• Computers and Concrete
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• v.32 no.6
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• pp.577-594
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• 2023

Engineered cementitious composites with calcined clay limestone cement (LC³-ECC) as a kind of green, low-carbon and high toughness concrete, has recently received significant investigation. However, the complicated relationship between potential influential factors and LC³-ECC compressive strength makes the prediction of LC³-ECC compressive strength difficult. Regarding this, the machine learning-based prediction models for the compressive strength of LC³-ECC concrete is firstly proposed and developed. Models combine three novel meta-heuristic algorithms (golden jackal optimization algorithm, butterfly optimization algorithm and whale optimization algorithm) with support vector regression (SVR) to improve the accuracy of prediction. A new dataset about LC³-ECC compressive strength was integrated based on 156 data from previous studies and used to develop the SVR-based models. Thirteen potential factors affecting the compressive strength of LC³-ECC were comprehensively considered in the model. The results show all hybrid SVR prediction models can reach the Coefficient of determination (R²) above 0.95 for the testing set and 0.97 for the training set. Radar and Taylor plots also show better overall prediction performance of the hybrid SVR models than several traditional machine learning techniques, which confirms the superiority of the three proposed methods. The successful development of this predictive model can provide scientific guidance for LC³-ECC materials and further apply to such low-carbon, sustainable cement-based materials.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.7-13
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• 2018

The strength of concrete is affected significantly not only by the internal influence factors of cement, water, sand, aggregate, and admixture, but also by the external influence factors of concrete placement delay and curing temperature. The objective of this research was to predict the concrete strength considering both the internal and external influence factors when concrete is placed at the construction site. In this study, a concrete strength test was conducted on the 24 combinations of internal and external influence factors, and a neural network model was constructed using the test data. This neural network model can predict the concrete strength considering the external influence factors of the concrete placement delay and curing temperature when concrete is placed at the construction site. Contractors can use the concrete strength prediction neural network model to make concrete more robust to external influence factors during concrete placement at a construction site.

• Journal of the Korea Institute of Building Construction
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• v.19 no.6
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• pp.503-510
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• 2019

Recently, construction technology of RC structures must be examined for creep in concrete. The factors affecting the creep prediction of concrete and the results of creep in domestic construction field were reviewed. The longer the creep test period and the higher the compressive strength, the higher the creep prediction accuracy. The higher the curing temperature, the higher the initial strength development of the concrete, but the difference in the creep coefficients increased over time. Based on the results of creep evaluation in the domestic construction field and lab. tests, a modified predictive model that complements the ACI-209 model was proposed. In the creep prediction of real members using general to high strength concrete, the test period and temperature should be considered precisely.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.03b
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• pp.591-598
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• 2000

In the present study, the variation of settlement, pore water pressure and undrained shear strength through model tests were measured. Also, the variation of water content, unit weight and shear strength by the vane shear tests were observed. In this study, appropriate deposit time of construction equipments used in treatment of hydraulic fills is determined from the prediction curve of increased shear strength in dredged fills.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.149-150
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• 2023

This study proposes the most suitable strength prediction model equation for UHPC by calculating the apparent activation energy of UHPC according to the curing temperature and deriving the integrated temperature and compressive strength prediction equation. The results are summarized as follows. The apparent activation energy was calculated using the Arrhenius function, which was calculated as 21.09 KJ/mol. A model equation suitable for UHPC was calculated, and when the Flowman model equation was used, it was confirmed that it was suitable for the properties of UHPC using a condensation promoting super plasticizing agent.

• Journal of the Korean Geosynthetics Society
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• v.10 no.4
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• pp.61-70
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• 2011

This paper describes prediction results of penetration rate using case history in order to compare empirical models for penetration rate prediction of TBM. The reasonable empirical model is evaluated by comparison with prediction results and measured result. The penetration rate prediction is applied in separate empirical models considering rock characteristics and mechanical characteristics of TBM. The rock of applied filed had almost gneiss and its unconfined compressive strength was irregular due to the exist of weak zones and joint. In prediction results using unconfined compressive strength, Graham's model (1976) had impractical result when it had lower strength. NTNU model (1998) of the separate empirical models used in average penetration rate had the highest accuracy by comparison with the others, because it is a reasonable model which has rock characteristics and mechanical characteristics of TBM. However, Tarkoy's model (1986) based on unconfined compressive strength correspond with the measured values in field. Therefore, it should be considered a rock type, geological characteristic and mechanical characteristic of TBM at prediction of penetration rate.

• Proceedings of the Korea Concrete Institute Conference
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• 2003.05a
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• pp.918-923
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• 2003

Reinforced concrete deep beams are commonly used in many structural applications, including transfer girders, pile caps, foundation walls, and offshore structures. The existing design methods were developed and calibrated using normal strength concrete test results, and their applicability th HSC deep beams must be assessed. For the shear strength prediction of high-strength concrete(HSC) deep beams, this paper proposed Softened Strut-and-Tie Model(SSTM) considered HSC and bending moment effect. The shear strength predictions of the refined model, the formulas the ACI 318-02 Appendix A STM, and Eq. of ACI 318-99 11.8 are compared with the collected experimental data of 74 HSC deep beams with compressive strength in the range of 49-78MPa . It is shown the shear strength of deep beam calculated by those equations are conservative on comparing test results. The comparison shows that the performance of the proposed SSTM is better than the ACI Code approach for all the parameters under comparison. The parameters reviewed include concrete strength, the shear span-depth ratio, and the ratio of horizontal and vertical reinforcement. The proposed SSTM gave a mean predicted to experimental ratio of 0.99, 32 percent higher than ACI 318-02 Code, however with the low coefficient variation.

• Computers and Concrete
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• v.5 no.5
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• pp.461-473
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• 2008

The paper explores the potential of Support Vector Machines (SVM) approach in predicting 28-day compressive strength and slump flow of self-compacting concrete. Total of 80 data collected from the exiting literature were used in present work. To compare the performance of the technique, prediction was also done using a back propagation neural network model. For this data-set, RBF kernel worked well in comparison to polynomial kernel based support vector machines and provide a root mean square error of 4.688 (MPa) (correlation coefficient=0.942) for 28-day compressive strength prediction and a root mean square error of 7.825 cm (correlation coefficient=0.931) for slump flow. Results obtained for RMSE and correlation coefficient suggested a comparable performance by Support Vector Machine approach to neural network approach for both 28-day compressive strength and slump flow prediction.

• Journal of the Korean Society of Industry Convergence
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• v.10 no.3
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• pp.143-147
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• 2007

In recents years, the statistical properties has become an important quantity for reliability based design of a component. The effects of the materials and test conditions for parameter estimation in residual strength degradation model are studied in carbon/epoxy laminate. It is shown that the correlation between the experimental results and the theoretical prediction on the fatigue life distribution using the life distribution convergence method is very reasonable.

• Proceedings of the Korea Concrete Institute Conference
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• 2003.05a
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• pp.908-911
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• 2003

This study was performed to verify the effect of reinforcement of RC Beams strengthened($90^{\circ}$ strip type) by FRP(CFRP) and Predited the shear strength of them using the upper bound theorem. The prediction model was confirmed with the result of the FEM analysis. The analyzed result showed thar shear-damaged RC beams by strengthened by FRP was improved their shear capacity.