• Earthquakes and Structures
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• 제22권2호
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• pp.121-135
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• 2022

Many public buildings such as schools, hospitals, etc., where partial infill walls are present in reinforced concrete (RC) structures, have undergone undesirable damage/failure attributed to captive column effect during a moderate to severe earthquake shaking. Often, the situation gets worsened when these RC frames are non-ductile in nature, thus reducing the deformable capability of the frame. Also, in many parts of the Indian subcontinent, it is mandatory to use fly-ash bricks for construction so as to reduce the burden on the disposal of fly-ash produced at thermal power plants. In some scenario, when the non-ductile RC frame, partially infilled by fly-ash bricks, suffers major structural damage, the challenge remains on how to retrofit and restore it. Thus, in this study, two full-scale one-bay, one-story non-ductile RC frame models, namely, bare frame and RC partially infilled frame with fly-ash bricks in 50% of its opening area are considered. In the previous experiments, these models were subjected to slow-cyclic displacement-controlled loading to replicate damage due to a moderate earthquake. Now, in this study these damaged frames were retrofitted and an experimental investigation was performed on the retrofitted specimens to examine the effectiveness of the proposed retrofitting scheme. A hybrid retrofitting technique combining epoxy injection grouting with an innovative and easy-to-implement steel jacketing technique was proposed. This proposed retrofitting method has ensured proper confinement of damaged concrete. The retrofitted models were subjected to the same slow cyclic displacement-controlled loading which was used to damage the frames. The experimental study concluded that the hybrid retrofitting technique was quite effective in enhancing and regaining various seismic performance parameters such as, lateral strength and lateral stiffness of partially fly-ash brick infilled RC frame. Thus, the steel jacketing retrofitting scheme along with the epoxy injection grouting can be relied on for possible repair of the structural members which are damaged due to the captive column effect during the seismic shaking.

• Earthquakes and Structures
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• 제22권2호
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• pp.185-201
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• 2022

Structural Health Monitoring (SHM) is used to provide reliable information about the structure's integrity in near realtime following extreme incidents such as earthquakes, considering the inevitable aging and degradation that occurs in operating environments. This paper experimentally investigates an integrated wireless sensor network (Wi-SN) based monitoring technique for damage detection in concrete structures. An effective SHM technique can be used to detect potential structural damage based on post-earthquake data. Two novel methods are proposed for damage detection in reinforced concrete (RC) building structures including: (i) Jerk Energy Method (JEM), which is based on time-domain analysis, and (ii) Modal Contributing Parameter (MCP), which is based on frequency-domain analysis. Wireless accelerometer sensors are installed at each story level to monitor the dynamic responses from the building structure. Prior knowledge of the initial state (immediately after construction) of the structure is not required in these methods. Proposed methods only use responses recorded during ambient vibration state (i.e., operational state) to estimate the damage index. Herein, the experimental studies serve as an illustration of the procedures. In particular, (i) a 3-story shear-type steel frame model is analyzed for several damage scenarios and (ii) 2-story RC scaled down (at 1/6th) building models, simulated and verified under experimental tests on a shaking table. As a result, in addition to the usual benefits like system adaptability, and cost-effectiveness, the proposed sensing system does not require a cluster of sensors. The spatial information in the real-time recorded data is used in global damage identification stage of SHM. Whereas in next stage of SHM, the damage is detected at the story level. Experimental results also show the efficiency and superior performance of the proposed measuring techniques.

• Advances in nano research
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• 제12권5호
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• pp.515-527
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• 2022

The application of fibers in concrete obviously enhances the properties of concrete, also the application of natural fibers in concrete is raising due to the availability, low cost and environmentally friendly. Besides, predicting the mechanical properties of concrete in general and shear strength in particular is highly significant in concrete mixture with fiber nanocomposite reinforced concrete (FRC) in construction projects. Despite numerous studies in shear strength, determining this strength still needs more investigations. In this research, Adaptive Neuro-Fuzzy Inference System (ANFIS) have been employed to determine the strength of reinforced concrete with fiber. 180 empirical data were gathered from reliable literature to develop the methods. Models were developed, validated and their statistical results were compared through the root mean squared error (RMSE), determination coefficient (R²), mean absolute error (MAE) and Pearson correlation coefficient (r). Comparing the RMSE of PSO (0.8859) and ANFIS (0.6047) have emphasized the significant role of structural parameters on the shear strength of concrete, also effective depth, web width, and a clear depth rate are essential parameters in modeling the shear capacity of FRC. Considering the accuracy of our models in determining the shear strength of FRC, the outcomes have shown that the R² values of PSO (0.7487) was better than ANFIS (2.4048). Thus, in this research, PSO has demonstrated better performance than ANFIS in predicting the shear strength of FRC in case of accuracy and the least error ratio. Thus, PSO could be applied as a proper tool to maximum accuracy predict the shear strength of FRC.

