### PDF Experimental Investigation and Prediction of Compressive

4.4 Compressive Strength of concrete 5. Prediction of Compressive Strength Using Soft Computing Techniques 5.1 Multiple linear Regression 5.2 Regression and Correlation Analysis 5.3 Multiple regression analysis 5.4 Methodology of Statistical Analysis 5.5 Formulae Derived from the Analysis

Learn More### PREDICTION OF CONCRETE MIX COMPRESSIVE STRENGTH

Prediction of Concrete Mix Compressive Strength Using Statistical . . . .1917 Journal of Engineering Science and Technology July , Vol. 13(7) 1. Introduction Today, there is a high demand for concrete with enhanced properties to serve specific infrastructure needs. Demand for High-Performance Concrete (HPC) with high durability is on the rise.

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With the obtained linear equation, data from 10 testing dataset are used to calculate the concrete compressive strength estimation. The prediction results are summarized in Table 2 and the MAPE for the multi-variable linear regression is 8.73%. Y = 1.25733 R + 0.0182 V − 65.387.

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Fe415. The results of the compression test conducted on 150 mm cubes at 7, 14 and 28 days are given in Table 2. The midspan deflections were measured at different load levels using a dial gauge. Table 2: Compressive Strength of Concrete at Different Ages (days) B8 V. DISCUSSION OF RESULTS Table 1 shows that the machine learning tool SVM gives

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Sobhani, Khanzadi and Movahedian [3] proposed the use of support vector machine for the prediction of compressive strength of no slump concrete. The input parameters used are cement, silica fumes, water, fine aggregate, coarse aggregate andfiller if any. The output parameter was the compressive strength of no slump concrete.

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Abstract. The aim of this study is prediction of 28-day compressive strength of concrete by data-driven models. Hence, by considering concrete constituents as input variables, two data-driven models namely Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models are constructed for the purpose of predicting the 28-days compressive strength of different concrete mix designs.

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Feb 25, · Second, as the indispensable tool for production, the machine directly affects the concrete property. In this study, the considered impact from the machine aspect mainly covers the reliability of the material weighing scales and the stable current value of the mixing unit. Wu Y. High-performance Concrete Compressive Strength Prediction

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2.2 Concrete compressive strength Studies have revealed that the compressive strength of the concrete nearby the rebar is straightly relevant to the failure type as well as the bond strength. Malvar (1994) showed that for the splitting failure, an increment in the concrete compressive strength would improve the bond strength.

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The accuracy of the proposed method was calculated using two performance measurements. Subsequently, the study has successfully built the prediction model that can accurately map the relationship between concrete mix proportion and compressive strength.

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Modeling of strength of high-performance concrete using artificial neural networks. Cem Concr Res. v28. 1797-1808. Google Scholar [2] Demir, F., A new way of prediction elastic modulus of normal and high strength concrete-fuzzy logic. Cem Concr Res. v35. 1531-1538. Google Scholar

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Home concrete compressive strength testing machine. Browsing Tag. concrete compressive strength testing machine. 1 post What is the difference between apache mahout and Prediction.io ? 124 views; World's No 1 Animated self learning Website with Informative tutorials explaining the code and the choices behind it all.

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Jul 01, · Despite the plethora of published works on machine learning methods for the prediction of concrete compressive strength based on mix parameters [, , , ,, , ,, , , , , , , , , , ], the comparison among the proposed models is not straightforward mainly due to the different databases used for their training. For a fair comparison the same database, the same input parameters and the same percentage of training

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The ultrafine fly ash (FA) is a hazardous material collected from coal productions, which has been proficiently employed for the manufacturing of geopolymer concrete (GPC). In this study, the three artificial intelligence (AI) techniques, namely, artificial neural network (ANN), adaptive neuro-fuzzy interface (ANFIS), and gene expression programming (GEP) are used to establish a reliable and

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Mar 01, · In this study, a support vector machine model available in Weka Algorithms, was utilized to test the predictive capacity of compressive strength in high performance concrete (HSC) with steel fiber addition. To test the performance of the algorithm, a certain percentage were allocated for training of the algorithm, and the rest for test. This was done from 60-40 percent split up to 90-10

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structural grade concrete. Compressive strength refers to the level of uniaxial compressive stress when the element becomes fully dissociated and other concrete properties such as elastic modulus and water tightness appear to have direct relationships with the compressive strength. Hence, the compressive strength is commonly used as the main

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Compressive strength of concrete cube test is the most important strength test for concrete. This single test gives an idea about all the characteristics of concrete. Concrete are very strong in compression. It is assumed that whole of the compression will be taken up by the concrete at the time designing any RCC structure.

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