Numerical Investigation of the Dynamic Responses of Long-Span Bridges With Consideration of the Random Traffic Flow Based on the Intelligent ACO-BPNN Model

Zhang, Liwen; Sun, Zhuo; Zhang, Chao; Dong, Fenghui; Wei, Pu

doi:10.1109/access.2018.2840333

Cited by 18 publications

(9 citation statements)

References 27 publications

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“…In practical engineering applications, Zhang [11] used finite element numerical models to predict the dynamic response of long-span bridges, applied them to practical problems, and achieved good results. CAO [12] further optimized the finite element numerical simulation method and changed the piezoelectric impedance/admittance sensing used for structural health monitoring, and the actual test results verified the correctness of the method.…”

Section: Introductionmentioning

confidence: 99%

Application of Computer Simulation Method in Deep Underground Engineering of Complicated Geological Condition

Qiao

Zhang

et al. 2020

IEEE Access

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Computer simulation technology is used to solve various engineering problems in the new century, particularly exemplified by the widespread use of numerical simulation research methods. Due to the complicated geological environment, a mine tunnel with a depth of 850 m in Hebei Province has caused various problems, such as large displacements and difficulty in implementing support schemes. However, the complex geological conditions make the project unique, and support schemes for other projects cannot be simply copied. In addition, due to engineering cost requirements, repeatedly evaluating different on-site support schemes is unacceptable. Therefore, it is necessary to obtain an appropriate support scheme through computer simulation technology. Consequently, by implementing FLAC 3D software and using the progressive design method (PDM), a variety of support schemes are designed, and simulation tests, such as analysis and optimization of parameters, are performed. The research results show that through the analysis of stress, displacement and other aspects of the various schemes designed by using PDM, the finally selected scheme that was applied to the actual project achieved good results. The findings have proven the feasibility of the method, and the method can guide engineering problems under similar geological conditions.

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Section: Introductionmentioning

confidence: 99%

Application of Computer Simulation Method in Deep Underground Engineering of Complicated Geological Condition

Qiao

Zhang

et al. 2020

IEEE Access

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“…Wang et al [19] utilised the global search capability of the mind evolutionary algorithm to determine the initial weights and thresholds of a BPNN model. Zhang et al [20] employed the ant colony algorithm to optimise the initial weights and thresholds of a BPNN model then established a calibration model. Owing to its characteristics of few parameters, ability to jump out of the local optimum and satisfactory convergence ability, we employ the WOA as well as an improved convergence factor a [21] to optimise the parameters of the BPNN model.…”

Section: ) Woa-bpnn Calibration Algorithm Stepsmentioning

confidence: 99%

Research on a Wi-Fi RSSI Calibration Algorithm Based on WOA-BPNN for Indoor Positioning

Yao

et al. 2022

Applied Sciences

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Owing to the heterogeneity of software and hardware in different types of mobile terminals, the received signal strength indication (RSSI) from the same Wi-Fi access point (AP) varies in indoor environments, which can affect the positioning accuracy of fingerprint methods. To solve this problem and consider the nonlinear characteristics of Wi-Fi signal strength propagation and attenuation, we propose a whale optimisation algorithm-back-propagation neural network (WOA-BPNN) model for indoor Wi-Fi RSSI calibration. Firstly, as the selection of the initial parameters of the BPNN model has a considerable impact on the positioning accuracy of the calibration algorithm, we use the WOA to avoid blindly selecting the parameters of the BPNN model. Then, we propose an improved nonlinear convergence factor to balance the searchability of the WOA, which can also help to optimise the calibration algorithm. Moreover, we change the structure of the BPNN model to compare its influence on the calibration effect of the WOA-BPNN calibration algorithm. Secondly, in view of the low positioning accuracy of indoor fingerprint positioning algorithms, we propose a region-adaptive weighted K-nearest neighbour positioning algorithm based on hierarchical clustering. Finally, we effectively combine the two proposed algorithms and compare the results with those of other calibration algorithms such as the linear regression (LR), support vector regression (SVR), BPNN, and genetic algorithm-BPNN (GA-BPNN) calibration algorithms. The test results show that among different mobile terminals, the proposed WOA-BPNN calibration algorithm can increase positioning accuracy (one sigma error) by 41%, 42%, 44% and 36%, on average. The indoor field tests suggest that the proposed methods can effectively reduce the indoor positioning error caused by the heterogeneous differences of software and hardware in different mobile terminals.

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“…Therefore, the problem of MSPOM can be regarded as a problem of pattern recognition by an ANN. BPNN is a feed-forward learning model of backpropagation [45] and can achieve a global optimal approximation for the desired mapping and it has strong generalization ability [46]. We established a BPNN structure to judge whether candidate polygonal object matching pairs match or do not match.…”

Section: Matching Model Based On Bpnn (Bpm)mentioning

confidence: 99%

Multiscale Spatial Polygonal Object Granularity Factor Matching Method Based on BPNN

Zhu

Cheng

Zhai

et al. 2021

IJGI

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Spatial object matching is one of the fundamental technologies used for updating and merging spatial data. This study focused mainly on the matching optimization of multiscale spatial polygonal objects. We proposed a granularity factor evaluation index that was developed to promote the recognition ability of complex matches in multiscale spatial polygonal object matching. Moreover, we designed the granularity factor matching model based on a backpropagation neural network (BPNN) and designed a multistage matching workflow. Our approach was validated experimentally using two topographical datasets at two different scales: 1:2000 and 1:10,000. Our results indicate that the granularity factor is effective both in improving the matching score of complex matching and reducing the occurrence of missing matching, and our matching model is suitable for multiscale spatial polygonal object matching, with a high precision and recall reach of 97.2% and 90.6%.

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Numerical Investigation of the Dynamic Responses of Long-Span Bridges With Consideration of the Random Traffic Flow Based on the Intelligent ACO-BPNN Model

Cited by 18 publications

References 27 publications

Application of Computer Simulation Method in Deep Underground Engineering of Complicated Geological Condition

Application of Computer Simulation Method in Deep Underground Engineering of Complicated Geological Condition

Research on a Wi-Fi RSSI Calibration Algorithm Based on WOA-BPNN for Indoor Positioning

Multiscale Spatial Polygonal Object Granularity Factor Matching Method Based on BPNN

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