• Structural Engineering and Mechanics
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• v.88 no.1
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• pp.95-107
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• 2023

In this study, circular thin-walled reinforced high strength concrete-filled steel tube (RHSCFST) stub columns with various tube thicknesses (i.e., 1.8, 2.5 and 3.0mm) and reinforcement ratios (i.e., 0, 1.6%, 2.4% and 3.2%) were fabricated to explore the influence of these factors on the axial compressive behavior of RHSCFST. The obtained test results show that the failure mode of RHSCFST transforms from outward buckling and tearing failure to drum failure with the increasing tube thickness. With the tube thickness and reinforcement ratio increased, the ultimate load-carrying capacity, compressive stiffness and ductility of columns increased, while the lateral strain in the stirrup decreased. Comparisons were also made between test results and the existing codes such as AIJ (2008), BS5400 (2005), ACI (2019) and EC4 (2010). It has been found that the existing codes provide conservative predictions for the ultimate load-carrying capacity of RHSCFST. Therefore, an accurate model for the prediction of the ultimate load-carrying capacity of circular thin-walled RHSCFST considering the steel reinforcement is developed, based on the obtained experimental results. It has been found that the model proposed in this study provides more accurate predictions of the ultimate load-carrying capacity than that from existing design codes.

• Korean Journal of Food Science and Technology
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• v.25 no.6
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• pp.632-636
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• 1993

Gel strength of fish protein at various processing conditions such as heating temperature, heating time and salt content was determined by using compressive stress and residual delay time of ultrasonic wave. The compressive stress, interpreted as indicating the relative gel strength, was increased with increasing the heating temperature and heating time, and with decreasing the salt content, while the delay time of ultrasonic wave reduced, indicating that the gel strength and the delay time are inverse proportion. The result of the multiple regression analysis with factorial design showed that the model equation consisted with delay time and processing condition variables gave the good prediction of the gel compressive stress which was coincided with compressive stress measured.

• Computers and Concrete
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• v.32 no.3
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• pp.327-337
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• 2023

Concrete is the most widely used building material, with various types including high- and ultra-high-strength, reinforced, normal, and lightweight concretes. However, accurately predicting concrete properties is challenging due to the geotechnical design code's requirement for specific characteristics. To overcome this issue, researchers have turned to new technologies like machine learning to develop proper methodologies for concrete specification. In this study, we propose a highly accurate deep learning-based predictive model to investigate the compressive strength (UCS) of lightweight concrete with natural aggregates (pumice). Our model was implemented on a database containing 249 experimental records and revealed that water, cement, water-cement ratio, fine-coarse aggregate, aggregate substitution rate, fine aggregate replacement, and superplasticizer are the most influential covariates on UCS. To validate our model, we trained and tested it on random subsets of the database, and its performance was evaluated using a confusion matrix and receiver operating characteristic (ROC) overall accuracy. The proposed model was compared with widely known machine learning methods such as MLP, SVM, and DT classifiers to assess its capability. In addition, the model was tested on 25 laboratory UCS tests to evaluate its predictability. Our findings showed that the proposed model achieved the highest accuracy (accuracy=0.97, precision=0.97) and the lowest error rate with a high learning rate (R2=0.914), as confirmed by ROC (AUC=0.971), which is higher than other classifiers. Therefore, the proposed method demonstrates a high level of performance and capability for UCS predictions.

• Steel and Composite Structures
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• v.46 no.2
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• pp.211-220
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• 2023

Welded hollow spherical joints are commonly used joints in space grid structures. An internal stiffener is generally adopted to strengthen the joints when large hollow spheres are used. To further strengthen it, external stiffeners can be used at the same time. In this study, axial compression tests are conducted on four full-scale 550 mm spherical joints. The failure modes and strengths of the tested joints are investigated. It shows that the external stiffeners are able to increase the strength of the joint up to 25%. A numerical model for large spherical joints with stiffeners is established and verified against the experimental results. Parametric studies are executed considering six main design factors using the verified model. It is found that the strength of the spherical joint increases as the thickness, height and number of the external stiffeners increase, and the hollow sphere's diameter has a neglectable effect on the enhancement caused by the external stiffeners. Based on the experimental and numerical results, a practical formula for the compressive bearing capacity of large welded hollow spherical joints with both internal and external stiffeners is proposed. The proposed formula gives a conservative prediction on the compressive capacity of large welded hollow spherical joints with both internal and external stiffeners.

