• Computers and Concrete
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• 제6권2호
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• pp.155-165
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• 2009

Splitting tensile strength (STS) of high-performance concrete (HPC) is one of the important mechanical properties for structural design. This property is related to compressive strength (CS), water/binder (W/B) ratio and concrete age. This paper presents a clustering-based fuzzy model for the prediction of STS based on the CS and (W/B) at a fixed age (28 days). The data driven fuzzy model consists of three main steps: fuzzy clustering, inference system, and prediction. The system can be analyzed directly by the model from measured data. The performance evaluations showed that the fuzzy model is more accurate than the other prediction models concerned.

• Computers and Concrete
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• 제24권4호
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• pp.295-302
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• 2019

Evolutionary algorithms based on conventional statistical methods such as regression and classification have been widely used in data mining applications. This work involves application of gene expression programming (GEP) for predicting compressive strength of fly ash geopolymer concrete, which is gaining increasing interest as an environmentally friendly alternative of Portland cement concrete. Based on 56 test results from the existing literature, a model was obtained relating the compressive strength of fly ash geopolymer concrete with the significantly influencing mix design parameters. The predictions of the model in training and validation were evaluated. The coefficient of determination ($R^2$), mean (${\mu}$) and standard deviation (${\sigma}$) were 0.89, 1.0 and 0.12 respectively, for the training set, and 0.89, 0.99 and 0.13 respectively, for the validation set. The error of prediction by the model was also evaluated and found to be very low. This indicates that the predictions of GEP model are in close agreement with the experimental results suggesting this as a promising method for compressive strength prediction of fly ash geopolymer concrete.

• Computers and Concrete
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• 제13권4호
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• pp.531-545
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• 2014

The purpose of this paper is to develop a prediction model for the compressive strength of waste LCD glass applied in concrete by analyzing a series of laboratory test results, which were obtained in our previous study. The hyperbolic function was used to perform the nonlinear-multivariate regression analysis of the compressive strength prediction model with the following parameters: water-binder ratio w/b, curing age t, and waste glass content G. According to the relative regression analysis, the compressive strength prediction model is developed. The calculated results are in accord with the laboratory measured data, which are the concrete compressive strengths of different mix proportions. In addition, a coefficient of determination $R^2$ value between 0.93 and 0.96 and a mean absolute percentage error MAPE between 5.4% and 8.4% were obtained by regression analysis using the predicted compressive analysis value, and the test results are also excellent. Therefore, the predicted results for compressive strength are highly accurate for waste LCD glass applied in concrete. Additionally, this predicted model exhibits a good predictive capacity when employed to calculate the compressive strength of washed glass sand concrete.

• 콘크리트학회논문집
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• 제15권1호
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• pp.87-94
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• 2003

현재 건설생산현장에서 이루어지고 있는 거푸집 제거 시기 결정, 설계기준강도 확보 등의 강도관리는 그 시점을 예측할 수 없다는 단점이 있기 때문에 건설생산현장에서의 공정계획 및 강도관리에서 한계가 있을 수밖에 없다. 이에 따라 콘크리트의 강도를 예측할 수 있으면 보다 합리적인 강도관리 및 공정계획이 가능하게 된다. 본 연구는 적산온도 방법에 의해 새로 제안된 강도예측모델의 적용가능성을 검증하기 위해 기존 강도예측모델 중 Logistic 모델과 비교 평가하였으며, 모의부재에서 채취한 코어공시체와 현장양생공시체의 압축강도를 비교 평가한 후 새로운 강도예측모델에 의해 강도를 예측하여 거푸집 제거시기를 결정하는 것에 대한 합리성을 검증하고자 하였다. 실험결과 Freiesleben의 활성화에너지를 이용한 등가재령함수에 있어서 콘크리트의 강도는 양생온도에 관계없이 유사한 강도수준을 나타내고 있으나 강도-적산온도의 상관성을 높이기 위해서는 등가재령 계산시 이용되는 활성화에너지에 대한 검토가 필요할 것으로 사료된다. 새로 제안된 모델의 경우 Logistic 모델에 비해 초기재령에 있어서 강도예측이 보다 정확한 것으로 나타났으며, SSE는 작고 결정계수는 높게 나타나고 있어 이를 이용한 강도예측이 보다 합리적일 것으로 판단된다. 본 연구의 범위 내에서 양생온도 10~15$^{\circ}C$의 경우 강도관리 측면에서 새로운 강도예측모델 사용시 압축강도 50kgf/${cm}^2$ 발현시점이 기존에 제안된 기간과 비교하여 빠르게 나타나고 있어 이를 건설생산현장에서 적용할 경우 거푸집제거시기의 단축에 의한 공기단축이 가능할 것으로 사료된다.

