Development of Prediction Model to Predict the Compressive Strength of Eco-Friendly Concrete Using Multivariate Polynomial Regression Combined with Stepwise Method

Imran, Hamza; Al-Abdaly, Nadia Moneem; Shamsa, Mohammed Hammodi; Shatnawi, Amjed; Ibrahim, Majed; Ostrowski, Krzysztof

doi:10.3390/ma15010317

Cited by 32 publications

(7 citation statements)

References 30 publications

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“…Machine learning (ML) methods have advanced quickly in recent years, and their theories and techniques have been widely utilized to tackle challenging issues in a variety of engineering and scientific domains [2][3][4][5][6][7][8]. Researchers have been driven to apply ANN models and optimization methods to address a variety of civil engineering issues due to the growth of ML techniques [9].…”

Section: Introductionmentioning

confidence: 99%

Carbon Emission Optimization of Ultra-High-Performance Concrete Using Machine Learning Methods

Wang,

Du,

Jia

et al. 2024

Materials

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Due to its exceptional qualities, ultra-high-performance concrete (UHPC) has recently become one of the hottest research areas, although the material’s significant carbon emissions go against the current development trend. In order to lower the carbon emissions of UHPC, this study suggests a machine learning-based strategy for optimizing the mix proportion of UHPC. To accomplish this, an artificial neural network (ANN) is initially applied to develop a prediction model for the compressive strength and slump flow of UHPC. Then, a genetic algorithm (GA) is employed to reduce the carbon emissions of UHPC while taking into account the strength, slump flow, component content, component proportion, and absolute volume of UHPC as constraint conditions. The outcome is then supported by the results of the experiments. In comparison to the experimental results, the research findings show that the ANN model has excellent prediction accuracy with an error of less than 10%. The carbon emissions of UHPC are decreased to 688 kg/m3 after GA optimization, and the effect of optimization is substantial. The machine learning (ML) model can provide theoretical support for the optimization of various aspects of UHPC.

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Section: Introductionmentioning

confidence: 99%

Carbon Emission Optimization of Ultra-High-Performance Concrete Using Machine Learning Methods

Wang,

Du,

Jia

et al. 2024

Materials

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“…The ANFIS model was found to be highly accurate in predicting the strength of the lightweight concrete. Imran et al (10) developed an environmental-friendly concrete https://www.indjst.org/ by substituting the traditional coarse aggregate by RAC (recycled aggregate concrete) in volume proportions of 0%, 25%, 50%, 75%, 100% and cement by GGBFS (ground granulated blast-furnace slag) in volume proportions of 0%, 20%, 40%, 60%, 80%. The performance of the developed concrete was predicted using a white-box machine learning model called multivariate polynomial regression.…”

Section: Introductionmentioning

confidence: 99%

Material Performance Evaluation of Cinder Based Light Weight Concrete with Micro-reinforcement

Sadhana,

Suguna,

Raghunath

2024

IJST

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Objectives: The present work examines the viability of using industrial solid waste, like cinder, to create lightweight aggregate concrete. Utilizing cinder aggregate as a substitute material for coarse aggregate in producing concrete not only helps in the conservation of natural resource but also provides a feasible solution for solid waste management. Method: The mechanical characteristics of cinder based lightweight concrete were improved by the inclusion of polypropylene fibre as micro-reinforcement, in volume proportions of 0.1%, 0.2%, 0.3%, and 0.4%. The laboratory tests that were conducted on the polypropylene fibre reinforced cinder based lightweight concrete included compressive strength test on cube specimen and cylinder specimen, flexural strength test on prism specimen and modulus of elasticity test on cylinder specimen. Findings: The findings show that cinder aggregate can be satisfactorily utilized as an alternate material to coarse aggregate in the production of lightweight concrete. It has been found that 0.3% of polypropylene fibre was the ideal dosage of micro-reinforcement that can be incorporated in lightweight concrete. Novelty: Further, a multiple linear regression (MLR) model was suggested to evaluate the performance parameters of the fibre-reinforced lightweight concrete, providing an alternative to the time and material consuming experimental works. Scatter plots and Statistical indicators such as R2, RMSE and MAPE indicated that the model demonstrated a strong correlation between the predicted values and the experimental results. Keywords: Cinder aggregate, Lightweight concrete, Mechanical characteristics, Micro reinforcement, Regression model

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“…Other works [18,19] confirmed the feasibility of using linear models when analyzing the strength characteristics of concrete at different ages, taking into account a variety of both external and internal factors. Metric methods, based on the assumption that the properties of an object can be learned by having an idea of its neighbors, have almost no learning phase (lazy learning), but at the same time they have good predictive ability [20,21]. Different works [22][23][24] showed models using one of the most famous metric algorithms-the k-nearest neighbors (KNN) method.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Properties of Vibro-Centrifuged Variatropic Concrete in Aggressive Environments Using Machine Learning Methods

Beskopylny,

Stel’makh,

Shcherban’

et al. 2024

Buildings

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In recent years, one of the most promising areas in modern concrete science and the technology of reinforced concrete structures is the technology of vibro-centrifugation of concrete, which makes it possible to obtain reinforced concrete elements with a variatropic structure. However, this area is poorly studied and there is a serious deficiency in both scientific and practical terms, expressed in the absence of a systematic knowledge of the life cycle management processes of vibro-centrifuged variatropic concrete. Artificial intelligence methods are seen as one of the most promising methods for improving the process of managing the life cycle of such concrete in reinforced concrete structures. The purpose of the study is to develop and compare machine learning algorithms based on ridge regression, decision tree and extreme gradient boosting (XGBoost) for predicting the compressive strength of vibro-centrifuged variatropic concrete using a database of experimental values obtained under laboratory conditions. As a result of laboratory tests, a dataset of 664 samples was generated, describing the influence of aggressive environmental factors (freezing–thawing, chloride content, sulfate content and number of wetting–drying cycles) on the final strength characteristics of concrete. The use of analytical techniques to extract additional knowledge from data contributed to improving the resulting predictive properties of machine learning models. As a result, the average absolute percentage error (MAPE) for the best XGBoost algorithm was 2.72%, mean absolute error (MAE) = 1.134627, mean squared error (MSE) = 4.801390, root-mean-square error (RMSE) = 2.191208 and R2 = 0.93, which allows to conclude that it is possible to use “smart” algorithms to improve the life cycle management process of vibro-centrifuged variatropic concrete, by reducing the time required for the compressive strength assessment of new structures.

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Development of Prediction Model to Predict the Compressive Strength of Eco-Friendly Concrete Using Multivariate Polynomial Regression Combined with Stepwise Method

Cited by 32 publications

References 30 publications

Carbon Emission Optimization of Ultra-High-Performance Concrete Using Machine Learning Methods

Carbon Emission Optimization of Ultra-High-Performance Concrete Using Machine Learning Methods

Material Performance Evaluation of Cinder Based Light Weight Concrete with Micro-reinforcement

Prediction of the Properties of Vibro-Centrifuged Variatropic Concrete in Aggressive Environments Using Machine Learning Methods

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