• 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.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2005년도 봄학술 발표회 논문집(I)
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• pp.131-134
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• 2005

Until recently tensile stresses in concrete have not been considered, since it does not affect the ultimate strength of reinforced concrete flexural members significantly. However, to verify the load-deflection relationship, the effect of tensile stresses between reinforcing bars and concrete, so-called tension stiffening effect must be taken into account. Main parameters of the tension stiffening behavior are known as concrete strength, and bond between concrete and reinforcing bars. In this study a total of twenty specimen subject to bending was tested with different concrete strength, coverage, and de-bonding length of longitudinal bars. The effects of these parameters on the flexural rigidity, crack initiation and propagation were carefully checked and analyzed.

• Geomechanics and Engineering
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• 제23권2호
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• pp.115-125
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• 2020

This study aims to explore practical and useful equations for rapid evaluation of uniaxial compressive strength of concrete (UCS-C) during the preliminary design stage of aggregate selection. For this purpose, aggregates which were produced from eight different intact rocks were used in the production of concretes. Laboratory experiments involved the tests for uniaxial compressive strength (UCS-R), point load index (PLI-R), P wave velocity (UPV-R), apparent porosity (n-R), unit weight (UW-R) and aggregate impact value (AIV-R) of the rock samples. UCS-C, point load index (PLI-C) and P wave velocity (UPV-C) of concrete samples were also determined. Relationships between UCS-R-rock parameters and UCS-C-concrete parameters were developed by regression analyses. In the simple regression analyses, PLI-C, UPV-C, UCS-R, PLI-R, and UPV-R were found to be statistically significant independent variables to estimate the UCS-C. However, higher coefficients of determination (R²=0.97-1.0) were obtained by multiple regression analyses. The results of simple regression analysis were also compared to the limited number of previous studies. The strength conversion factor (k) values were found to be 14.3 and 14.7 for concrete and rock samples, respectively. It is concluded that the UCS-C can roughly be estimated from derived equations only for the specified rock types.

• Structural Engineering and Mechanics
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• 제66권2호
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• pp.203-215
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• 2018

A large body of experiments have been conducted to date to evaluate the punching shear strength of flat slab-column connections, but it is noted that only a few of them have been considered for the development of the ACI Code provisions. The limited test results used for the development of the code provisions fall short of predicting accurately the punching shear strength of such connections. In an effort to address this shortfall and to gain an insight into the factors that control the punching shear strength of flat slab-column connections, we report a qualified database of 650 punching shear test results in this article. All slabs examined in this database were tested under gravity loading and do not contain shear reinforcement. In order to justify including any test result for evaluation punching shear database, we have developed an approved set of criteria. Carefully established set of criteria represent the actual characteristics of structures that include minimum compressive strength, effective depths of slab, flexural and compression reinforcement ratio and column size. The key parameters that significantly affect the punching shear strength of flat slab-column connections are then examined using ACI 318-14 expression. The results reported here have paramount significance on the range of applicability of the ACI Code provision and seem to indicate that the ACI provisions do not sufficiently capture many trends identified through regression of the principal parameters, and fall on the unsafe side for the prediction of the punching shear strength of flat slab-column connections.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1997년도 가을 학술발표회 논문집
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• pp.408-415
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• 1997

Bond strength of reinforcing bar to high-performance concrete using Belite cement is explored using beam end test specimen. The key parameters for the bond test are slump of concrete, top bar effect, and strength of concrete in addition to concrete covers. Specimen failed in the typical brittle bond failure splitting the concrete cover as the wedging action. The test results show that for the group with portland cement I using superplasticizer additional slump does not decrease the bond strength of the top bar is less than bond strength of bottom bar, but the top bar factor satisfy the modification factor for top reinforcement. The result also show that bond strength is function of square root of concrete compressive strength and cover thickness. More detailed evaluation will be conducted from the test specimen with high strength concrete using the belite cement.

• Structural Engineering and Mechanics
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• 제83권4호
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• pp.515-535
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• 2022

The strength models for fiber-reinforced polymer (FRP)-confined normal strength concrete (NC) cylinders available in the literature have been suggested based on small databases using limited variables of such structural members portraying less accuracy. The artificial neural network (ANN) is an advanced technique for precisely predicting the response of composite structures by considering a large number of parameters. The main objective of the present investigation is to develop an ANN model for the axial strength of FRP-confined NC cylinders using various parameters to give the highest accuracy of the predictions. To secure this aim, a large experimental database of 313 FRP-confined NC cylinders has been constructed from previous research investigations. An evaluation of 33 different empirical strength models has been performed using various statistical parameters (root mean squared error RMSE, mean absolute error MAE, and coefficient of determination R²) over the developed database. Then, a new ANN model using the Group Method of Data Handling (GMDH) has been proposed based on the experimental database that portrayed the highest performance as compared with the previous models with R²=0.92, RMSE=0.27, and MAE=0.33. Therefore, the suggested ANN model can accurately capture the axial strength of FRP-confined NC cylinders that can be used for the further analysis and design of such members in the construction industry.