• Computers and Concrete
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• 제30권4호
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• pp.277-287
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• 2022

Due to the scarcity of extortionate experimental data, fatigue failure of the reinforced concrete (RC) element might be achieved economically adopting nonlinear finite element (FE) analysis as an alternative approach. However, conventional implicit dynamic analysis is expensive, quasi-static method overlooks interaction effects and inertia, direct cyclic analysis computes stabilized responses. Apart from this, explicit dynamic analysis may provide a numerical operating system for factual long-term responses. The study explores the fatigue behavior based on a simplified explicit dynamic solution employing nonlinear time domain analysis. Among fourteen RC beams, one beam is selected to validate under static loading, one under fatigue with the experimental study and other twelve to check the detail fatigue behavior. The SWOT (Strength, Weakness, Opportunities, Threats) analysis has been carried out to pinpoint the detail scenario in the adoption of numerical approach as an alternative to the experimental study. Excellent agreement of FE and experimental results is seen. The 3D nonlinear RC beam model at service fatigue limits is truthful to be used as an expedient contrivance to envisage the precise fatigue behavior. The simplified analysis approach for RC beam under fatigue offers savings in computation to predict responses providing acceptable accuracy rather than the complicated laboratory investigation. At higher frequency, the flexural failure occurs a bit earlier gradually compared to the repeated loading case of lower frequency. The deflection increases by 6%-10% at the end of first cycle for beams with increasing frequency of cyclic loading. However, at the end of fatigue loading, greater deflection occur earlier for higher load range because of more rapid stiffness degradation. For higher frequency, a slight boost in concrete compressive strains at an initial stage of loading has been seen indicating somewhat stepper increment. Stiffness degradation in larger loading cycle at same duration escalates the upsurge of the rate of strain in case of higher frequency.

• 한국구조물진단유지관리공학회 논문집
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• 제25권5호
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• pp.157-164
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• 2021

최근 미세먼지와 관련된 환경문제를 개선하기 위해 유해물질을 제거할 수 있는 광촉매와 흡착제에 대한 연구가 활발히 진행되고 있다. 본 연구에서는 전체 공극량이 일반 건설재료에 비해 상당히 큰 폼 콘크리트에 다량의 마이크로 공극를 갖는 야자계 활성탄소를 이용해서 다공성 폼 복합체를 제작하였다. 미세먼지 흡착 가능성을 평가하기 위해 제작된 폼 복합체에 대해 공극 구조를 분석하였다. 폼 복합체의 공극구조 분석은 측정된 질소 흡착등온선으로부터 BET와 Harkins-jura이론을 적용하였다. 분석결과 활성탄소를 혼입한 폼 복합체의 비표면적과 마이크로 공극 부피가 Plain보다 크게 증가하였다. 활성탄소 혼입율이 증가할수록 폼 복합체의 비표면적과 마이크로 공극 부피가 증가하는 경향을 나타냈다. 이는 폼 복합체가 가스상의 미세먼지 전구물질 NO_X에 대한 흡착성능이 높을 것으로 보인다.

• 터널과지하공간
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• 제32권6호
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• pp.345-352
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• 2022

도심지 터널 건설에서 발파공법은 민원이 제기되는 문제점이 있어 적용에 제약받고 있다. 이에 대한 대안으로 TBM 및 기계굴착 공법 적용이 필수적으로 검토되고 있다. 이 중 쉴드 TBM(Tunnel Boring Machine)은 굴진과 세그먼트 체결이 번갈아 반복되며 굴진하는 공정을 가지고 있는데, 세그먼트 체결 동안 굴진을 멈추게 된다. 이러한 가동 정지시간을 최소화하고자 세그먼트 체결 중에도 가동할 수 있는 연속굴착형 TBM 기술이 개발되고 있다. 나선형 세그먼트의 굴진 반력을 확보하기 위해 추진잭을 개조하고 신뢰성을 확보하는 연구가 진행 중이다. 또한 체결 중 세그먼트를 제외한 나머지 부분의 추진잭을 가동하는 유압제어 및 유압시스템 설계기술이 개발될 예정이다. 본 보고는 연속굴착형 TBM 과제 중 부품개조 및 유압제어 기술에 대한 일부 내용을 소개한다.

