Prediction of thermo-mechanical properties of rubber-modified recycled aggregate concrete

Feng, Wanhui; Wang, Yufei; Sun, Junbo; Tang, Yunchao; Wu, Dongxiao; Jiang, Zhiwei; Wang, Jianqun; Wang, Xiangyu

doi:10.1016/j.conbuildmat.2021.125970

Cited by 94 publications

(38 citation statements)

References 63 publications

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“…High prediction accuracy is observed on the training set (Figure 9a) and test set (Figure 9b), as indicated by the high R values (0.9971 and 0.9893 on the training and test sets, respectively) and low RMSE values (0.7167 MPa and 1.5158 MPa on the training and test sets, respectively). Compared with previously published papers [42,51], the obtained results show much higher accuracy (R is around 0.99), which might be attributed to the model performance or the accuracy and size of the database. Furthermore, no overfitting problems take place as the test set RMSE (and R) is close to that on the training set.…”

Section: Performance Of the Bas-bpnn Modelcontrasting

confidence: 68%

“…The artificial neural network (ANN) is one of the commonly used machine learning models, which comprises many categories such as recurrent neural networks (RNN) and feedforward neural network (FFNN). The FFNN includes the Back-propagation neural network (BPNN), which is widely employed to solve problems in the field of building materials and construction [42,75,76]. Back propagation (BP) is a popular approach to adjust the weights and bias of the model, which is composed of an input layer, one or more hidden layers, and one output layer.…”

Section: Bpnnmentioning

confidence: 99%

“…ML models make predictions and decisions by building a mathematical model without being explicit programming based on sample data [37][38][39]. Many ML models have been used to predict concrete strength, such as neural networks, support vector regression (SVR), and tree-based models [40][41][42][43]. For instance, Huynh et al, (2020) [44] utilised artificial neural network (ANN), deep neural network (DNN), and deep residual network (ResNet) to predict the compressive strength of fly ash-based geopolymer concrete.…”

Section: Introductionmentioning

confidence: 99%

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Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network

et al. 2022

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High-strength concrete (HSC) is a functional material possessing superior mechanical performance and considerable durability, which has been widely used in long-span bridges and high-rise buildings. Unconfined compressive strength (UCS) is one of the most crucial parameters for evaluating HSC performance. Previously, the mix design of HSC is based on the laboratory test results which is time and money consuming. Nowadays, the UCS can be predicted based on the existing database to guide the mix design with the development of machine learning (ML) such as back-propagation neural network (BPNN). However, the BPNN’s hyperparameters (the number of hidden layers, the number of neurons in each layer), which is commonly adjusted by the traditional trial and error method, usually influence the prediction accuracy. Therefore, in this study, BPNN is utilised to predict the UCS of HSC with the hyperparameters tuned by a bio-inspired beetle antennae search (BAS) algorithm. The database is established based on the results of 324 HSC samples from previous literature. The established BAS-BPNN model possesses excellent prediction reliability and accuracy as shown in the high correlation coefficient (R = 0.9893) and low Root-mean-square error (RMSE = 1.5158 MPa). By introducing the BAS algorithm, the prediction process can be totally automatical since the optimal hyperparameters of BPNN are obtained automatically. The established BPNN model has the benefit of being applied in practice to support the HSC mix design. In addition, sensitivity analysis is conducted to investigate the significance of input variables. Cement content is proved to influence the UCS most significantly while superplasticizer content has the least significance. However, owing to the dataset limitation and limited performance of ML models which affect the UCS prediction accuracy, further data collection and model update must be implemented.

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Section: Performance Of the Bas-bpnn Modelcontrasting

confidence: 68%

Section: Bpnnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network

et al. 2022

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“…The newly developed model was also evaluated by comparing the model results with that of a field test. Furthermore, prediction studies using machine learning have been conducted in various areas [ 11 , 12 , 13 , 14 , 15 ]. However, there has been no parametric study on the prediction of concrete fragments and their travel distance.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Concrete Fragments Amount and Travel Distance under Impact Loading Using Deep Neural Network and Gradient Boosting Method

Kim

Seo

et al. 2022

Materials

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In the present study, the amount of fragments generated and their travel distances due to vehicle collision with concrete median barrier (CMB) was analyzed and predicted. In this regard, machine learning was applied to the results of numerical analysis, which were developed by comparing with field test. The numerical model was developed using smoothed particle hydrodynamics (SPH). SPH is a mesh-free method that can be used to predict the amount of fragments and their travel distances from concrete structures under impact loading. In addition, deep neural network (DNN) and gradient boosting machine (GBM) were also employed as machine learning methods. In this study, the results of DNN, GBM, and numerical analysis were then compared with the conducted field test. Such comparisons revealed that numerical analysis generated lower error than both DNN and GBM. When prediction results of both the amount of fragments and their travel distances were considered, the result of DNN showed smaller errors than that of GBM. Therefore, in studies where machine learning is used to predict the amount of fragments and their travel distances, careful selection of an appropriate method from the various available machine learning methods such as DNN, GBM, and random forest is absolutely important.

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“…Later, Sutton et al [25][26][27] improved the theories related to digital image technology and applied this method to study the variation in the displacement field in the crack propagation process. Some scholars [28][29][30][31][32] have used glass fiber-reinforced polymers to replace steel to avoid performance degradation caused by steel corrosion. In order to improve the real-time monitoring effect of the structure, a conductive cement composite material was developed using artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

Study on Crack Propagation and Coalescence in Fractured Limestone Based on 3D-DIC Technology

et al. 2022

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To deeply understand the influence of crack inclination angle on crack propagation and coalescence in fractured limestone, uniaxial compression tests were carried out on limestone specimens with prefabricated cracks. The strain field evolution diagram of the failure process of the specimens was obtained using 3D digital image correlation technology (3D-DIC technology). This, in combination with the crack propagation diagram, was used to analyze the entire failure process of the limestone specimens. The test results show that the evolution process of the principal strain field agrees well with the process of crack initiation, propagation, and coalescence. The crack development process is the process of the high strain zones consistently propagating and also the process of micro-cracks appearing, developing, and nucleating to form macro-cracks. With the increase in the parallel crack inclination angle, the stress concentration zone of the intermediate crack transfers from both ends of the crack to the middle. Meanwhile, the coalescing crack type between the parallel crack and the intermediate crack changes from a coexisting tensile crack and tensile-shear crack to a single tensile crack. With the increase in the parallel crack inclination angle, the failure of the fractured limestone specimens changes from simple splitting or tensile failure to the coexistence of tensile-shear fracture and splitting. 3D-DIC technology provides an effective method to study crack propagation and coalescence during rock failure.

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Prediction of thermo-mechanical properties of rubber-modified recycled aggregate concrete

Cited by 94 publications

References 63 publications

Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network

Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network

Prediction of Concrete Fragments Amount and Travel Distance under Impact Loading Using Deep Neural Network and Gradient Boosting Method

Study on Crack Propagation and Coalescence in Fractured Limestone Based on 3D-DIC Technology

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