Orthogonal tests investigation into hybrid fiber-reinforce recycled aggregate concrete and convolutional neural network prediction

Huang, Wei; Quan, Wenli; Ge, Pei

doi:10.1080/13467581.2021.1918553

Cited by 8 publications

(4 citation statements)

References 39 publications

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“…When there are many test groups in the whole experiment, selecting representative points from the whole test according to the orthogonality can effectively reduce the number of tests, and the orthogonal experiment is dispersibility and neat comparability. The range and variance was applied to analyze the experimental data; by analyzing the degree of influence of variables on the research object, the best conditions or the best combination can be used to achieve the experimental goals [24][25][26]. In order to study the influence rule of basalt fiber volume rate-V BF (factor A), PVA fiber volume rate-V PF (factor B) and rubber volume rate-V R (factor C) on the compressive strength, splitting tensile strength and flexural strength of HFRC, the L9 (3 3 ) orthogonal experiment scheme was selected, with each factor taking three levels.…”

Section: Experimental Designmentioning

confidence: 99%

Mechanical Properties of Hybrid Fiber Reinforced Rubber Concrete

Yong-dong

2021

Materials

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Orthogonal experiments were designed for hybrid fiber rubber concrete (HFRC). The mechanical properties of HFRC were tested and compared with ordinary concrete. The effects of basalt fiber volume ratio (VBF), PVA fiber volume ratio (VPF) and rubber volume ratio (VR) on the compressive strength, splitting tensile strength and flexural strength of HFRC were analyzed. The results show that the strength of HFRC is the best when the volume ratio of basalt fiber is 0.3%, the volume ratio of PVA fiber is 0.2% and the volume ratio of rubber is 5%. Basalt fiber has the greatest influence on the strength of HFRC. The strength of HFRC mixed with hybrid fiber is greatly improved, which reflects the good fiber “positive hybrid effect”. With the increase of rubber volume ratio, the strength of HFRC decreases gradually. With the help of SEM and EDS, the toughening and cracking resistance mechanism of the fiber to HFRC was analyzed. Finally, the strength of HFRC was predicted by model.

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Section: Experimental Designmentioning

confidence: 99%

Mechanical Properties of Hybrid Fiber Reinforced Rubber Concrete

Yong-dong

2021

Materials

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“…Additionally, other machine learning methods, besides BP neural networks, have demonstrated a favorable trend in predicting the 28-day compressive strength of concrete [38][39][40][41][42]. With the continuous advancement of deep learning, models for predicting the compressive strength of concrete established using CNNs and improved convolutional neural networks exhibit superior predictive performance compared to traditional machine learning methods [43][44][45][46]. This provides novel insights and methods for researching the prediction of compressive strength in pervious concrete.…”

Section: Introductionmentioning

confidence: 99%

The Prediction of Pervious Concrete Compressive Strength Based on a Convolutional Neural Network

Yu,

Zhu,

Xiang

2024

Buildings

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To overcome limitations inherent in existing mechanical performance prediction models for pervious concrete, including material constraints, limited applicability, and inadequate accuracy, this study employs a deep learning approach to construct a Convolutional Neural Network (CNN) model with three convolutional modules. The primary objective of the model is to precisely predict the 28-day compressive strength of pervious concrete. Eight input variables, encompassing coarse and fine aggregate content, water content, admixture content, cement content, fly ash content, and silica fume content, were selected for the model. The dataset utilized for both model training and testing consists of 111 sample sets. To ensure the model’s coverage within the practical range of pervious concrete strength and to enhance its robustness in real-world applications, an additional 12 sets of experimental data were incorporated for training and testing. The research findings indicate that, in comparison to the conventional machine learning method of Backpropagation (BP) neural networks, the developed CNN prediction model in this paper demonstrates a higher coefficient of determination, reaching 0.938, on the test dataset. The mean absolute percentage error is 9.13%, signifying that the proposed prediction model exhibits notable accuracy and universality in predicting the 28-day compressive strength of pervious concrete, regardless of the materials used in its preparation.

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“…Concha [25] used a neural network to predict the carbonization depth of recycled-aggregate concrete, and the prediction results showed that the prediction model could provide better prediction results even if there was ambiguity in the data, and the results could be used to evaluate the health status of recycled-aggregate concrete structures. Huang et al [26] used a convolutional neural network (CNN) to predict the compressive strength of mixed-fiber-reinforced recycled-aggregate concrete. The results showed that the CNN prediction model had good prediction accuracy, and the average relative error and maximum relative error of the prediction results were 1.98% and 4.12%, respectively.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Influencing Factors and Two-Stage Prediction of Frost Resistance of Active-Admixture Recycled Concrete Based on Grey Theory–BPNN

Fu,

2024

Materials

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Sensitivity analysis of influencing factors on frost resistance is carried out in this paper, and a two-stage neural network model based on grey theory and Back Propagation Neural Networks (BPNNs) is established for the sake of predicting the frost resistance of active-admixture recycled concrete quickly and accurately. Firstly, the influence degree of cement, water, sand, natural aggregate, recycled aggregate, mineral powder, fly ash, fiber and air-entraining agent on the frost resistance of active-admixture recycled-aggregate concrete was analyzed based on the grey system theory, and the primary and secondary relationships of various factors were effectively distinguished. Then, the input layer of the model was determined as cement, water, sand, recycled aggregate and air-entraining agent, and the output layer was the relative dynamic elastic modulus. A total of 120 datasets were collected from the experimental data of another author, and the relative dynamic elastic modulus was predicted using the two-stage BPNN prediction model proposed in this paper and compared with the BPNN prediction results. The results show that the proposed two-stage BPNN model, after removing less-sensitive parameters from the input layer, has better prediction accuracy and shorter run time than the BPNN model.

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Orthogonal tests investigation into hybrid fiber-reinforce recycled aggregate concrete and convolutional neural network prediction

Cited by 8 publications

References 39 publications

Mechanical Properties of Hybrid Fiber Reinforced Rubber Concrete

Mechanical Properties of Hybrid Fiber Reinforced Rubber Concrete

The Prediction of Pervious Concrete Compressive Strength Based on a Convolutional Neural Network

Sensitivity Analysis of Influencing Factors and Two-Stage Prediction of Frost Resistance of Active-Admixture Recycled Concrete Based on Grey Theory–BPNN

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