Surrogate model-based multi-objective design optimization of vibration suppression effect of acoustic black holes and damping materials on a rectangular plate

Du, Xiaofei; Fu, Qidi

doi:10.1016/j.apacoust.2023.109837

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…Given the better ability to express nonlinear relationships, deep neural networks (DNN) are used as surrogate models [18][19][20][21] for the bearing capacity of brackets. A DNN consists of one or more fully connected layers, each containing multiple neurons.…”

Section: Surrogate Based On Deep Neural Network Model For Predicting ...mentioning

confidence: 99%

Study on the Ultimate Load-Bearing Capacity of Disc Buckle Tall Formwork Support Considering Uncertain Factors

Huang,

Peng,

Hou

et al. 2024

Buildings

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Disc buckle steel pipe brackets are widely used in building construction due to the advantages of its simple structure, large-bearing capacity, rapid assembling and disassembling, and strong versatility. In complex construction projects, the uncertainties affecting the stability of disc buckle steel pipe support need to be considered to ensure the safety of disc buckle steel pipe supports. A surrogate model based on a deep neural network is built and trained to predict the ultimate load-carrying capacity of a stent. The results of the finite element model calculations are used to form the sample set of the surrogate model. Then, we combined the computationally efficient DNN surrogate model with the Monte Carlo method to consider the distribution of the ultimate load capacity of the disc buckle bracket under the uncertainties of the bracket node pin wedge tightness, the wall thickness of the steel pipe, and the connection of the connecting wall member. At the same time, based on the DNN model, the SHapley Additive exPlanations (SHAP) interpretability analysis method was used to study the degree of influence of various uncertainty factors on the ultimate bearing capacity of the stent. In practical engineering, the stability analysis of a disc buckle tall formwork support has shown that a surrogate model based on a deep neural network is efficient in predicting the buckling characteristic value of the support. The error rate of the prediction is less than 2%. The buckling characteristic values of the bracket vary in the range of 17–25. Among the various factors that influence the buckling characteristic value of the bracket, the joint wedge tightness has the greatest impact, followed by the bottom and top wall-connecting parts.

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Section: Surrogate Based On Deep Neural Network Model For Predicting ...mentioning

confidence: 99%

Study on the Ultimate Load-Bearing Capacity of Disc Buckle Tall Formwork Support Considering Uncertain Factors

Huang,

Peng,

Hou

et al. 2024

Buildings

View full text Add to dashboard Cite

show abstract

“…To sum up, although there are many improved Cartographer algorithms currently available, most of them have certain limitations in the application direction of drones due to their rich environmental characteristics, complex structures, and static scenes. To solve the problem of insufficient point cloud data and fast moving coordinate system drift in open environments, we propose an algorithm that integrates VIO to optimize Cartographer position estimation based on our previous experience in engineering measurement research on underwater robots and structural vibration [14][15]. In addition to radar data and flight control IMU data, the position data of optimized tightly coupled binocular vision VIO is also introduced into the position estimation of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

A precise localization algorithm for unmanned aerial vehicles integrating visual-internal odometry and cartographer

Xu,

Chen,

Chen

et al. 2024

J. meas. eng.

Self Cite

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Accurate positioning in space is an important foundation for ensuring the stability of autonomous flight and successful mapping and navigation of unmanned aerial vehicles (UAVs). At present, the SLAM algorithm based on the Cartographer algorithm for real-time positioning and mapping is widely used in fields such as robot navigation and autonomous driving. However, in the context of UAV applications, this algorithm has a high dependence on the amount of point cloud information in the surrounding environment, and cannot achieve precise positioning in open spaces with insufficient lighting and fewer feature points, resulting in significant mapping errors. In order to solve the problem of low positioning estimation accuracy in the Cartographer algorithm in above environment, this paper proposes a precise positioning algorithm for UAVs that integrates VIO and Cartographer. This algorithm can continuously output more accurate position estimation information during UAV flight, compensating for the problem of inaccurate position estimation caused by partial feature loss or coordinate system drift in point clouds. In addition, this algorithm ensures navigation obstacle avoidance in narrow spaces by improving positioning accuracy and mapping accuracy, making it more applicable in the field of UAVs. Finally, the effectiveness of the proposed positioning algorithm was verified through experimental analysis of the Cartographer dataset and practical testing of UAVs in real scenarios.

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Analysis of dynamic modeling and power flow of ABH laminated composite thin beam with elastic boundaries

Jiang,

Zhao,

Guo

et al. 2024

Thin-Walled Structures

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Surrogate model-based multi-objective design optimization of vibration suppression effect of acoustic black holes and damping materials on a rectangular plate

Cited by 4 publications

References 36 publications

Study on the Ultimate Load-Bearing Capacity of Disc Buckle Tall Formwork Support Considering Uncertain Factors

Study on the Ultimate Load-Bearing Capacity of Disc Buckle Tall Formwork Support Considering Uncertain Factors

A precise localization algorithm for unmanned aerial vehicles integrating visual-internal odometry and cartographer

Analysis of dynamic modeling and power flow of ABH laminated composite thin beam with elastic boundaries

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