• International Journal of Concrete Structures and Materials
• /
• 제9권2호
• /
• pp.255-266
• /
• 2015

A strut-and-tie based method intended for determining the load-carrying capacity of reinforced concrete (RC) corbels is presented in this paper. In addition to the normal strut-and-tie force equilibrium requirements, the proposed model is based on secant stiffness formulation, incorporating strain compatibility and constitutive laws of cracked RC. The proposed method evaluates the load-carrying capacity as limited by the failure modes associated with nodal crushing, yielding of the longitudinal principal reinforcement, as well as crushing or splitting of the diagonal strut. Load-carrying capacity predictions obtained from the proposed analysis method are in a better agreement with corbel test results of a comprehensive database, comprising 455 test results, compiled from the available literature, than other existing models for corbels. This method is illustrated to provide more accurate estimates of behaviour and capacity than the shear-friction based approach implemented by the ACI 318-11, the strut-and-tie provisions in different codes (American, Australian, Canadian, Eurocode and New Zealand).

• 대한전기학회논문지:시스템및제어부문D
• /
• 제54권9호
• /
• pp.567-569
• /
• 2005

NARX(Nonlinear AutoRegressive with eXogenous input) neural network was used for prediction of nuclear reactor behavior which was influenced by control rods in short-term period and also by the concentration of xenon and boron in long-term period in load following operations. The developed model was designed to predict reactor power, xenon worth and axial offset with different burnup states when control rods and boron were adjusted in load following operations. Data of the Korea Next Generation PWR were collected by ONED94 code. The test results presented exhibit the capability of the NARX neural network model to capture the long term and short term dynamics of the reactor core and the developed model seems to be utilized as a handy tool for the use of a plant simulation.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
• /
• 제5권10호
• /
• pp.293-302
• /
• 2016

전기는 생산과 소비가 동시에 이루어지므로 필요한 전력 사용량을 예측하고, 이를 충족시킬 수 있는 충분한 공급능력을 확보해야만 안정적인 전력 공급이 가능하다. 특히, 대학 캠퍼스는 전력 사용이 많은 곳으로 시간과 환경에 따라 전력 변화폭이 다양하다. 이러한 이유로, 효율적인 전력 공급 및 관리를 위해서는 전력 사용량을 실시간으로 예측할 수 있는 모델이 요구된다. 국내외 대학 건물에 대해서는 전력 사용 패턴과 사례 분석을 통해 전력 사용에 영향을 주는 요인들을 파악하기 위한 다양한 연구가 진행되었으나, 전력 사용량의 정량적 예측을 위해서는 더 많은 연구가 필요한 상황이다. 본 논문에서는, 기계 학습 기법을 이용하여 대학 캠퍼스의 전력 사용량 예측 모델을 구성하고 평가한다. 이를 위해, 대학 캠퍼스의 주요 건물 클러스터에 대해 전력 사용량을 15분마다 1년 이상 수집한 데이터 셋을 사용한다. 수집된 전력 사용량 데이터는 수열 형태의 시계열 데이터로 기계 학습 모델에 적용 시 주기성 정보를 반영할 수 없으므로, 2차원 공간의 연속적인 데이터로 증강함으로써 주기성을 반영하였다. 이 데이터와 교육기관의 특성을 반영하기 위한 요일과 공휴일로 구성된 8차원 특성 벡터에 대해 주성분 분석(Principal Component Analysis) 알고리즘을 적용한다. 이어, 인공 신경망(Artificial Neural Network)과 지지 벡터 회귀분석(Support Vector Regression)을 이용하여 전력 사용량 예측 모델을 학습시키고, 5겹 교차검증(5-fold Cross Validation)을 통하여 적용된 기법의 성능을 평가하여, 실제 전력 사용량과 예측 결과를 비교한다.

