• Computers and Concrete
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• v.22 no.4
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• pp.355-363
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• 2018

In the following paper, a socio-political heuristic search approach, named the imperialist competitive algorithm (ICA) has been used to improve the efficiency of the multi-layer perceptron artificial neural network (ANN) for predicting the compressive strength of concrete. 173 concrete samples have been investigated. For this purpose the values of slump flow, the weight of aggregate and cement, the maximum size of aggregate and the water-cement ratio have been used as the inputs. The compressive strength of concrete has been used as the output in the hybrid ICA-ANN model. Results have been compared with the multiple-linear regression model (MLR), the genetic algorithm (GA) and particle swarm optimization (PSO). The results indicate the superiority and high accuracy of the hybrid ICA-ANN model in predicting the compressive strength of concrete when compared to the other methods.

• Computers and Concrete
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• v.19 no.6
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• pp.617-623
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• 2017

This work is conducted with the aim of using waste material to reserve the natural resources. The objective is accomplished by conducting experimentation and verify by modeling based on fuzzy logic. In experimentation, concrete is casted by using natural/river sand as fine aggregate and termed as control specimen. Natural sand is conserved by replacing it with used foundry sand (UFS) by an amount of 10, 20 and 30% by weight. Fresh and hardened properties of concrete are investigated at different ages. It is observed that compressive strength and modulus of elasticity reduced with the increase in amount of UFS. Furthermore, concrete compressive strength is predicted by using fuzzy logic model and verified at different replacement ratio and age with experimental observations.

• Computers and Concrete
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• v.21 no.1
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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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• v.26 no.4
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• pp.309-316
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• 2020

The application of multi-variable adaptive regression spline (MARS) in predicting he long-term compressive strength of a concrete with various admixtures has been investigated in this study. The compressive strength of concrete specimens, which were made based on 24 different mix designs using various mineral and chemical admixtures in different curing ages have been obtained. First, The values of fly ash (FA), micro-silica (MS), water-reducing admixture (WRA), coarse and fine aggregates, cement, water, age of samples and compressive strength were defined as inputs to the model, and MARS analysis was used to model the compressive strength of concrete and to evaluate the most important parameters affecting the estimation of compressive strength of the concrete. Next, the proposed equation by the MARS method using particle swarm optimization (PSO) algorithm has been optimized to have more efficient equation from the economical point of view. The proposed model in this study predicted the compressive strength of the concrete with various admixtures with a correlation coefficient of R=0.958 rather than the measured compressive strengths within the laboratory. The final model reduced the production cost and provided compressive strength by reducing the WRA and increasing the FA and curing days, simultaneously. It was also found that due to the use of the liquid membrane-forming compounds (LMFC) for its lower cost than water spraying method (SWM) and also for the longer operating time of the LMFC having positive mechanical effects on the final concrete, the final product had lower cost and better mechanical properties.

• Computers and Concrete
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• v.11 no.6
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• pp.603-620
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• 2013

This paper improves genetic programming (GP) and weight genetic programming (WGP) and proposes soft-computing polynomials (SCP) for accurate prediction and visible polynomials. The proposed genetic programming system (GPS) comprises GP, WGP and SCP. To represent confined compressive strength and strain of circular concrete columns in meaningful representations, this paper conducts sensitivity analysis and applies pruning techniques. Analytical results demonstrate that all proposed models perform well in achieving good accuracy and visible formulas; notably, SCP can model problems in polynomial forms. Finally, concrete compressive strength and lateral steel ratio are identified as important to both confined compressive strength and strain of circular concrete columns. By using the suggested formulas, calculations are more accurate than those of analytical models. Moreover, a formula is applied for confined compressive strength based on current data and achieves accuracy comparable to that of neural networks.

• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.4
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• pp.97-105
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• 2001

The ultrasonic pulse velocity test has a strong potential to be developed into a very useful and relatively inexpensive in-place test for assuring the quality of concrete placed in structure. The main problem in realizing this potential is that the relationship between compressive strength ad ultrasonic pulse velocity is uncertain and concrete is an inherently variable material. The objective of this study is to improve the reliability of in-place concrete strength predictions by ultrasonic pulse velocity method. Experimental cement content, s/a rate, and curing condition of concrete. Accuracy of the prediction expressed in empirical formula are examined by multiple regression analysis and linear regression analysis and practical equation for estimation the concrete strength are proposed. Multiple regression model uses water-cement ratio cement content s/a rate, and pulse velocity as dependent variables and the compressive strength as an independent variable. Also linear regression model is used to only pulse velocity as dependent variables. Comparing the results of the analysis the proposed equation expressed highest reliability than other previous proposed equations.

• Computers and Concrete
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• v.21 no.4
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• pp.463-470
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• 2018

In the present study, soft computing i.e., machine learning techniques and regression models algorithms have earned much importance for the prediction of the various parameters in different fields of science and engineering. This paper depicts that how regression models can be implemented for the prediction of compressive strength of concrete. Three models are taken into consideration for this; they are Gaussian Process for Regression (GPR), Multi Adaptive Regression Spline (MARS) and Minimax Probability Machine Regression (MPMR). Contents of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate and age in days have been taken as inputs and compressive strength as output for GPR, MARS and MPMR models. A comparatively large set of data including 1030 normalized previously published results which were obtained from experiments were utilized. Here, a comparison is made between the results obtained from all the above mentioned models and the model which provides the best fit is established. The experimental results manifest that proposed models are robust for determination of compressive strength of concrete.

• Advances in concrete construction
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• v.16 no.6
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• pp.317-325
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• 2023

Strength prediction and correlation of concrete is done using experimental and analytical methods. Main objective is to correlate the experimental and simulated values of compressive strength of concrete mix using Fly Ash (FA) and Silica Fume (SF) by partial replacement of cement in concrete. Mix proportion was determined using IS method for M40grade concrete. Hundred and forty-seven cubes were cast and tested using Universal Testing Machine (UTM). Genetic Algorithm (GA) model was developed using C++ program to simulate the compressive strength of concrete for various proportions of FA and SF replacements individually at 3% increments. Experiments reveal that 12 percent silica fume replacement produced maximum compressive strength of 35.5 N/mm², 44.5 N/mm² and 54.8 N/mm² moreover 9 percent fly ash replacement produced a maximum strength of 31.9 N/mm², 37.6 N/mm² and 51.8 N/mm² during individual material replacement of concrete mix. Correlation coefficient for each curing period of fly ash and silica fume replaced mix were acquired using trend lines. The correlation coefficient is found to be approximately 0.9 in FA and SF replaced mix irrespective of the mix proportion and age of concrete. A higher and positive correlation was found between the experimental and simulated values irrespective of the curing period in all the replacements.

• Computers and Concrete
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• v.24 no.2
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• pp.137-150
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• 2019

In this paper, surrogate models such as multivariate adaptive regression splines (MARS) and M5P model tree (M5P MT) methods have been investigated in order to propose a new formulation for the 28-days compressive strength of self-compacting concrete (SCC) incorporating metakaolin as a supplementary cementitious materials. A database comprising experimental data has been assembled from several published papers in the literature and the data have been used for training and testing. In particular, the data are arranged in a format of seven input parameters covering contents of cement, coarse aggregate to fine aggregate ratio, water, metakaolin, super plasticizer, largest maximum size and binder as well as one output parameter, which is the 28-days compressive strength. The efficiency of the proposed techniques has been demonstrated by means of certain statistical criteria. The findings have been compared to experimental results and their comparisons shows that the MARS and M5P MT approaches predict the compressive strength of SCC incorporating metakaolin with great precision. The performed sensitivity analysis to assign effective parameters on 28-days compressive strength indicates that cementitious binder content is the most effective variable in the mixture.

• Proceedings of the Korea Concrete Institute Conference
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• 1999.10a
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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.