Prediction of Compressive Strength of Concrete Using

Jul 28,  · Different modeling techniques like multiple linear regression, decision tree, and random forest, etc. will be used for predicting the concrete compressive strength. A comparative analysis will be performed to identify the best model for our prediction in terms of accuracy. The best model will be helpful for civil engineers in choosing the appropriate concrete for bridges, houses construction.

performance concrete by using SF and FA (10%, 30% by weight of cement). The SF concrete showed similar strength development to that of the Ordinary Portland Cement concrete but slight higher values at all tested ages (1, 3, 7, 28, 365 days). FA concrete gave lowest compressive strength at early ages, same at 28 days and higher at 365 days than

5.2 Prediction of Fresh and Hardened Properties of SCC using RA The relationship between the Predicted and Literature Slump Flow Diameter, L-Box ratio, V Funnel Flow Time and Compressive Strength at 28 days by Regression Analysis is depicted in Fig. 9 - 12. The

jats:title>Abstract The use of agricultural wastes as additives for other valuable products has been the focus of

Compressive strength prediction of environmentally friendly concrete using arti cial neural networks", Journal of Building Engineering, 16, pp. 213{219 ( ). 70. Naderpour, H., Vahdani, R., and Mirrashid, M. Soft computing research in structural control by mass damper (A review paper)", 4th International Conference

Javed et al. [22] predict the compressive strength of sug-arcane bagasse ash (SCBA) concrete using gene expression programming (GEP). The author used the experimental test for calibration and validation of the model. Similarly, Aslam et al. [23] predict the compressive strength of high-strength concrete (HSC) by employing GEP.

In the additional information file it was mentioned "The concrete compressive strength is a highly nonlinear function of age and ingredients". For this reason (and for training) I decided to apply a multiple regression model and gam model to predict the strength of the concrete compressive strength.

The conventional process of testing the compressive strength of concrete involves casting several cubes for the respective grade (such as M5, M10, M15 etc.) and 

Aug 07,  · Abstract The mechanical properties of concrete are one of the most important properties in a design code. Accurate prediction models for mechanical properties are always desirable. Prediction of compressive and splitting tensile strength of concrete with fly ash by using gene expression programming

Opara, Chukwuemeka . "PREDICTION OF COMPRESSIVE STRENGTH OF SAW DUST ASH-CEMENT CONCRETE USING ARTIFICIAL NEURAL NETWORK METHOD". Afribary, Afribary, 23 Feb. 2021.Web. 15 Aug. 2021.

In recent decades, artificial neural networks are known as intelligent methods for modeling of behavior of physical phenomena. In this paper, implementation 

20] F. Demir, "Prediction of compressive strength of concrete using ANN and Fuzzy logic", Cement and Concrete Research, 2005, vol. 35, pp. 1531-1538. [21] Z. S_en, "Combining Back propagations and Genetic Algorithms to train to train neural networks for Ambient Temperature Modelling", Solar Energy, 1998 vol. 63 (1), pp. 39-49.

Improvement of compressive strength prediction accuracy for concrete is crucial and is considered a challenging task to reduce costly experiments and time. Particularly, the determination of compressive strength of concrete using ground granulated blast furnace slag (GGBFS) is more difficult due to the complexity of the composition mix design.

concrete compressive strength of concrete from early ages. Proportions of the ingredients and different water cement ratios in estimating the strength at an early age of concrete were investigated. A rapid and reliable concrete strength prediction would be a great significance.

It can be observed that relatively fewer works have been done on compressive strength prediction of RAC wherein most previous studies were particularly centric about high-performance concrete (HPC) containing blast furnace slag (BFS), flay ash (FA) and superplasticizer. Table 1. Summary of existing models.

In this article, we are going to show the formulas which relate the compressive strength of concrete with 

characteristic strength of concrete is defined as the compressive strength of a sample that has been cured for 28 days. However, to hasten the construction progress we must be able to predict the concrete strength based upon the early strength data. Therefore, rapid and reliable prediction

For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, the soft computing techniques known as gene expression programming (GEP) are executed to deliver an empirical equation to estimate the compressive strength of GPC made by employing FA. To build a model, a consistent, extensive and reliable data

Prediction of Compressive Strength of Self-Compacting Concrete (SCC) with Silica Fume Using Neural Networks Models Self-Compacting Concrete (SCC) is a relatively new type of concrete with high workability, high volume of paste and containing cement replacement materials such as slag, natural pozzolana and silica fume.

compressive strength. In this method, compressive strength is indirectly estimated using the penetration of a probe in to the concrete which is charged with explosives. Lesser the depth of penetration of the probe means the higher the compressive strength of concrete (Mallick, 1983; Windsor Probe Test System Inc., 1994).

Abstract: This work aims at developing mathematical model for predicting the compressive strength of sawdust ash - cement concrete based on Osadebe's five comp

This research proposes a new Artificial Intelligence based approach, Artificial Neural Networks (ANNs), to estimate the concrete compressive strength using the UPV and RH test data. Data from a total of 315 cylinder concrete samples are collected to develop and validate the ANFIS prediction model. The model prediction results are compared with

In this study a mathematical model is proposed and developed using multivariate regression equation for the prediction of concrete compressive strength at 

CONCRETE STRENGTH. Predicting the compressive strength of concrete using ML methods and Deep Nueral Networks. EXISTING SOCIETAL ISSUE: In earlier days, the concrete strength is measure through other traditional methods like using drill holes, weight spring, or using sensors.

Cylindrical specimens are tested in accordance with ASTM C-39 (which is standard Test Method for Compressive Strength of Cylindrical Concrete Specimens). A test 

The aim of this study is to evaluate the capability of Artificial Neural Network Model (ANN) in predicting the 28 days compressive strength of concrete.