Prediction of Concrete Compressive Strength Using Artificial Neural Network

نویسندگان

چکیده

Concrete is the most widely used material by humans after water. Rapid growth in construction industry, concrete will continue to be dominant future. a composite like aggregates, water, and admixtures. Destructive testing of know its strength achieved mix design an expensive time-consuming process. With recent advances soft computing techniques artificial intelligence, these results can predicted feeding algorithm with large number data available obtain desired results. In present research work, it proposed use neural networks predict different types concrete. A Multilayer Perceptron has input output layers, one or more hidden layers many neurons stacked together. Data capturing done regarding are preliminarily trained various inputs solve problems applica- ble This ANN captured helps minimizing repetitive process tests involved through experimental procedures which time, material, money- consuming practical difficulties. The advantage python that designer create customized program for interactive design, Python determination also improve analytical skill student programs converted into executable software. Cubes cast validate Result Software.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Prediction of Pervious Concrete Permeability and Compressive Strength Using Artificial Neural Networks

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...

متن کامل

EVALUATION OF CONCRETE COMPRESSIVE STRENGTH USING ARTIFICIAL NEURAL NETWORK AND MULTIPLE LINEAR REGRESSION MODELS

In the present study, two different data-driven models, artificial neural network (ANN) and multiple linear regression (MLR) models, have been developed to predict the 28 days compressive strength of concrete. Seven different parameters namely 3/4 mm sand, 3/8 mm sand, cement content, gravel, maximums size of aggregate, fineness modulus, and water-cement ratio were considered as input variables...

متن کامل

Prediction of ultimate strength of shale using artificial neural network

A rock failure criterion is very important for prediction of the ultimate strength in rock mechanics and geotechnics; it is determined for rock mechanics studies in mining, civil, and oil wellborn drilling operations. Also shales are among the most difficult to treat formations. Therefore, in this research work, using the artificial neural network (ANN), a model was built to predict the ultimat...

متن کامل

PREDICTION OF COMPRESSIVE STRENGTH AND DURABILITY OF HIGH PERFORMANCE CONCRETE BY ARTIFICIAL NEURAL NETWORKS

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...

متن کامل

prediction of pervious concrete permeability and compressive strength using artificial neural networks

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...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: International Research Journal on Advanced Science Hub

سال: 2022

ISSN: ['2582-4376']

DOI: https://doi.org/10.47392/irjash.2022.069