• 제어로봇시스템학회논문지
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• 제20권1호
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• pp.58-69
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• 2014

This paper proposes a method for predicting maximum friction coefficients and optimal slip ratios as optimal control parameters for traction control or slip control of autonomous mobile robots on rough terrain. This paper focuses on strength of ground surface which indicates different characteristics depending on material types on surface. Strength of various material types can be estimated by Willoughby sinkage model and by a developed testbed which can measure forces, velocities, and displacements generated by wheel-terrain interaction. Estimated strength is collaborated on building improved Brixius model with friction-slip data from experiments with the testbed over sand and grass material. Improved Brixius model covers widespread material types in outdoor environments on predicting friction-slip characteristics depending on strength of ground surface. Thus, a prediction model for obtaining optimal control parameters is derived by partial differentiation of the improved Brixius model with respect to slip. This prediction model can be applied to autonomous mobile robots and finally gives secure maneuverability on rough terrain. Proposed method is verified by various experiments under similar conditions with the ones for real outdoor robots.

• 소성∙가공
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• 제29권2호
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• pp.76-88
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• 2020

The need for advanced high strength steel (AHSS) forming technology is increasing as interest in light weight and safe automobiles increases. Multipoint dieless forming (MDF) is a novel sheet metal forming technology that can create any desired longitudinal and transverse curvature in sheet metal. However, since the springback phenomenon becomes larger with high strength metal such as AHSS, predicting the required MDF to produce the exact desired curvature in two directions is more difficult. In this study, a prediction model using artificial neural network (ANN) was developed to predict the springback that occurs during AHSS forming through MDF. In order to verify the validity of model, a fit test was performed and the results were compared with the conventional regression model. The data required for training was obtained through simulation, then further random sample data was created to verify the prediction performance. The predicted results were compared with the simulation results. As a result of this comparison, it was found that the prediction of our ANN based model was more accurate than regression analysis. If a sufficient amount of data is used in training, the ANN model can play a major role in reducing the forming cost of high-strength steels.

• Computers and Concrete
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• 제19권5호
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• pp.457-465
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• 2017

The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2013년도 춘계 학술논문 발표대회
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• pp.89-90
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• 2013

This study is to predict the compressive strength for the concrete of ground granulated blast-furnace slag, and use Plowman's, Gompertz's model. The results are as follows; The prediction compressive strength were simiar using Rastrup's equivalent age model. but The prediction compressive strength using Freiesleben's equivalent age model weren't simiar in bfs replacement Ratio of 50%, because it is analyzed as the activation energy.

• Structural Engineering and Mechanics
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• 제56권2호
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• pp.201-221
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• 2015

The fatigue damage problems are frequently encountered in the design of civil engineering structures. A realistic and accurate fatigue life prediction is quite essential to ensure the safety of engineering design. However, constructing a reliable fatigue life prediction model can be quite challenging. The use of traditional deterministic approach in predicting the fatigue life is sometimes too dangerous in the real practical designs as the method itself contains a wide range of uncertain factors. In this paper, a new fatigue life prediction method is going to be proposed where the residual strength is been utilized. Several cumulative damage models, capable of predicting the fatigue life of a structural element, are considered. Based on Miner's rule, a randomized approach is developed from a deterministic equation. The residual strength is used in a one to one transformation methodology which is used for the derivation of the fatigue life. To arrive at more robust results, fuzzy sets are introduced to model the parameter uncertainties. This leads to a convoluted fuzzy based fatigue life prediction model. The developed model is illustrated in an example analysis. The calculated results are compared with real experimental data. The applicability of this approach for a required reliability level is also discussed.

• 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.