• Computers and Concrete
• /
• 제31권3호
• /
• pp.253-265
• /
• 2023

In this research, the gene expression programming (GEP) technique was employed to provide a new model for predicting the maximum loading capacity of concrete-encased steel (CES) columns. This model was developed based on 96 CES column specimens available in the literature. The six main parameters used in the model were the compressive strength of concrete (f_c), yield stress of structural steel (f_ys), yield stress of steel rebar (f_yr), and cross-sectional areas of concrete, structural steel, and steel rebar (A_c, A_s and A_r respectively). The performance of the prediction model for the ultimate load-carrying capacity was investigated using different statistical indicators such as root mean square error (RMSE), correlation coefficient (R), mean absolute error (MAE), and relative square error (RSE), the corresponding values of which for the proposed model were 620.28, 0.99, 411.8, and 0.01, respectively. Here, the predictions of the model and those of available codes including ACI ITG, AS 3600, CSA-A23, EN 1994, JGJ 138, and NZS 3101 were compared for further model assessment. The obtained results showed that the proposed model had the highest correlation with the experimental data and the lowest error. In addition, to see if the developed model matched engineering realities and corresponded to the previously developed models, a parametric study and sensitivity analysis were carried out. The sensitivity analysis results indicated that the concrete cross-sectional area (A_c) has the greatest effect on the model, while parameter (f_yr) has a negligible effect.

• 상하수도학회지
• /
• 제38권1호
• /
• pp.1-15
• /
• 2024

Occurrence of process environment changes, such as influent load variances and process condition changes, can reduce treatment efficiency, increasing effluent water quality. In order to prevent exceeding effluent standards, it is necessary to manage effluent water quality based on process operation data including influent and process condition before exceeding occur. Accordingly, the development of the effluent water quality prediction system and the application of technology to wastewater treatment processes are getting attention. Therefore, in this study, through the multi-channel measuring instruments in the bio-reactor and smart multi-item water quality sensors (location in bio-reactor influent/effluent) were installed in The Seonam water recycling center #2 treatment plant series 3, it was collected water quality data centering around COD, T-N. Using the collected data, the artificial intelligence-based effluent quality prediction model was developed, and relative errors were compared with effluent TMS measurement data. Through relative error comparison, the applicability of the artificial intelligence-based effluent water quality prediction model in wastewater treatment process was reviewed.

• 한국항공우주학회지
• /
• 제50권2호
• /
• pp.75-82
• /
• 2022

내부 압력과 압축하중을 받는 발사체 추진제 탱크 구조의 좌굴 하중을 비파괴적으로 예측할 수 있는 기법이 필요하다. 기하학적 초기 결함이 존재하는 단순 원통 구조의 전역 좌굴 하중은 좌굴이 발생하지 않는 범위에서의 고유진동수-압축하중의 상관관계를 이용한 Vibration correlation technique (VCT)을 사용하여 비파괴적으로 예측 가능하다. 본 연구에서는 내부 압력과 압축하중을 동시에 받는 추진제 탱크 구조 형태인 얇은 금속재 단순 원통 구조의 진동 및 좌굴 시험을 수행하였고 VCT를 이용하여 전역 좌굴 하중을 예측하였다. 두께가 얇은 구조의 진동 시험을 위해 스피커를 이용한 비접촉식 가진 방법을 이용하였고 응답은 고분자 압전 센서(PVDF)로 측정하였다. VCT로 예측된 전역 좌굴 하중을 좌굴 시험에서 측정된 좌굴 하중과 비교하여 비파괴적 전역 좌굴 하중 예측 기법을 검증하였다.

