Numerical Analysis of Flexural Behavior of Prestressed Steel-Concrete Continuous Composite Beams Based on BP Neural Network

Du, Huanhuan; Pan, Jianyou; Shen, Huaxun; Dong, Jie

doi:10.1155/2022/5501610

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Han et al [26] used the BPNN to analyse the effect of different admixtures of waste fly ash on the compressive strength of high-performance concrete, and the results showed that the BPNN has high accuracy in predicting the compressive strength of waste fly ash concrete. Du Huanhuan et al [27] established a three-dimensional finite element numerical analysis model for the flexural performance of prestressed steelconcrete continuous composite beams and simulated the whole process of the test based on the BPNN. Cao et al [28] proposed a fully convolutional neural network to automatically identify and detect cracks by constructing a framework for learning concrete cracks and performing semantic segmentation to achieve crack identification.…”

Section: Introductionmentioning

confidence: 99%

Study of the Effect of Retarder and Expander on the Strength and Cracking Performance of Rubber Concrete Based on Back Propagation Neural Network

Sui,

Qiao,

et al. 2023

Materials

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The advantages of rubber concrete (RC) are good ductility, fatigue resistance, and impact resistance, but few studies have been conducted on the effects of different rubber admixtures on the strength of RC and the cracking performance of rubber mortar. In this study, the compressive and flexural tests of rubber concrete and the crack resistance test of rubber mortar were carried out by changing the rubber content and adding expansion agent and retarder in this test. The test results show that the strength of RC decreases with the increase in rubber admixture. At 15% of rubber admixture, the expansion agent and retarder increase the compressive strength and flexural strength of RC the most; the compressive strength increased to 116% (22.6 MPa) and 109% (21.2 MPa), and the flexural strength increased to 111% (4.02 MPa) and 116%. (4.21 MPa). At the same rubber admixture, the expander improves the cracking time of the rubber mortar by about 3 times, and the retarder improves the cracking time of the rubber mortar by about 1.6 times. The BP neural network (BPNN) was established to simulate and predict the compressive and flexural strengths of RC with different admixtures and the cracking time of rubber mortar. The simulation results show that the predicted 7-day compressive strength of RC fits best with the actual value, with a value of 0.994, and the predicted 28-day flexural strength was closest to the measured value, with an average relative error of 1.7%. It was shown that the calculation results of the artificial intelligence prediction model are more accurate. The simulation results and test results show that the expander and retarder significantly improve the strength of RC as well as the cracking performance of rubber mortar.

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Section: Introductionmentioning

confidence: 99%

Study of the Effect of Retarder and Expander on the Strength and Cracking Performance of Rubber Concrete Based on Back Propagation Neural Network

Sui,

Qiao,

et al. 2023

Materials

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“…It has been widely adopted in the design and construction of various large-span bridges due to its high structural stiffness, lightweight properties, and reduced deflection [3][4][5][6]. Over the past several decades, researchers have conducted theoretical and experimental studies [7][8][9][10] to verify the excellent performance of the steel-concrete composite bridge deck system during the service life of bridge structures. Among various types of steel-concrete composite bridge deck systems, the orthotropic steel deck (OSD)-concrete structure has been extensively utilized in medium and large-span bridges due to its advantages such as lightweight design, high load-carrying capacity, and fast construction speed [11].…”

Section: Introductionmentioning

confidence: 99%

Design and Optimization of the Bi-Directional U-Ribbed Stiffening Plate–Concrete Composite Bridge Deck Structure

Wei,

Liao,

Liu

et al. 2023

Applied Sciences

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The steel–concrete composite structure is widely used in civil engineering for large-span bridges. Orthotropic steel bridge decks (OSDs) have particularly gained popularity due to their excellent mechanical performance. To address cracking issues in OSDs and concrete in negative moment regions, a novel bi-directional U-ribbed stiffening plate (BUSP)–concrete composite bridge deck is proposed. By using finite element analysis, the mechanical performance is evaluated based on maximum tensile stress and vertical displacement of concrete overlays. Results show that the BUSP–concrete deck outperformed conventional flat decks. It is also found that increasing the height, thickness, and opening width of U-ribs reduced tensile stress and maximum displacement. Adjusting height had the most significant effect on displacement while opening width affected tensile stress the most. Considering material usage, optimizing height is proved to be more effective than adjusting thickness and opening width. Decreasing spacing parameters improved performance but added complexity and reduced construction convenience. These findings will guide the design and optimization of steel–concrete composite bridge deck structures.

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Machine Learning and Optimization Algorithms for Vibration, Bending and Buckling Analyses of Composite/Nanocomposite Structures: A Systematic and Comprehensive Review

Ercument,

Safaei,

Sahmani

et al. 2024

Arch Computat Methods Eng

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Numerical Analysis of Flexural Behavior of Prestressed Steel-Concrete Continuous Composite Beams Based on BP Neural Network

Cited by 3 publications

References 19 publications

Study of the Effect of Retarder and Expander on the Strength and Cracking Performance of Rubber Concrete Based on Back Propagation Neural Network

Study of the Effect of Retarder and Expander on the Strength and Cracking Performance of Rubber Concrete Based on Back Propagation Neural Network

Design and Optimization of the Bi-Directional U-Ribbed Stiffening Plate–Concrete Composite Bridge Deck Structure

Machine Learning and Optimization Algorithms for Vibration, Bending and Buckling Analyses of Composite/Nanocomposite Structures: A Systematic and Comprehensive Review

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