• Computers and Concrete
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• 제28권5호
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• pp.521-532
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• 2021

Present paper focuses on the modeling of size effect on the compressive strength of normal concrete with the application of Discrete Element Method (DEM). Test specimens with different size and shape were cast and uniaxial compressive strength test was performed on each sample. Five different concrete mixes were used, all belonging to a different normal strength concrete class (C20/25, C30/37, C35/45, C45/55, and C50/60). The numerical simulations were carried out by using the PFC 5 software, which applies rigid spheres and contacts between them to model the material. DEM modeling of size effect could be advantageous because the development of micro-cracks in the material can be observed and the failure mode can be visualized. The series of experiments were repeated with the model after calibration. The relationship of the parallel bond strength of the contacts and the laboratory compressive strength test was analyzed by aiming to determine a relation between the compressive strength and the bond strength of different sized models. An equation was derived based on Bazant's size effect law to estimate the parallel bond strength of differently sized specimens. The parameters of the equation were optimized based on measurement data using nonlinear least-squares method with SSE (sum of squared errors) objective function. The laboratory test results showed a good agreement with the literature data (compressive strength is decreasing with the increase of the size of the specimen regardless of the shape). The derived estimation models showed strong correlation with the measurement data. The results indicated that the size effect is stronger on concretes with lower strength class due to the higher level of inhomogeneity of the material. It was observed that size effect is more significant on cube specimens than on cylinder samples, which can be caused by the side ratios of the specimens and the size of the purely compressed zone. A limit value for the minimum size of DE model for cubes and cylinder was determined, above which the size effect on compressive strength can be neglected within the investigated size range. The relationship of model size (particle number) and computational time was analyzed and a method to decrease the computational time (number of iterations) of material genesis is proposed.

• 한국산업융합학회 논문집
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• 제19권3호
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• pp.133-143
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• 2016

The objective in here is to find the density, stiffness, and strength of truss-wall rectangular (TWR) model which is combined with lattice truss (MLT) inside space. The TWR unit-cell model is defined as a unit cell originated from a solid-wall rectangular (SWR) model and it has an empty space inside. Thus, the empty space inside of the TWR is filled with lattice truss model defined as TWR-MLT. The ideal solutions derived of TWR-MLT are based on TWR with MLT model and it has developed by Gibson-Ashby's theory. To validate the ideal solutions of the TWR-MLT, ABAQUS software is applied to predict the density, strength, and stiffness, and then each of them are compared with the Gibson-Ashby's ideal solution as a log-log scale. Applied material property is stainless steel 304 because of cost effectiveness and easy to get around. For the analysis, SWR and TWR-MLT models are 1mm, 2mm, and 3mm truss diameter separately within a fixed 20mm opening width. In conclusion, the relative Young's modulus and relative yield strength of the TWR-MLT unit model is reasonably matched to the ideal expectations of the Gibson-Ashby's theory. In nearby future, TWR-MLT model can be verified by advanced technologies such as 3D printing skills.t.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2006년도 정기 학술대회 논문집
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• pp.1009-1014
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• 2006

Synthetic materials with excellent thermodynamic characteristics and the merit of anti-corrosion are frequently used in buildings and constructions for enforcement of bent in stead of steel plates. Among them, many practical studies have been conducted on bond strength because of increased bond strength of FRP plates. Previous investigators identified the bond strength of FRP plates through experiments with settlement of various variables to identify the bond strength. However, the experiments to identify the bond force are difficult to be conducted because they requires large expenses and long time for equipment arrangement, thus, are conducted with limitation. In this study, for bond experiment, optimum neural network model was developed with use of Back-propagation and Conjugate gradient technique of previous investigators. Learning was performed with use of the variables of previous investigators in developed neural network model so as to identify the bond strength of FRP plates. for verification of developed model, credibility and excellence was proven by comparing with the models of previous investigators.

• Structural Engineering and Mechanics
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• 제86권2호
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• pp.181-195
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• 2023

To predict the rheological behaviours along with the compressive strength of self-compacting concrete that incorporates environmentally friendly ingredients as cement substitutes, a comparative evaluation of machine learning methods is conducted. To model four parameters, slump flow diameter, L-box ratio, V-funnel time, as well as compressive strength at 28 days-a complete mix design dataset from available pieces of literature is gathered and used to construct the suggested machine learning standards, SVM, MARS, and Mp5-MT. Six input variables-the amount of binder, the percentage of SCMs, the proportion of water to the binder, the amount of fine and coarse aggregates, and the amount of superplasticizer are grouped in a particular pattern. For optimizing the hyper-parameters of the MARS model with the lowest possible prediction error, a gravitational search algorithm (GSA) is required. In terms of the correlation coefficient for modelling slump flow diameter, L-box ratio, V-funnel duration, and compressive strength, the prediction results showed that MARS combined with GSA could improve the accuracy of the solo MARS model with 1.35%, 11.1%, 2.3%, as well as 1.07%. By contrast, Mp5-MT often demonstrates greater identification capability and more accurate prediction in comparison to MARS-GSA, and it may be regarded as an efficient approach to forecasting the rheological behaviors and compressive strength of SCC in infrastructure practice.

• Geomechanics and Engineering
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• 제16권1호
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• pp.49-58
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• 2018

Open-mode cracks could be commonly observed in layered rocks. A concept model is firstly used to explore the mechanism of the vertical cracks (VCs) in the top layer. Then the crack behaviour of the two-layer model is simulated based on a cohesive zone model (CZM) for layer interfaces and a plastic-damage model for rocks. The model indicates that the tensile stress normal to the VCs changes to compression if the crack spacing to layer thickness ratio is lower than a threshold. The results indicate that there is a threshold for interfacial shear strength that controls the crack patterns of the layered system. If the shear strength is lower than the threshold, the top layer is meshed by the VCs and interfacial cracks (ICs). When the shear strength is higher than the threshold, the top layer is meshed by the VCs and parallel cracks (PCs). If the shear strength is comparative to the threshold, a combining pattern of VCs, PCs and ICs for the top layer can be formed. The evolutions of stress distribution in the crack-bound block indicate that the ICs and PCs can reduce the load transferred for the substrate layer, and thus leads to a crack saturation state.

