• 대한기계학회논문집
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• 제17권10호
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• pp.2475-2482
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• 1993

The effects of the hole size(2R) and the specimen width(W) on the fatigue strength and the fatigue life in plain woven glass/epoxy composite plates are experimentally investigated under constant amplitude tensile fatigue loading. It is shown in this study that the notch sensitivity under fatigue loading is lower than that under static loading. It can be explained by the fact that the stress concentration is relaxed by the damage developed at the boundary of circular hole. To predict the fatigue strength at a specific cycle, the modified point stress criterion represented as a function of the geometry of the specimen(2R and W) is applied. It is found that the model used in the prediction of the notched tensile strength predicts the fatigue strength with reasonable accuracy. A model for predicting the fatigue life in the notched specimen, based on the S-$N_f$, curve in the smooth specimen, is suggested.

• 콘크리트학회논문집
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• 제16권6호
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• pp.813-822
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• 2004

축하중을 받는 철근콘크리트 부재의 전단강도를 예측하기 위하여, 본 연구에서는 전단력과 축하중 및 휨모멘트를 받는 철근 콘크리트 부재의 전단거동을 예측할 수 있는 변환각 트러스 모델(TATM)을 제안하였다. TATM에서, 축력의 영향을 고려하기 위하여 축압축력이 증가할수록 고정각은 감소하며 균열 방향의 콘크리트 전단저항은 증가한다. TATM의 예측결과가 축력을 받는 철근콘크리트 부재에 대하여 정확성과 신뢰성을 가지는지 검증하기 위하여, 축력을 받는 총 67개의 전단실험 결과를 수집하였으며, TATM 및 기존의 트러스 모델(MCFT, RA-STM FA-STM)과 비교하였다. 수집한 실험결과와 해석결과를 비교한 결과, TATM에 의한 해석결과는 실험결과를 평균 0.95, 변동계수 $12.0\%$로 기존의 트러스 모델보다 더 정확히 예측하였으며, 철근능력비, 축력, 전단경간비 및 압축철근비의 영향을 받지 않았다.

• Computers and Concrete
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• 제22권2호
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• pp.249-259
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• 2018

This paper presents Artificial Neural Network (ANN) models for evaluating bond strength of deformed, plain and cold formed bars in low strength concrete. The ANN models were implemented using the experimental database developed by conducting experiments in three different universities on total of 138 pullout and 108 splitting specimens under monotonic loading. The key parameters examined in the experiments are low strength concrete, bar development length, concrete cover, rebar type (deformed, cold-formed, plain) and diameter. These deficient parameters are typically found in non-engineered reinforced concrete structures of developing countries. To develop ANN bond model for each bar type, four inputs (the low strength concrete, development length, concrete cover and bar diameter) are used for training the neurons in the network. Multi-Layer-Perceptron was trained according to a back-propagation algorithm. The ANN bond model for deformed bar consists of a single hidden layer and the 9 neurons. For Tor bar and plain bars the ANN models consist of 5 and 6 neurons and a single hidden layer, respectively. The developed ANN models are capable of predicting bond strength for both pull and splitting bond failure modes. The developed ANN models have higher coefficient of determination in training, validation and testing with good prediction and generalization capacity. The comparison of experimental bond strength values with the outcomes of ANN models showed good agreement. Moreover, the ANN model predictions by varying different parameters are also presented for all bar types.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2002년도 봄 학술발표회 논문집
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• pp.997-1002
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• 2002

Traditional prediction models have been developed with a fixed equation form based on the limited number of data and parameters. If new data is quite different from original data, then the model should update not only its coefficients but also its equation form. However, artificial neural network (ANN) does not need a specific equation form. Instead of that, it needs enough input-output data. Also, it can continuously re-train the new data, so that it can conveniently adapt to new data. Therefore, the purpose of this paper is to develop the I-PreConS (Intelligent system for PREdiction of CONcrete Strength using ANN) that provides in-place strength information of the concrete to facilitate concrete form removal and scheduling for construction.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2004년도 춘계 학술발표회 제16권1호
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• pp.532-535
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• 2004

This paper presents a nonlinear finite element analysis procedure for the prediction of shear strength of reinforced concrete deep beams. A computer program, named RCAHESTC(Reinforced Concrete Analysis in Higher Evaluation System Technology), for the analysis of reinforced concrete structures was used. Material nonlinearity is taken into account by comprising tensile. compressive and shear models of cracked concrete and a model of reinforcing steel. The smeared crack approach is incorporated. The proposed numerical method for the prediction of shear strength of reinforced concrete deep beams is verified by comparison with the reliable experimental results.

