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Here, data from various concrete mixing plants and ongoing construction sites was collected for M20, M25, M30, M35, M40, M45, M50, M55, M60 and

Inquire nowMultiple Linear Regression (MLR) and Artificial Neural Network (ANN) models were built to predict slump as well as 7-days and 28-days compressive strength. A variety of experiments was carried out that suggests ANN performs better and yields more accurate prediction compared to MLR model for both slump compressive strength. 1.

Inquire nowMar 18, 2018 Request PDF Prediction of Concrete Properties Using Multiple Linear Regression and Artificial Neural Network The selection of appropriate type and grade of concrete for a particular ...

Inquire nowOct 20, 2021 The purpose of this study is to apply multiple linear regressions (MLRs) and arti ﬁcial neural network (ANN) to predict the mechanical properties, such as compressive strength (CS), ﬂexural...

Inquire nowOct 20, 2021 The purpose of this study is to apply multiple linear regressions (MLRs) and artificial neural network (ANN) to predict the mechanical properties, such as compressive strength (CS), flexural strength (FS) and split tensile strength (STS) of concrete at the age of 28 days curing made completely from the recycled coarse aggregate (RCA).

Inquire nowApr 09, 2021 Also, DoE is more frequently used in the prediction of concrete mechanical properties, but other applications, such as density, absorption and cost optimization are also available. Regression analysis is the simplest analytical method, which is employed to understand the relationship between variables.

Inquire nowApr 01, 2022 Artificial Neural Network (ANN)-based ML model to predict the compressive strength of High-Performance concrete was initially demonstrated by Yeh [ 30] in 1998. It was shown that the ANN model provides better prediction as compared to a model based on regression analysis.

Inquire nowJan 01, 2009 The role of concrete dimensionless variables in CCS prediction has been expressed in the success of their nonlinear model. In a subsequent study [26], the role of multilinear regression in...

Inquire nowJul 04, 2022 Using the results of the SPH analysis, artificial neural network (ANN) was constructed to consider the uncertainties for the prediction of amount of fragments and travel distance of concrete after ...

Inquire nowThe selection of appropriate type and grade of concrete for a particular application is the critical step in any construction project. Workability compressive strength are the two significant parameters that need special attention. ... Prediction of Concrete Properties Using Multiple Linear Regression and Artificial Neural Network. 2018 ...

Inquire nowJul 12, 2017 High-quality models are often non-linear, justifying the study of nonlinear regression tools. In this paper, we employ a traditional multiple linear regression method by ordinary least squares to solve the task. However, the model is built upon nonlinear features automatically engineered by Kaizen Programming, a recently proposed hybrid method.

Inquire nowApr 09, 2021 1. Introduction. Design of Experiment (DoE) is an effective tool for handling multiple variables in problem solving [].The method has been used to improve experimentation performance in engineering, services, and manufacturing industries [].Traditionally, problems with multiple variables are solved using the “one variable at a time” (OVAT) approach, which

Inquire nowThe purpose of this study is to apply multiple linear regressions (MLRs) and artificial neural network (ANN) to predict the mechanical properties, such as compressive strength (CS), flexural strength (FS) and split tensile strength (STS) of concrete at the age of 28 days curing made completely from the recycled coarse aggregate (RCA).

Inquire nowDownload Table Fitting results using multiple linear stepwise regressions for concrete properties from publication: Concrete properties prediction based on database

Inquire nowto its compressive strength results in only modest strength prediction (R2 =0.80). Using a multiple parameter regression model (augmented Abrams equation) approach, significantly improved strength prediction (R2 =0.98) was achieved. INTRODUCTION Concrete has an extremely versatile use in construction due to its availability, flexibility of

Inquire nowJul 04, 2022 Using the results of the SPH analysis, artificial neural network (ANN) was constructed to consider the uncertainties for the prediction of amount of fragments and travel distance of concrete after ...

Inquire nowApr 01, 2022 Prediction of concrete strengths using XGBoost tree ensemble. In this section, the prediction of the compressive and tensile strength of the concrete is presented when the k-NN (k = 10) imputation method is used, and the hyperparameter-optimized XGBoost, tree ensemble model, is implemented.

Inquire nowA multiple linear regression prediction of concrete ... of the 28-day concrete using the multi-linear ... relationship of parameters and concrete properties caused researchers to

Inquire nowDec 17, 2019 Hence, the present study focuses on binary blended ambient-cured AAB mix and its shrinkage properties. The objective is to use multiple linear regression to generate a relatively simple model to predict the shrinkage strain of AAB concrete by using age of the specimen and percentage of fly ash in the mix as independent variables (IN) and ...