• 한국컴퓨터정보학회논문지
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• 제27권8호
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• pp.61-68
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• 2022

본 논문에서는 딥러닝 모델 기반 보행자의 GPS 경로를 예측하는 시스템을 제안한다. 보행자 경로 예측은 보행자의 위험 및 충돌 상황들을 알림을 통해 방지할 수 있으며, 다양한 마케팅 등 비즈니스 면에서도 영향을 끼치는 연구이다. 또한 보행자 뿐 아니라 많은 각광을 받고 있는 무인 이동수단의 경로 예측에도 활용될 수 있다. 다양한 경로 예측 방식들 중 본 논문은 GPS 데이터를 활용하여 경로를 예측하는 연구이다. 시계열 데이터인 보행자의 GPS 경로를 학습하여 다음 경로를 예측하도록 하는 딥러닝 모델 기반 연구이다. 본 논문에서는 보행자의 GPS 경로를 딥러닝 모델이 학습할 수 있도록하는 데이터 셋 구성 방식을 제시하였으며, 예측 범위에 큰 제약이 없는 경로 예측 딥러닝 모델을 제안한다. 본 연구의 경로 예측 딥러닝 모델에 적합한 파라메터들을 제시하였으며, 우수한 예측 성능을 보이는 결과를 제시한다.

• 시스템엔지니어링학술지
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• 제18권2호
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.

• 한국전자통신학회논문지
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• 제17권3호
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• pp.473-482
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• 2022

코로나 시대에서 감염의 위험을 줄이기 위하여 반드시 마스크를 착용하여야 하며, 건축 공사장과 같은 위험한 작업 환경에서 일하는 직원의 안전을 위하여 헬맷을 쓰는 것은 필수불가결하다. 본 논문에서는 헬멧과 마스크의 착용 여부를 분류하는 효과적인 딥러닝 모델 HelmetMask-Net를 제안한다. HelmetMask-Net은 CNN 기반으로 설계되며, 전처리, 컨벌류션 계층, 맥스풀링 계층과 4 가지 출력이 있는 완전결합 계층으로 구성되며, 헬멧, 마스크, 헬멧과 마스크, 헬멧과 마스크을 착용하지 않은 4 가지 경우를 구분한다. 정확도, 최적화, 초월 변수의 수를 고려한 실험으로 2 컨볼루션 계층과 AdaGrad 최적화를 가진 구조가 선정되었다. 모의 실험 결과 99%의 정확도를 보여 주었고, 기존의 모델에 비하여 성능이 우수함을 확인하였다. 제안된 분류기는 코비드 19 시대에 직원의 안전을 향상시킬 수 있을 것이다.

• 플랜트 저널
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• 제18권2호
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• pp.32-40
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• 2022

본 연구에서는 부분부하 운전 시 가스터빈의 공기량 조절에 따른 열병합 발전의 운전데이터 변화를 알아보았다. 가스터빈 부분부하 80%시 시뮬레이션 한 결과 입구가이드베인을 최대 24% 추가로 닫을 수 있었고, 압축기 공기량은 66.11 kg/s 감소, 배기가스 온도는 52℃ 상승시킬 수 있었다. 부분부하 90%는 입구가이드베인을 최대 12% 추가로 닫을 수 있었고, 압축기 공기량은 33.33 kg/s 감소, 배기가스 온도는 23℃ 상승 시킬 수 있었다. 열부하 추종운전 시 부분 부하 80%에서 출력을 최대 5.68 MW 상승, 복합발전 효율을 0.73% 상승, 열병합발전 효율을 1.81% 상승 시킬 수 있었고, 부분부하 90%에서 출력을 최대 2.55 MW 상승, 복합발전 효율을 0.32% 상승, 열병합발전 효율을 0.72% 상승 시킬 수 있었다.