Modelling and Prediction of Concrete Compressive Strength Using Machine Learning
نویسندگان
چکیده
منابع مشابه
Proposal Prediction on the Concrete Compressive Strength Using Supervised Learning
The high performance concrete is a highly complex material that consists of cement, water, blast furnace slag, super-plasticizer, coarse and fine aggregate. All of the materials play a certain role in the compressive strength of concrete. Combined with the information about age, the compressive strength of concrete is determined by eight attributes: 1. Cement (kg/m!) 2. Fly ash (kg/m!) 3. Blast...
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Nowadays, the better performance of lightweight structures during earthquake has resulted in using lightweight concrete more than ever. However, determining the compressive strength of concrete used in these structures during their service through a none-destructive test is a popular and useful method. One of the main methods of non-destructive testing in the assessment of compressive strength...
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Pervious concrete is a concrete mixture prepared from cement, aggregates, water, little or no fines, and in some cases admixtures. The hydrological property of pervious concrete is the primary reason for its reappearance in construction. Much research has been conducted on plain concrete, but little attention has been paid to porous concrete, particularly to the analytical prediction modeling o...
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Artificial neural networks (ANNs) as a powerful approach have been widely utilized to demonstrate some of the engineering problems. A three-layer ANN including three neurons in the hidden layer is considered to produce a verified pattern for assessing the compressive strength of concrete incorporating metakaolin (MK). For this purpose, an extensive database including 469 experimental specimens ...
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Neural networks have recently been widely used to model some of the human activities in many areas of civil engineering applications. In the present paper, artificial neural networks (ANN) for predicting compressive strength of cubes and durability of concrete containing metakaolin with fly ash and silica fume with fly ash are developed at the age of 3, 7, 28, 56 and 90 days. For building these...
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ژورنال
عنوان ژورنال: International Journal of Scientific Research in Computer Science, Engineering and Information Technology
سال: 2021
ISSN: 2456-3307
DOI: 10.32628/cseit217385