Simulation of the Compressive Strength of Cemented Tailing Backfill through the Use of Firefly Algorithm and Random Forest Model

Wang, Qiang; Jia, Zhang; Huang, Jiandong

doi:10.1155/2021/5536998

Cited by 16 publications

(10 citation statements)

References 34 publications

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“…Integration. FA is a nature-inspired heuristic methodology developed by Yang in 2008 [37]. Due to its merit and optimization accuracy, it is widely adopted by researchers to Computational Intelligence and Neuroscience fnd solutions for a number of optimization problems [38][39][40][41][42][43].…”

Section: Firefy Algorithm-based Feature Reduction Andmentioning

confidence: 99%

Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection

Krishnamoorthy

Zhang

Kadry

et al. 2023

Computational Intelligence and Neuroscience

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Malfunctions in the immune system cause multiple sclerosis (MS), which initiates mild to severe nerve damage. MS will disturb the signal communication between the brain and other body parts, and early diagnosis will help reduce the harshness of MS in humankind. Magnetic resonance imaging (MRI) supported MS detection is a standard clinical procedure in which the bio-image recorded with a chosen modality is considered to assess the severity of the disease. The proposed research aims to implement a convolutional neural network (CNN) supported scheme to detect MS lesions in the chosen brain MRI slices. The stages of this framework include (i) image collection and resizing, (ii) deep feature mining, (iii) hand-crafted feature mining, (iii) feature optimization with firefly algorithm, and (iv) serial feature integration and classification. In this work, five-fold cross-validation is executed, and the final result is considered for the assessment. The brain MRI slices with/without the skull section are examined separately, presenting the attained results. The experimental outcome of this study confirms that the VGG16 with random forest (RF) classifier offered a classification accuracy of >98% MRI with skull, and VGG16 with K-nearest neighbor (KNN) provided an accuracy of >98% without the skull.

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Section: Firefy Algorithm-based Feature Reduction Andmentioning

confidence: 99%

Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection

Krishnamoorthy

Zhang

Kadry

et al. 2023

Computational Intelligence and Neuroscience

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“…The large amount of carbon dioxide emitted in the process of cement production is a serious burden and threat to the environment [ 5 , 6 , 7 ]. Finding new materials to replace some of the cement used in concrete is a necessary way to ensure the sustainable development of the concrete industry [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. At present, the use of fly ash, blast furnace slag, metakaolin, and other mineral admixtures to replace part of the cement used in concrete is the main solution to alleviate the large resource consumption and negative impact on the environment in the cement production process [ 17 , 18 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Design of Building Materials: Development of an AI-Based Method for Cement-Slag Concrete Design

Zhu

Zhou

et al. 2022

Materials

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Cement-slag concrete has become one of the most widely used building materials considering its economical advantage and satisfying uniaxial compressive strength (UCS). In this study, an AI-based method for cement-slag concrete design was developed based on the balance of economic and mechanical properties. Firstly, the hyperparameters of random forest (RF), decision tree (DT), and support vector machine (SVM) were tuned by the beetle antennae search algorithm (BAS). The results of the model evaluation showed the RF with the best prediction effect on the UCS of concrete was selected as the objective function of UCS optimization. Afterward, the objective function of concrete cost optimization was established according to the linear relationship between concrete cost and each mixture. The obtained results showed that the weighted method can be used to construct the multi-objective optimization function of UCS and cost for cement-slag concrete, which is solved by the multi-objective beetle antennae search (MOBAS) algorithm. An optimal concrete mixture ratio can be obtained by Technique for Order Preference by Similarity to Ideal Solution. Considering the current global environment trend of “Net Carbon Zero”, the multi-objective optimization design should be proposed based on the objectives of economy-carbon emission-mechanical properties for future studies.

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“…The above machine methods achieved good results in the performance prediction of cement-based materials, and machine learning methods were widely used in the prediction of cement-based materials [ 33 , 34 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 ]. Machine learning technology has been widely used in the cement-based materials performance evaluation process, but these methods still have some limitations, such as uncertainty, time-consuming, and low efficiency [ 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 ]. Therefore, it is necessary to propose a more efficient and simple machine learning technology to predict the compressive strength of metakaolin cement-based materials.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Compressive Strength for Cement-Based Materials with Metakaolin Based on the Hybrid Machine Learning Method

et al. 2022

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Cement-based materials are widely used in construction engineering because of their excellent properties. With the continuous improvement of the functional requirements of building infrastructure, the performance requirements of cement-based materials are becoming higher and higher. As an important property of cement-based materials, compressive strength is of great significance to its research. In this study, a Random Forests (RF) and Firefly Algorithm (FA) hybrid machine learning model was proposed to predict the compressive strength of metakaolin cement-based materials. The database containing five input parameters (cement grade, water to binder ratio, cement-sand ratio, metakaolin to binder ratio, and superplasticizer) based on 361 samples was employed for the prediction. In this model, FA was used to optimize the hyperparameters, and RF was used to predict the compressive strength of metakaolin cement-based materials. The reliability of the hybrid model was verified by comparing the predicted and actual values of the dataset. The importance of five variables was also evaluated, and the results showed the cement grade has the greatest influence on the compressive strength of metakaolin cement-based materials, followed by the water-binder ratio.

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Simulation of the Compressive Strength of Cemented Tailing Backfill through the Use of Firefly Algorithm and Random Forest Model

Cited by 16 publications

References 34 publications

Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection

Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection

Intelligent Design of Building Materials: Development of an AI-Based Method for Cement-Slag Concrete Design

Prediction of the Compressive Strength for Cement-Based Materials with Metakaolin Based on the Hybrid Machine Learning Method

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