• Composite Materials and Engineering
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• 제3권3호
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• pp.201-220
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• 2021

In this study, potential of three machine learning techniques i.e., M5P, Support vector machines and Gaussian processes were evaluated to find the best algorithm for the prediction of flexural strength of concrete mix with steel fibre. The study comprises the comparison of results obtained from above-said techniques for given dataset. The dataset consists of 124 observations from past research studies and this dataset is randomly divided into two subsets namely training and testing datasets with (70-30)% proportion by weight. Cement, fine aggregates, coarse aggregates, water, super plasticizer/ high-range water reducer, steel fibre, fibre length and curing days were taken as input parameters whereas flexural strength of the concrete mix was taken as the output parameter. Performance of the techniques was checked by statistic evaluation parameters. Results show that the Gaussian process technique works better than other techniques with its minimum error bandwidth. Statistical analysis shows that the Gaussian process predicts better results with higher coefficient of correlation value (0.9138) and minimum mean absolute error (1.2954) and Root mean square error value (1.9672). Sensitivity analysis proves that steel fibre is the significant parameter among other parameters to predict the flexural strength of concrete mix. According to the shape of the fibre, the mixed type performs better for this data than the hooked shape of the steel fibre, which has a higher CC of 0.9649, which shows that the shape of fibers do effect the flexural strength of the concrete. However, the intricacy of the mixed fibres needs further investigations. For future mixes, the most favorable range for the increase in flexural strength of concrete mix found to be (1-3)%.

• Computers and Concrete
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• 제12권1호
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• pp.53-64
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• 2013

The curing temperature is known to influence the rate of mechanical properties development of early age concrete. In realistic sites the temperature of concrete is not isothermal $20^{\circ}C$, so the paper measured adiabatic temperature increases of four different concretes to understand heat emission during hydration at early age. The temperature-matching curing schedule in accordance with adiabatic temperature increase is adopted to simulate the situation in real massive concrete. The specimens under temperature-matching curing are subjected to realistic temperature for first few days as well as adiabatic condition. The mechanical properties including compressive strength, splitting strength and modulus of elasticity of concretes cured under both temperature-matching curing and isothermal $20^{\circ}C$ curing are investigated. The results denote that comparing temperature-matching curing with isothermal $20^{\circ}C$ curing, the early age concretes mechanical properties are obviously improved, but the later mechanical properties of concretes with pure Portland and containing silica fume are decreased a little and still increased for concretes containing fly ash and slag. On this basement using an equivalent age approach evaluates mechanical properties of early age concrete in real structures, the model parameters are defined by the compressive strength test, and can predict the compressive strength, splitting strength and elasticity modulus through measuring or calculating by finite element method the concreted temperature at early age, and the method is valid, which is applied in a concrete wall for evaluation of crack risking.

• Smart Structures and Systems
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• 제3권1호
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• pp.51-68
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• 2007

High-strength concrete (HSC) is becoming increasingly attractive for various construction projects since it offers a multitude of benefits over normal-strength concrete (NSC). Unfortunately, current design provisions for shear capacity of RC slender beams are generally based on data developed for NSC members having a compressive strength of up to 50 MPa, with limited recommendations on the use of HSC. The failure of HSC beams is noticeably different than that of NSC beams since the transition zone between the cement paste and aggregates is much denser in HSC. Thus, unlike NSC beams in which micro-cracks propagate around aggregates, providing significant aggregate interlock, micro-cracks in HSC are trans-granular, resulting in relatively smoother fracture surfaces, thereby inhibiting aggregate interlock as a shear transfer mechanism and reducing the influence of compressive strength on the ultimate shear strength of HSC beams. In this study, a new approach based on genetic algorithms (GAs) was used to predict the shear capacity of both NSC and HSC slender beams without shear reinforcement. Shear capacity predictions of the GA model were compared to calculations of four other commonly used methods: the ACI method, CSA method, Eurocode-2, and Zsutty's equation. A parametric study was conducted to evaluate the ability of the GA model to capture the effect of basic shear design parameters on the behaviour of reinforced concrete (RC) beams under shear loading. The parameters investigated include compressivestrength, amount of longitudinal reinforcement, and beam's depth. It was found that the GA model provided more accurate evaluation of shear capacity compared to that of the other common methods and better captured the influence of the significant shear design parameters. Therefore, the GA model offers an attractive user-friendly alternative to conventional shear design methods.

• 한국정밀공학회지
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• 제16권10호
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• pp.141-151
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• 1999

The resistance Spot Welding is widely used in the field of assembling the plates. However we don't still have any satisfactory solution, which is non-destructive quality evaluation in real-time or on-line, against it. Moreover, even though the rate of welding under the condition of expulsion has been high until now, quality control of welding against expulsion hasn't still been established. In this paper, it was proposed on the quality assurance technique of resistance spot welding pieces using Neuro-Fuzzy algorithm. Four parameters from electrode separation signal in the case of non-expulsion, and dynamic resistance patterns in the case of expulsion are selected as fuzzy input parameters. The parameters consist of Fuzzy Inference System are determined through Neuro-Learning algorithm. And then, fuzzy Inference System is constructed. It was confirmed that the fuzzy inference values of strength have within ${\pm}$4% error specimen in comparison with real strength for the total strength range, and the specimen percent having within ${\pm}$1% error was 88.8%. According to KS(Korean Industrial Standard), tensile-shear strength limit for electric coated of zinc is 400kgf/mm2. Judging to the quality of welding is good or bad, according to this criterion and the results of inference, the probability of misjudgement that good quality is valuated into poor one was 0.43%, on contrary it was 2.59%. Finally, the proposed Neuro-Fuzzy Inference System can infer the tensile-shear strength of resistance spot welding pieces with high performance for all cases-non-expulsion and expulsion. And On-Line Welding Quality Inspection System will be realized sooner or later.