Strength index analysis of concrete with large size recycled aggregate based on back propagation neural network

Li, Tan; Singh, Amardeep

doi:10.1177/13694332211046348

Cited by 3 publications

(2 citation statements)

References 18 publications

(20 reference statements)

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“…Li T et al optimized the BP neural network model by analyzing each model parameter. Results showed that optimized model had significant support for the study of concrete strength [8] .…”

Section: Related Workmentioning

confidence: 89%

Teaching quality evaluation model construction of university industry and education integration by adaptive BP and DDAE-SVR

Zhang,

Weng

2024

Preprint

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To adapt to changing teaching modes in colleges and universities, and better predict and analyze the students' learning situation, this study aims at a series of deficiencies existing in the traditional teaching quality evaluation index system. By leveraging deep learning, the evaluation model is enhanced and constructed. Firstly, the system is refined by integrating the concept of integrating industry and education. Secondly, the traditional back propagation neural network is improved, and the model is built by incorporating deep noise reduction auto-encoder and support vector regression technology. Model test showed that model used in this study had good iterative performance. When the number of iterations was 62, the model started to enter a stable state, and the optimal fitness value in the stable state was 0.25. In addition, the detection accuracy was up to 0.98, and prediction effect can be satisfied by most teachers and students. To sum up, the quality evaluation model can accurately evaluate teaching quality, provide a reliable reference for colleges and enterprises, and promote the in-depth integration development of industry and education.

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“…Li T et al optimized the BP neural network model by analyzing each model parameter. Results showed that optimized model had significant support for the study of concrete strength [8] .…”

Section: Related Workmentioning

confidence: 89%

Teaching quality evaluation model construction of university industry and education integration by adaptive BP and DDAE-SVR

Zhang,

Weng

2024

Preprint

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“…Existing studies indicate that models based on machine learning and deep learning can predict the compressive strength of concrete with relatively high accuracy [30][31][32]. For instance, predictive models constructed using BP neural networks have shown good predictive performance regarding the compressive strength of different types of concrete [33][34][35][36][37]. 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].…”

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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Data-Driven Compressive Strength Prediction of Fly Ash Concrete Using Ensemble Learner Algorithms

et al. 2022

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Concrete is one of the most popular materials for building all types of structures, and it has a wide range of applications in the construction industry. Cement production and use have a significant environmental impact due to the emission of different gases. The use of fly ash concrete (FAC) is crucial in eliminating this defect. However, varied features of cementitious composites exist, and understanding their mechanical characteristics is critical for safety. On the other hand, for forecasting the mechanical characteristics of concrete, machine learning approaches are extensively employed algorithms. The goal of this work is to compare ensemble deep neural network models, i.e., the super learner algorithm, simple averaging, weighted averaging, integrated stacking, as well as separate stacking ensemble models, and super learner models, in order to develop an accurate approach for estimating the compressive strength of FAC and reducing the high variance of the predictive models. Separate stacking with the random forest meta-learner received the most accurate predictions (97.6%) with the highest coefficient of determination and the lowest mean square error and variance.

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Strength index analysis of concrete with large size recycled aggregate based on back propagation neural network

Cited by 3 publications

References 18 publications

Teaching quality evaluation model construction of university industry and education integration by adaptive BP and DDAE-SVR

Teaching quality evaluation model construction of university industry and education integration by adaptive BP and DDAE-SVR

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

Data-Driven Compressive Strength Prediction of Fly Ash Concrete Using Ensemble Learner Algorithms

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