Inquire nowMar 01, 2017 Sadowski L, Nikoo M. Corrosion current density prediction in reinforced concrete by imperialist competitive algorithm. Neural Computing Applications, 2014, 25(7–8): 1627–1638. Google Scholar Khademi F, Behfarnia K. Evaluation of concrete compressive strength using artificial neural network and multiple linear regression models.

Inquire nowThe present study is to compare the multiple regression analysis (MRA) model and artificial neural network (ANN) model designed to predict the mechanical strength of fiber-reinforced concrete on 28#x2009;days. The model uses the data from early literatures; the data consist of tensile strength of fiber, percentage of fiber, water/cement ratio, cross-sectional area of test

Inquire nowThe use of ANN technique in the prediction of concrete mix proportions can be efficient and economical as it would reduce the need of preparing a large number of trial mixes. The learning processes in artificial neural networks use previous experimental mix design data to predict mix proportions specified by various input parameters.

Inquire nowConcrete production by replacing cement with green materials has been conducted in recent years considering the strategy of sustainable development. This study researched the topic of compressive strength regarding one type of green concrete containing blast furnace slag. Although some researchers have proposed using machine learning models to predict the

Inquire nowHere, data from various concrete mixing plants and ongoing construction sites was collected for M20, M25, M30, M35, M40, M45, M50, M55, M60 and M70 grade of concrete. Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models were built to predict slump as well as 7-days and 28-days compressive strength.

Inquire nowJul 12, 2017 High-quality models are often non-linear, justifying the study of nonlinear regression tools. In this paper, we employ a traditional multiple linear regression method by ordinary least squares to solve the task. However, the model is built upon nonlinear features automatically engineered by Kaizen Programming, a recently proposed hybrid method.

Inquire nowThe concrete element is modeled using a solid 3-dimensional element in order to display the non-linear behavior of the concrete. The mesh that has been used for the concrete is C3D8R which stand for an 8 node linear brick with reduced integration. The mesh size 10 is being used. Figure 3 shows types of elements,

Inquire nowA Multiple Linear Regression Prediction of Concrete Compressive Strength Based on Physical Properties of Electric Arc Furnace Oxidizing Slag @inproceedings{Chen2010AML, title={A Multiple Linear Regression Prediction of Concrete Compressive Strength Based on Physical Properties of Electric Arc Furnace Oxidizing Slag}, author={Li Chen and Shungo ...

Inquire nowJun 07, 2021 A new model based on multiple linear regression analysis of the data from this study and other 14 studies from the literature was developed. The model can be used to predict the compressive strength of general-use concrete mixes with recycled aggregate (20–40 MPa) considering both the recycled aggregate content and the curing age of concrete.

Inquire nowDec 01, 2016 The present study proposes three different data-driven models, i.e., Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR) models to predict the 28 days compressive strength of concrete using 14 different input variables. In addition, the performance of data-driven models with and ...

Inquire nowJul 04, 2022 Using the results of the SPH analysis, artificial neural network (ANN) was constructed to consider the uncertainties for the prediction of amount of fragments and travel distance of concrete after ...

Inquire nowJan 20, 2022 Deepa et al. conducted a study for the prediction of the compressive strength of high-performance concrete mix using tree based modeling such as random forests, random tree and M5 modeling techniques. To predict the compressive strength of concrete, Atici had applied multiple regression analysis and ANN techniques. The results of the study ...

Inquire nowSep 15, 2016 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

Inquire nowT1 - Prediction of Mechanical Properties of Concrete Made with Recycled Concrete Aggregates Using Statistical Analysis of Data Available in Literature. AU - Jayasuriya, Anuruddha. AU - Chen, Tola. AU - Shibata, Emily. AU - Adams, Matthew P. PY - 2020/1/1. Y1 -

Inquire nowMay 01, 2022 The PU-based polymer concrete samples was obtained by mixing natural sand and PU matrix using 85:15 and 90:10 ratios, and normal concrete samples were

Inquire nowAug 01, 2020 A valid global data set is collected from the WHO daily statistics and correlation among the total confirmed, active, deceased, positive cases are stated in this paper. Regression model such as Linear and Multiple Linear Regression techniques are applied to the data set to visualize the trend of the affected cases.

Inquire nowConcrete production by replacing cement with green materials has been conducted in recent years considering the strategy of sustainable development. This study researched the topic of compressive strength regarding one type of green concrete containing blast furnace slag. Although some researchers have proposed using machine learning models to predict the

Inquire nowSep 28, 2021 1.2. Artificial Neural Networks. Machine learning belongs to the field of artificial intelligence, and it is used for prediction, i.e., classification that represents the prediction of the categorical value, or regression, which is the prediction of the numerical value [].Certain studies in concrete mix design that used ML were focused on modeling mixtures with particular

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