• Computers and Concrete
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• v.34 no.1
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• pp.33-48
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• 2024

This paper presents the detailed experimental and analytical investigation on the evolution of static (E_s) and dynamic modulus of elasticity (E_d) of concrete having 0%, 35%, and 50% FA used as partial cement replacement. Destructive and non-destructive tests were conducted on cylindrical specimens to evaluate the compressive strength and MoE of concrete in compression at the age of 28, 56, 90, and 150 days for all mixes. Experimental results show that the concrete having 35% FA achieved compressive strength and MoE similar to plain concrete at the age of 90 days, while 50% FA concrete attained satisfactory compressive strength and MoE at the age of 150 days. The comprehensive statistical analysis has been carried out in two ways on the basis of the experimental results. Firstly, the 28-day crushing strength of plain concrete in compression was used to design the models for the prediction of E_s and E_d of fly ash concrete at any age and percentage replacement of FA. Secondly, using the values of UPV and RHN, models have been developed to predict the age or time-dependent E_s and E_d of fly ash concrete. These models will be helpful in assessing the E_s and E_d of fly ash concrete without knowing the 28-day crushing strength of plain concrete in compression in the laboratory. Hence, the suggested models in the present study will be beneficial in conducting the health assessment of fly ash based concrete structures.

• Computers and Concrete
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• v.29 no.3
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• pp.145-159
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• 2022

In this study, the extreme learning machine and deep learning models were devised to estimate the bond strength of corroded reinforcement in concrete. The six inputs and one output were used in this study. The compressive strength, concrete cover, bond length, steel type, diameter of steel bar, and corrosion level were selected as the input variables. The results of bond strength were used as the output variable. Moreover, the Analysis of variance (Anova) was used to find the effect of input variables on the bond strength of corroded reinforcement in concrete. The prediction results were compared to the experimental results and each other. The extreme learning machine and the deep learning models estimated the bond strength by 99.81% and 99.99% accuracy, respectively. This study found that the deep learning model can be estimated the bond strength of corroded reinforcement with higher accuracy than the extreme learning machine model. The Anova results found that the corrosion level was found to be the input variable that most affects the bond strength of corroded reinforcement in concrete.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.6
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• pp.124-133
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• 2012

Compressive strength in concrete increases with time. Regression analysis with time is conventionally performed for strength evaluation and prediction. In this study, hydrate amount is assumed as a function of hydration rate and porosity, and modeling on compressive strength is carried out considering decreasing porosity with time, which does not need the regression analysis with time. For twenty one mix proportions of HPC (High Performance Concrete), DUCOM (FE program) which can simulate the behavior in early aged concrete is utilized, and porosity from each mix proportions is obtained with time. For HPC with OPC (Ordinary Portland Cement) concrete, modeling on compressive strength is performed considering hydration rate, unit content of cement, and porosity with time. For HPC with mineral admixtures, a long-term parameter which can handle long-term strength development is additionally considered. From the comparison with the previous test results, the applicability of the proposed model is verified.

• Journal of the Korea Institute of Building Construction
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• v.18 no.6
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• pp.517-525
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• 2018

The construction of super tall building which structure is RC and must be certainly considered on column shortening estimation and construction reflected concrete creep has been increased. Regarding the Fck 60~80MPa grade high strength concrete applied in the domestic super tall building project, the mechanical properties and creep deflection according to curing conditions(Drying creep/Basic creep) were reviewed in this research. Results of compressive strength and elastic modulus under sealed curing condition were 5% higher than unsealed condition and difference of results according to the curing condition was increased over time. Autogenous and drying shrinkage tendency showed adversely in the case of high strength concrete. Additionally, creep modulus under unseal curing condition was evaluated 2~3 times higher than sealed condition. Modified model of ACI-209 based on test result was applied to estimate long period shortening of vertical members(such as Core Wall/Mega Column) exactly, it is designed to modify and suggest the optimal creep model based on various data accumulated during construction, in the future.

• Journal of Korean Association for Spatial Structures
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• v.23 no.4
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• pp.81-88
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• 2023

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

• Journal of the Korea Concrete Institute
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• v.27 no.6
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• pp.669-676
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• 2015

Time dependent model for prediction of compressive strength development of concrete cured by microwave heating form was presented in this study. The presented model is similar to the equation which is given in ACI 209R-92 but the constants which is dependent on cement type and curing method in the presented model are modified by the regression analysis of the experimental data. Laboratory scale concrete specimens were cast and cured by the microwave heating form and drilled cores extracted from the specimens were fractured in compression. The measured core strengths are converted to standard core and in-situ strengths. These in-situ strengths are used for the regression.