• Steel and Composite Structures
• /
• 제33권4호
• /
• pp.583-594
• /
• 2019

In the areas highly exposed to earthquakes, concrete-filled steel tube columns (CFSTCs) are known to provide superior structural aspects such as (i) high strength for good seismic performance (ii) high ductility (iii) enhanced energy absorption (iv) confining pressure to concrete, (v) high section modulus, etc. Numerous studies were reported on behavior of CFSTCs under axial compression loadings. This paper presents an analytical model to predict ultimate load capacity of CFSTCs with circular sections under axial load by using multivariate adaptive regression splines (MARS). MARS is a nonlinear and non-parametric regression methodology. After careful study of literature, 150 comprehensive experimental data presented in the previous studies were examined to prepare a data set and the dependent variables such as geometrical and mechanical properties of circular CFST system have been identified. Basically, MARS model establishes a relation between predictors and dependent variables. Separate regression lines can be formed through the concept of divide and conquers strategy. About 70% of the consolidated data has been used for development of model and the rest of the data has been used for validation of the model. Proper care has been taken such that the input data consists of all ranges of variables. From the studies, it is noted that the predicted ultimate axial load capacity of CFSTCs is found to match with the corresponding experimental observations of literature.

• Computers and Concrete
• /
• 제33권6호
• /
• pp.739-754
• /
• 2024

This study presents an innovative AI-driven approach to assess the ultimate axial load in Double-Skinned Profiled Steel sheet Composite Walls (DPSCWs). Utilizing a dataset of 80 entries, seven input parameters were employed, and various AI techniques, including Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Decision Tree with AdaBoost Regression, Random Forest Regression, Gradient Boost Regression Tree, Elastic Net Regression, Ridge Regression, and LASSO Regression, were evaluated. Decision Tree Regression and Random Forest Regression emerged as the most accurate models. The top three performing models were integrated into a hybrid approach, excelling in accurately estimating DPSCWs' ultimate axial load. This adaptable hybrid model outperforms traditional methods, reducing errors in complex scenarios. The validated Artificial Neural Network (ANN) model showcases less than 1% error, enhancing reliability. Correlation analysis highlights robust predictions, emphasizing the importance of steel sheet thickness. The study contributes insights for predicting DPSCW strength in civil engineering, suggesting optimization and database expansion. The research advances precise load capacity estimation, empowering engineers to enhance construction safety and explore further machine learning applications in structural engineering.

• 한국물환경학회지
• /
• 제32권6호
• /
• pp.589-599
• /
• 2016

Implemented since 2004, TPLC (Total Pollution Load Control) is the most powerful water-quality protection program. Recently, uncertainty of prediction using steady state model increased due to changing water environments, and necessity of a dynamic state model, especially the watershed model, gained importance. For application of watershed model on TPLC, it needs to be feasible to adjust the relationship (mass-balance) between discharged loads estimated by technical guidance, and arrived loads based on observed data at the watershed outlet. However, at HSPF, simulation is performed as a semi-distributed model (lumped model) in a sub-basin. Therefore, if the estimated discharged loads from individual pollution source is directly entered as the point source data into the RCHRES module (without delivery ratio), the pollutant load is not reduced properly until it reaches the outlet of the sub-basin. The hypothetic RCHRES generated using the HSPF BMP Reach Toolkit was applied to solve this problem (although this is not the original application of Reach Toolkit). It was observed that the impact of discharged load according to spatial distribution of pollution sources in a sub-basin, could be expressed by multi-segmentation of the hypothetical RCHRES. Thus, the discharged pollutant load could be adjusted easily by modification of the infiltration rate or characteristics of flow control devices.

• Structural Engineering and Mechanics
• /
• 제66권2호
• /
• pp.151-160
• /
• 2018

With the aim to evaluate the fatigue damage accumulation and predict the residual life of engineering components under variable amplitude loadings, this paper proposed a new strain energy-based damage accumulation model by considering both effects of mean stress and load interaction on fatigue life in a low cycle fatigue (LCF) regime. Moreover, an integrated procedure is elaborated for facilitating its application based on S-N curve and loading conditions. Eight experimental datasets of aluminum alloys and steels are utilized for model validation and comparison. Through comparing experimental results with model predictions by the proposed, Miner's rule, damaged stress model (DSM) and damaged energy model (DEM), results show that the proposed one provides more accurate predictions than others, which can be extended for further application under multi-level stress loadings.