Application of New Information Technology on Concrete: An Overview / Naujų Informacinių Technologijų Naudojimas Ruošiant Betoną. Apžvalga

Boukhatem, Bakhta; Kenai, Said; Tagnit-Hamou, Arezki; Ghrici, Mohamed

doi:10.3846/13923730.2011.574343

Cited by 29 publications

(5 citation statements)

References 56 publications

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“…In recent years, the ML methods have become popular as they allow researchers to improve the prediction accuracy of concrete properties [4] and are used for various engineering applications [5,6]. e ML methods have been used to increase the prediction accuracy of concrete properties [7][8][9][10][11][12][13][14][15], and the data derived from the literature sources were used.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods

Cihan

2019

Advances in Civil Engineering

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Machine learning methods have been successfully applied to many engineering disciplines. Prediction of the concrete compressive strength (fc) and slump (S) is important in terms of the desirability of concrete and its sustainability. The goals of this study were (i) to determine the most successful normalization technique for the datasets, (ii) to select the prime regression method to predict the fc and S outputs, (iii) to obtain the best subset with the ReliefF feature selection method, and (iv) to compare the regression results for the original and selected subsets. Experimental results demonstrate that the decimal scaling and min-max normalization techniques are the most successful methods for predicting the compressive strength and slump outputs, respectively. According to the evaluation metrics, such as the correlation coefficient, root mean squared error, and mean absolute error, the fuzzy logic method makes better predictions than any other regression method. Moreover, when the input variable was reduced from seven to four by the ReliefF feature selection method, the predicted accuracy was within the acceptable error rate.

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Section: Introductionmentioning

confidence: 99%

Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods

Cihan

2019

Advances in Civil Engineering

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“…The development of computer integrated information system, raises the development of software for the concrete mix design. These systems are expected to greatly affect construction design over the past decade and have revolutionary effects on future consumption of natural resources and building material designs (Boukhatem, Kenai, Tagnit-Hamou, & Ghrici, 2011).…”

Section: Fuzzy Logicmentioning

confidence: 99%

Modeling a rheological cement mixture suitable for 3D printing technology with Fuzzy Logic

Küçük¹,

Pakdamar²,

Ipekçi³

2022

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The material preferred in additive manufacturing technology 3D printing, which has advanced in current years and has been utilized in the construction sector, is a cement-based mixture. Considering the experimental studies for this mixture, which does not have a definite formulation, the success of the resulting products shows the rheological properties that enable the material to be built. Data on the constructability of the material can be obtained by defining the volume ratios of the resulting samples with the Fuzzy Logic method. For the constructability of the material, a Mamdani type fuzzy model was created and the "CENTROID center of gravity" method was stipulated as the clarification method. While the component material ratios of the cementitious mixture create the fuzzy inputs of the system, static and dynamic yield stresses are reached as fuzzy output values. 15 rules obtained from experimental studies that enable the system to work were defined and then the operation of the method was tested. In this study, it is aimed to develop a decision mechanism for intermediate values that are not encountered in the experimental results by blurring the mixture models supported with definite findings.

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“…Concrete has cost and durability advantages compared with other construction materials; it is one of the most widely used building materials in China (Boukhatem et al, 2011). Durability is one of the most important properties of concrete during its use.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Prediction of the Frost Resistance of High-Performance Concrete: A Machine Learning Method

Zhang,

Cao,

Xia

et al. 2023

Journal of Civil Engineering and Management

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Frost resistance in very cold areas is an important engineering issue for the durability of concrete, and the efficient and accurate prediction of the frost resistance of concrete is a crucial basis for determining reasonable design mix proportions. For a quick and accurate prediction of the frost resistance of concrete, a Bayesian optimization (BO)-random forest (RF) approach was used to establish a frost resistance prediction model that consists of three phases. A case study of a key national engineering project results show that (1) the RF can be used to effectively screen the factors that influence concrete frost resistance. (2) R2 of BO-RF for the training set and the test set are 0.967 and 0.959, respectively, which are better than those of the other algorithms. (3) Using the test data from the first section of the project for prediction, good results are obtained for the second section. The proposed BO-RF hybrid algorithm can accurately and quickly predict the frost resistance of concrete, and provide a reference basis for intelligent prediction of concrete durability.

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Application of New Information Technology on Concrete: An Overview / Naujų Informacinių Technologijų Naudojimas Ruošiant Betoną. Apžvalga

Cited by 29 publications

References 56 publications

Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods

Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods

Modeling a rheological cement mixture suitable for 3D printing technology with Fuzzy Logic

Intelligent Prediction of the Frost Resistance of High-Performance Concrete: A Machine Learning Method

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