Prediction of compressive strength and portland cement composition using cross-validation and feature ranking techniques

Vakharia, Vinay; Gujar, Rajesh

doi:10.1016/j.conbuildmat.2019.07.224

Cited by 62 publications

(15 citation statements)

References 23 publications

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“…In turn, lower values of MAE and RMSE and MAPE mean that the algorithm predicts the output variables better than the other algorithms. Additionally, to avoid overfitting, tenfold cross-validation is performed according to 38 , as presented in Fig. 5 .…”

Section: Resultsmentioning

confidence: 99%

Design of a machine learning model for the precise manufacturing of green cementitious composites modified with waste granite powder

Czarnecki

Hadzima-Nyarko

Chajec

et al. 2022

Sci Rep

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In this study, a machine learning model for the precise manufacturing of green cementitious composites modified with granite powder sourced from quarry waste was designed. For this purpose, decision tree, random forest and AdaBoost ensemble models were used and compared. A database was created containing 216 sets of data based on an experimental study. The database consists of parameters such as the percentage of cement substituted with granite powder, time of testing and curing conditions. It was shown that this method for designing green cementitious composite mixes, in terms of predicting compressive strength using ensemble models and only three input parameters, can be more accurate and much more precise than the conventional approach. Moreover, to the best of the authors' knowledge, artificial intelligence has been one of the most effective and precise methods used in the design and manufacturing industry in recent decades. The simplicity of this method makes it more suitable for construction practice due to the ease of evaluating the input variables. As the push towards decreasing carbon emissions increases, a method for designing green cementitious composites without producing waste that is more precise than traditional tests performed in a laboratory is essential.

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Section: Resultsmentioning

confidence: 99%

Design of a machine learning model for the precise manufacturing of green cementitious composites modified with waste granite powder

Czarnecki

Hadzima-Nyarko

Chajec

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…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%

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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“…In order to avoid the over-fitting problem during the hyper-parameter optimization, cross-validation methods are needed. Cross-validation can solve the problem of randomly selecting the verification data set and provide random, sustainable and objective model verification method (Vakharia and Gujar, 2019). Leave-one-out cross validation, k -fold cross validation, boot strapping, Monte Carlo test and three ways split test are the common cross-validation methods (Rohani et al, 2018).…”

Section: Machine Learning Algorithms and Their Performance Evaluationmentioning

confidence: 99%

Machine learning based algorithms for wind pressure prediction of high-rise buildings

Huang

et al. 2022

Advances in Structural Engineering

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In recent years, machine learning (ML) techniques have been used in various fields of engineering practice. In order to evaluate the feasibility of machine learning algorithms for prediction of wind-induced effects on high-rise buildings, four ML algorithms including ridge regression, decision tree, random forest and gradient boosting regression tree are adopted in this study to predict wind pressures on Commonwealth Advisory Aeronautical Research Council standard tall building. The gradient boosting regression tree model is proved to be well performed in predicting both mean wind pressures and fluctuating wind pressures. Compared to expensive wind tunnel tests and time-consuming computational fluid dynamic simulations, it is expected that the gradient boosting regression tree model is an efficient and economical alternative for predicting wind pressures on high-rise buildings.

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Prediction of compressive strength and portland cement composition using cross-validation and feature ranking techniques

Cited by 62 publications

References 23 publications

Design of a machine learning model for the precise manufacturing of green cementitious composites modified with waste granite powder

Design of a machine learning model for the precise manufacturing of green cementitious composites modified with waste granite powder

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

Machine learning based algorithms for wind pressure prediction of high-rise buildings

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