Corrigendum to “Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods”

Cihan, M. Timur

doi:10.1155/2020/8201734

Cited by 4 publications

(9 citation statements)

References 1 publication

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“…MLR-Multiple linear regression [3][4][5][6][7][8][9][10][11][12][13] SVM-Support vector machine [2,6,[13][14][15][16][17][18] ANFIS-Adaptive neuro-fuzzy inference system [5,10,11,[18][19][20] FL-Fuzzy logic [2,21,22] RF-Random forest [2,17,23] DT-Decision tree [2,15,23] GP-genetic programming [18,24,25] M5PMT-M5P Model tree [9,26,27] Salp swarm algorithm [27,28] CART-Classification and regression tree [12] Artificial neural networks are computational structures that are trained to learn patterns from examples. The development of ANNs is inspired by the human brain, a biological neural network functioning based on communication between neurons.…”

Section: Prediction Methods Referencementioning

confidence: 99%

“…Disadvantages are sensitivity to dataset, iterative process of determining the optimal structure, and hardware dependence [29]. ANNs are used in concrete mix design to predict optimal mix proportions or properties such as compressive and tensile strength [25,[34][35][36][37], modulus of elasticity [38], slump [2,[39][40][41], drying shrinkage [42], etc.…”

Section: Prediction Methods Referencementioning

confidence: 99%

“…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 [2]. Certain studies in concrete mix design that used ML were focused on modeling mixtures with particular ingredients, generating models that are predicting the compressive strength.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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Application of Artificial Neural Networks for Prediction of Mechanical Properties of CNT/CNF Reinforced Concrete

Kekez

Kubica

2021

Materials

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Prominence of concrete is characterized by its high mechanical properties and durability, combined with multifunctionality and aesthetic appeal. Development of alternative eco-friendly or multipurpose materials has conditioned improvements in concrete mix design to optimize concrete production speed and price, as well as carbon footprint. Artificial neural networks represent a new and efficient tool in achieving optimal concrete mixtures according to its intended function. This paper addresses concrete mix design and the application of artificial neural networks (ANNs) for self-sensing concrete. The authors review concrete mix design methods and the development of ANNs for prediction of properties for various types of concrete. Furthermore, the authors present developments and applications of ANNs for prediction of compressive strength and flexural strength of carbon nanotubes/carbon nanofibers (CNT/CNF) reinforced concrete using experimental results for the learning process. The goal is to bring the ANN approach closer to a variety of concrete researchers and possibly propose the implementation of ANNs in the civil engineering practice.

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Section: Prediction Methods Referencementioning

confidence: 99%

Section: Prediction Methods Referencementioning

confidence: 99%

Section: Artificial Neural Networkmentioning

confidence: 99%

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Application of Artificial Neural Networks for Prediction of Mechanical Properties of CNT/CNF Reinforced Concrete

Kekez

Kubica

2021

Materials

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“…However, the concept was not widely used until the development of information technology, which allowed its reopening and further deployment [5]. Currently, artificial neural networks (ANNs) serve for classification, i.e., the prediction of a categorical value, or regression, i.e., the prediction of a numerical value [6]. The basic concept of ANNs is grounded in the learning of patterns from the presented examples in a supervised or unsupervised manner, in other words, with or without the target values, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…The most used learning algorithm is the feed-forward backpropagation (BP) algorithm because of its simplicity and applicability. The BP algorithm is based on the "backpropagation learning rule" which was established in 1985 as a solution for issues occurring in single-layer or bilayer networks [6]. It represents a generalization of the delta rule and functions as a gradient descent technique of error minimization by incremental adjustment of the connection weight between the layers of a multilayer perceptron (MLP) [7].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Bonding Strength of Externally Bonded SRP Composites Using Artificial Neural Networks

Kekez

Krzywoń

2022

Materials

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External bonding of fiber reinforced composites is currently the most popular method of strengthening building structures. Debonding performance is critical to the effectiveness of such strengthening. Many models of bond prediction can be found in the literature. Most of them were developed based on laboratory research, therefore, their accuracy with less popular strengthening systems is limited. This manuscript presents the possibility of using a model based on neural networks to analyze and predict the debonding strength of steel-reinforced polymer (SRP) and steel-reinforced grout (SRG) composites to concrete. The model is built on the basis of laboratory testing of 328 samples obtained from the literature. The results are compared with a dozen of the most popular analytical methods for predicting the load capacity. The prediction accuracy in the neural network model is by far the best. The total correlation coefficient reaches a value of 0.913 while, for the best analytical method (Swiss standard SIA 166 model), it is 0.756. The sensitivity analysis confirmed the importance of the modulus of elasticity and the concrete strength for debonding. It is also interesting that the width of the element proved to be very important, which is probably related to the low variability of this parameter in the laboratory tests.

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Machine-Learning-Algorithm to predict the High-Performance concrete compressive strength using multiple data

Kamath

Prashanth

Kumar

et al. 2022

JEDT

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Purpose The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength development. This study aims to predict the compressive strength of normal concrete and high-performance concrete using four datasets. Design/methodology/approach In this paper, five established individual Machine Learning (ML) regression models have been compared: Decision Regression Tree, Random Forest Regression, Lasso Regression, Ridge Regression and Multiple-Linear regression. Four datasets were studied, two of which are previous research datasets, and two datasets are from the sophisticated lab using five established individual ML regression models. Findings The five statistical indicators like coefficient of determination (R2), mean absolute error, root mean squared error, Nash–Sutcliffe efficiency and mean absolute percentage error have been used to compare the performance of the models. The models are further compared using statistical indicators with previous studies. Lastly, to understand the variable effect of the predictor, the sensitivity and parametric analysis were carried out to find the performance of the variable. Originality/value The findings of this paper will allow readers to understand the factors involved in identifying the machine learning models and concrete datasets. In so doing, we hope that this research advances the toolset needed to predict compressive strength.

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Corrigendum to “Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods”

Cited by 4 publications

References 1 publication

Application of Artificial Neural Networks for Prediction of Mechanical Properties of CNT/CNF Reinforced Concrete

Application of Artificial Neural Networks for Prediction of Mechanical Properties of CNT/CNF Reinforced Concrete

Prediction of Bonding Strength of Externally Bonded SRP Composites Using Artificial Neural Networks

Machine-Learning-Algorithm to predict the High-Performance concrete compressive strength using multiple data

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