven subsidence, wall displacement, and structural stability of the wall, and trained the data based on these factors. The application analysis of the trained DNN model showed that the model could predict the stability of the retaining wall with an average accuracy of over 94%, considering factors such as the axial force of the anchor, uneven subsidence, displacement of the wall, and structural stability of the wall.