• Computers and Concrete
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• 제21권1호
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• pp.47-54
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• 2018

Artificial neural network models can be successfully used to simulate the complex behavior of many problems in civil engineering. As compared to conventional computational methods, this popular modeling technique is powerful when the relationship between system parameters is intrinsically nonlinear, or cannot be explicitly identified, as in the case of concrete behavior. In this investigation, an artificial neural network model was developed to assess the residual compressive strength of self-compacted concrete at elevated temperatures ($20-900^{\circ}C$) and various relative humidity conditions (28-99%). A total of 332 experimental datasets, collected from available literature, were used for model calibration and verification. Data used in model development incorporated concrete ingredients, filler and fiber types, and environmental conditions. Based on the feed-forward back propagation algorithm, systematic analyses were performed to improve the accuracy of prediction and determine the most appropriate network topology. Training, testing, and validation results indicated that residual compressive strength of self-compacted concrete, exposed to high temperatures and relative humidity levels, could be estimated precisely with the suggested model. As illustrated by statistical indices, the reliability between experimental and predicted results was excellent. With new ingredients and different environmental conditions, the proposed model is an efficient approach to estimate the residual compressive strength of self-compacted concrete as a substitute for sophisticated laboratory procedures.

• Computers and Concrete
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• 제27권5호
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• pp.489-512
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• 2021

In this study, two powerful techniques, namely particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) were selected and combined with a pre-developed ANN model aiming at improving its performance prediction of the compressive strength of concrete modified with fly ash. To achieve this study's aims, a comprehensive database with 379 data samples was collected from the available literature. The output of the database is the compressive strength (CS) of concrete samples, which are influenced by 9 parameters as model inputs, namely those related to mix composition. The modeling steps related to ICA-ANN (or neuro-imperialism) and PSO-ANN (or neuro-swarm) were conducted through the use of several parametric studies to design the most influential parameters on these hybrid models. A comparison of the CS values predicted by hybrid intelligence techniques with the experimental CS values confirmed that the neuro-swarm model could provide a higher degree of accuracy than another proposed hybrid model (i.e., neuro-imperialism). The train and test correlation coefficient values of (0.9042 and 0.9137) and (0.8383 and 0.8777) for neuro-swarm and neuro-imperialism models, respectively revealed that although both techniques are capable enough in prediction tasks, the developed neuro-swarm model can be considered as a better alternative technique in mapping the concrete strength behavior.

• 대한토목학회논문집
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• 제32권6D호
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• pp.599-605
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• 2012

교통개방시점의 예측은 공사 계획을 위해 중요하며 이를 위해 에폭시 아스팔트 혼합물의 양생에 따른 강도를 예측하는 것이 필요하다. 본 연구에서는 에폭시 아스팔트 혼합물의 양생온도와 시간에 따른 마샬안정도를 측정하고 이를 이용해 강도 발현식을 구하였으며, 변화하는 온도와 강도에 따른 반응속도를 반영할 수 있도록 화학적 반응속도론을 이용하여 에폭시 아스팔트 강도 예측모델을 개발하였다. 예측모델을 사용하여 에폭시 아스팔트 포장이 적용된 국내 교량에 대해 교통개방시기를 예측하였다. 2009년~2011년의 기상조건에 따라 가정된 포장체 온도를 사용한 예측결과는 실제 교통개방일과 17일의 차이가 발생했으나 이는 2012년의 실제 기상상태와의 차이 때문이다. 실제 측정된 포장 온도를 예측모델에 대입할 경우 2일의 교통개방가능일 차이가 있었으며, 상관관계 분석 결과 R2가 0.95로 실제 강도값과 매우 유사한 결과를 얻을 수 있었다. 기상상태와 포장체의 온도에 대한 충분한 데이터를 확보한다면 에폭시 아스팔트 강도 예측모델을 사용하여 상당히 신뢰도 있는 교통개방 가능 시기의 예측이 가능한 것으로 나타났다.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1999년도 학회창립 10주년 기념 1999년도 가을 학술발표회 논문집
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• pp.155-158
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• 1999

This paper discusses the validity of models to predict the compressive strength of concrete subjected to various temperature histories and the shortcomings of existing rate constant model and apparent activation energy concept. Based on the discussion, a modified rate constant model is proposed. The modified rate constant model, in which apparent activation energy is a nonlinear function of curing temperature and age, accurately estimates the development of the experimental compressive strengths by a few researches. Also, the apparent activation energy of concrete cured with high temperature decreases rapidly with age, but that cured with low temperature decreases gradually with age. Finally a generalized model to predict apparent activation energy and compressive strength is proposed, which is based on the regression results.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2021년도 가을 학술논문 발표대회
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• pp.101-102
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• 2021

The compressive strength of concrete is greatly affected by the temperature inside the concrete at the initial age immediately after pouring. The apparent activation energy of cement and the setting time of concrete are major factors influencing the development of compressive strength of concrete. This study measured the apparent activation energy and setting time according to the change in W/B for each mixing rate of Ground Granulated Blast-Furnace Slag (GGBFS). And after calculating the compressive strength prediction model, the accuracy of the prediction model was evaluated by comparing the predicted compressive strength and the compressive strength.