• Computers and Concrete
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• 제32권5호
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• pp.513-525
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• 2023

Recently, research on predicting the behavior of reinforced concrete (RC) columns using machine learning methods has been actively conducted. However, most studies have focused on predicting the ultimate strength of RC columns using a regression algorithm. Therefore, this study develops a successive machine learning process for predicting multiple nonlinear behaviors of rectangular RC columns. This process consists of three stages: single machine learning, bagging ensemble, and stacking ensemble. In the case of strength prediction, sufficient prediction accuracy is confirmed even in the first stage. In the case of displacement, although sufficient accuracy is not achieved in the first and second stages, the stacking ensemble model in the third stage performs better than the machine learning models in the first and second stages. In addition, the performance of the final prediction models is verified by comparing the backbone curves and hysteresis loops obtained from predicted outputs with actual experimental data.

• 대한기계학회논문집A
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• 제26권11호
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• pp.2399-2408
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• 2002

A prediction procedure has been developed to evaluate the microtructures and material properties of heat affected zone (HAZ) in pressure vessel steel weld, based on temperature analysis, thermodynamics calculation and reaction kinetics model. Temperature distributions in HAE are calculated by finite element method. The microstructures in HAZ are predicted by combining the temperature analysis results with the reaction kinetics model for austenite grain growth and austenite decomposition. Substituting the microstructure prediction results into the previous experimental relations, the mechanical material properties such as hardness, yielding strength and tensile strength are calculated. The prediction procedure is modified and verified by the comparison between the present results and the previous study results for the simulated HAZ in reactor pressure vessel (RPV) circurnferential weld. Finally, the microstructures and mechanical material properties are determined by applying the final procedure to real RPV circumferential weld and the local weak zone in HAZ is evaluated based on the application results.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2003년도 춘계학술대회논문집
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• pp.375-378
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• 2003

In the present study, a new approach to predict the strength of sintered materials has been carried out and a new framework combining neck growth model and ideal pore model has been established based on the results of tensile tests on powder injection molded specimens with the various porosity. Powder injection molding (PIM) uses the shaping advantage of injection molding but is applicable to metals and ceramics. PIM delivers structural materials in a shaping technology previously restricted to polymers. 17-4 PH stainless steel powders with average diameters of 10 $\mu\textrm{m}$ were injection-molded into flat tensile specimens sintered at the various temperatures ranging from 900 to 1350$^{\circ}C$ for 1h. The relationships between strength and porosity were applied to the experimental results and verified.

• Structural Engineering and Mechanics
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• 제38권1호
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• pp.1-25
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• 2011

A new nonlinear model was developed to evaluate the shear resistance of steel fiber-reinforced concrete beams (SFRCB) using linear genetic programming (LGP). The proposed model relates the shear strength to the geometrical and mechanical properties of SFRCB. The best model was selected after developing and controlling several models with different combinations of the influencing parameters. The models were developed using a comprehensive database containing 213 test results of SFRC beams without stirrups obtained through an extensive literature review. The database includes experimental results for normal and high-strength concrete beams. To verify the applicability of the proposed model, it was employed to estimate the shear strength of a part of test results that were not included in the modeling process. The external validation of the model was further verified using several statistical criteria recommended by researchers. The contributions of the parameters affecting the shear strength were evaluated through a sensitivity analysis. The results indicate that the LGP model gives precise estimates of the shear strength of SFRCB. The prediction performance of the model is significantly better than several solutions found in the literature. The LGP-based design equation is remarkably straightforward and useful for pre-design applications.

• Computers and Concrete
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• 제19권6호
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• pp.631-639
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• 2017

This study experimentally investigated the flexural capacity of a concrete beam reinforced with a newly developed GFRP bar that overcomes the lower modulus of elasticity and bond strength compared to a steel bar. The GFRP bar was fabricated by thermosetting a braided pultrusion process to form the outer fiber ribs. The mechanical properties of the modulus of elasticity and bond strength were enhanced compared with those of commercial GFRP bars. In the four-point bending test results, all specimens failed according to the intended failure mode due to flexural design in compliance with ACI 440.1R-15. The effects of the reinforcement ratio and concrete compressive strength were investigated. Equations from the code were used to predict the deflection, and they overestimated the deflection compared with the experimental results. A modified model using two coefficients was developed to provide much better predictive ability, even when the effective moment of inertia was less than the theoretical $I_{cr}$. The deformability of the test beams satisfied the specified value of 4.0 in compliance with CSA S6-10. A modified effective moment of inertia with two correction factors was proposed and it could provide much better predictability in prediction even at the effective moment of inertia less than that of theoretical cracked moment of inertia.