Effect of the addition and processing of glass polishing waste on the durability of geopolymeric mortars

Azevedo, Afonso Rangel Garcez de; Marvila, Markssuel Teixeira; Ali, Mujahid; Khan, Muhammad Imran; Masood, Faisal; Vieira, Carlos Maurício Fontes

doi:10.1016/j.cscm.2021.e00662

Cited by 49 publications

(18 citation statements)

References 43 publications

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“…The method of producing GPC is represented in Figure 1 , along with the various components and curing regimes employed. Consumption of these waste materials benefits both the environment and the economy, as the demand for inexpensive housing will increase as the population expands [ 31 , 32 , 33 ]. GPC has been the topic of broad research and development on a global scale, and it may one day become the best green construction material [ 34 , 35 , 36 , 37 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites

Wang

Ahmad

et al. 2022

Polymers

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Geopolymers may be the best alternative to ordinary Portland cement because they are manufactured using waste materials enriched in aluminosilicate. Research on geopolymer composites is accelerating. However, considerable work, expense, and time are needed to cast, cure, and test specimens. The application of computational methods to the stated objective is critical for speedy and cost-effective research. In this study, supervised machine learning approaches were employed to predict the compressive strength of geopolymer composites. One individual machine learning approach, decision tree, and two ensembled machine learning approaches, AdaBoost and random forest, were used. The coefficient correlation (R2), statistical tests, and k-fold analysis were used to determine the validity and comparison of all models. It was discovered that ensembled machine learning techniques outperformed individual machine learning techniques in forecasting the compressive strength of geopolymer composites. However, the outcomes of the individual machine learning model were also within the acceptable limit. R2 values of 0.90, 0.90, and 0.83 were obtained for AdaBoost, random forest, and decision models, respectively. The models’ decreased error values, such as mean absolute error, mean absolute percentage error, and root-mean-square errors, further confirmed the ensembled machine learning techniques’ increased precision. Machine learning approaches will aid the building industry by providing quick and cost-effective methods for evaluating material properties.

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Section: Introductionmentioning

confidence: 99%

Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites

Wang

Ahmad

et al. 2022

Polymers

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“…Bayesian models offer the privilege of dealing with extremely complicated models, particularly those with difficult-to-calculate probability functions. On the other hand, standard NN models have been criticized for their inability to fit training data accurately, and they may generate forecasted results with undesirable variances [19][20][21][22]. Overfitting is among the main causes of this issue.…”

Section: Introductionmentioning

confidence: 99%

An Advanced Machine Learning Approach to Predicting Pedestrian Fatality Caused by Road Crashes: A Step toward Sustainable Pedestrian Safety

et al. 2022

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More than 8000 pedestrians were killed due to road crashes in Australia over the last 30 years. Pedestrians are assumed to be the most vulnerable users of roads. This susceptibility of pedestrians to road crashes conflicts with sustainable transportation objectives. It is critical to know the causes of pedestrian injuries in order to enhance the safety of these vulnerable road users. To achieve this, traditional statistical models are used frequently. However, they have been criticized for their inflexibility in handling outliers and missing or noisy data, and their strict pre-assumptions. This study applied an advanced machine learning algorithm, a Bayesian neural network, which has the characters of both Bayesian theory and neural networks. Several structures of this model were built, and the best structure was selected, which included three hidden neuron layers—sixteen hidden nodes in the first layer and eight hidden nodes in the second and third layers. The performance of this model was compared with the performances of some other machine learning techniques, including standard Bayesian networks, a standard neural network, and a random forest model. The Bayesian neural network model outperformed the other models. In addition, a study on the importance of the features showed that the individuals’ characteristics, time, and circumstantial factors were essential. They greatly increased model performance if the model used them. This research lays the groundwork for using machine learning approaches to alleviate pedestrian deaths caused by road accidents.

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“…Different experimental work has been performed to evaluate concrete’s short and long-term mechanical properties by replacing the different fine aggregate or cement levels with SF [ 27 , 28 , 29 ]. The literature suggests that the mechanical properties of SF, including compressive strength, initial strain due to creep, and modulus of elasticity, improved by replacing 15% of SF content.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Machine Learning Techniques

et al. 2021

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Silica fume (SF) is a frequently used mineral admixture in producing sustainable concrete in the construction sector. Incorporating SF as a partial substitution of cement in concrete has obvious advantages, including reduced CO2 emission, cost-effective concrete, enhanced durability, and mechanical properties. Due to ever-increasing environmental concerns, the development of predictive machine learning (ML) models requires time. Therefore, the present study focuses on developing modeling techniques in predicting the compressive strength of silica fume concrete. The employed techniques include decision tree (DT) and support vector machine (SVM). An extensive and reliable database of 283 compressive strengths was established from the available literature information. The six most influential factors, i.e., cement, fine aggregate, coarse aggregate, water, superplasticizer, and silica fume, were considered as significant input parameters. The evaluation of models was performed by different statistical parameters, such as mean absolute error (MAE), root mean squared error (RMSE), root mean squared log error (RMSLE), and coefficient of determination (R2). Individual and ensemble models of DT and SVM showed satisfactory results with high prediction accuracy. Statistical analyses indicated that DT models bested SVM for predicting compressive strength. Ensemble modeling showed an enhancement of 11 percent and 1.5 percent for DT and SVM compressive strength models, respectively, as depicted by statistical parameters. Moreover, sensitivity analyses showed that cement and water are the governing parameters in developing compressive strength. A cross-validation technique was used to avoid overfitting issues and confirm the generalized modeling output. ML algorithms are used to predict SFC compressive strength to promote the use of green concrete.

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Effect of the addition and processing of glass polishing waste on the durability of geopolymeric mortars

Cited by 49 publications

References 43 publications

Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites

Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites

An Advanced Machine Learning Approach to Predicting Pedestrian Fatality Caused by Road Crashes: A Step toward Sustainable Pedestrian Safety

Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Machine Learning Techniques

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