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Symmetry
Special Issues
Wind Resistance and Health Monitoring of Symmetrical Bridge Structures
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Wind Resistance and Health Monitoring of Symmetrical Bridge Structures

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 7120

Special Issue Editors

Prof. Dr. Shengli Li

E-Mail Website
Guest Editor
School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: structural wind engineering and health monitoring; intelligent roads; acoustic emission monitoring (detection); infrared thermal imaging detection; cable vibration control; cable structure durability
Dr. Pan Guo

E-Mail Website
Guest Editor
School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: wind resistance of cable bridges; structural health monitoring; structural inspection and evaluation
Dr. Guangming Wu

E-Mail Website
Guest Editor
School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: structural health monitoring; acoustic emission monitoring; bridge bearings; big data analysis; damage diagnosis; mechanical analysis
Dr. Panjie Li

E-Mail Website
Guest Editor
School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: cable structure health monitoring; rapid detection and safety diagnosis of small and medium bridges; big data and artificial intelligence
Dr. Bin Xu

E-Mail Website
Guest Editor
School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: cable bridge health monitoring; cable dynamic characteristic analysis; inverse analysis of cable force and key parameters
Dr. Can Cui

E-Mail Website
Guest Editor
School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: machine vision; picture processing; defect detecting; honeycomb structure
Dr. Pengfei Zheng

E-Mail Website
Guest Editor
School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: interaction analysis between bridge and bridge deck structure, traffic structure analysis, intelligent detection and monitoring
Dr. Xidong Wang

E-Mail Website
Guest Editor
School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: wind vibration control of cable structure; algorithm optimization of human induced vibration control; active and passive control method of pedestrian structure

Special Issue Information

Dear Colleagues,

The wind resistance and health monitoring of bridge structures are closely related to structural symmetry. Due to the different geometric forms of symmetric and asymmetric bridge structures, the critical wind speed of wind-induced vibration and static wind stability mechanisms are different. The static wind instability of bridge structures is very destructive and there is no warning before static wind instability. Therefore, it is necessary to study the wind resistance of both symmetric and asymmetric bridge structures. In addition, the deformation and internal force distribution of symmetric and asymmetric bridge structures are different. There is a linear mutation in individual positions, which brings difficulty in construction control. Meanwhile, the shrinkage and creep effects of concrete will lead to increased structural secondary internal force and deflection deformation in the later stage of the bridge’s useful life, affecting the safety of the bridge structure. Therefore, in order to ensure the safety of symmetric bridge structures during construction and service life, it is necessary to study their health monitoring.

This Special Issue invites researchers to submit original research papers and review articles related to the wind resistance and health monitoring of symmetrical bridge structures, in which the theoretical or practical problems of symmetry are considered. Applied case studies are especially welcome. The topics of interest include but are not limited to:

Static wind stability and instability characteristics of symmetrical bridge structures;

Dynamic characteristics of symmetrical bridge structures;

Wind resistance stability of symmetrical bridge structures during the construction period;

Data-driven damage detection of symmetrical bridge structures;

Health monitoring of symmetrical bridge structures based on acoustic emission technique;

Vibration control of cables for symmetrical suspension and cable-stayed bridges.

Prof. Dr. Shengli Li
Dr. Pan Guo
Dr. Guangming Wu
Dr. Panjie Li
Dr. Bin Xu
Dr. Can Cui
Dr. Pengfei Zheng
Dr. Xidong Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • symmetrical bridge structures
  • wind resistance
  • structural health monitoring
  • static performance
  • dynamic characteristics

Published Papers (2 papers)

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Research

24 pages, 11470 KiB  
Article
Numerical Modeling of Ice Accumulation on Three-Dimensional Bridge Cables under Freezing Rain and Natural Wind Conditions
by Dalei Wang, Mengjin Sun, Rujin Ma and Xiang Shen
Symmetry 2022, 14(2), 396; https://doi.org/10.3390/sym14020396 - 16 Feb 2022
Cited by 3 | Viewed by 2155
Abstract
In order to accurately predict the ice accumulation on bridge cables under two typical freezing rain conditions, rime and glaze ice, this paper proposes a numerical simulation framework based on the three-dimensional Messinger theory. Two technical challenges of determining the flow direction of [...] Read more.
In order to accurately predict the ice accumulation on bridge cables under two typical freezing rain conditions, rime and glaze ice, this paper proposes a numerical simulation framework based on the three-dimensional Messinger theory. Two technical challenges of determining the flow direction of unfrozen water and solving three-dimensional Messinger equations are solved in this research. Based on the outflow, mass was calculated according to the three-dimensional Messinger theory, and the flow direction of unfrozen water in each cell was determined by the resultant force of air shear stress and water film gravity. To solve the three-dimensional equations, an iterative method without finding the stagnation line was introduced. The final iced geometries were determined when the inflow mass ratio was satisfied with the converge criteria. Moreover, this modified numerical model was programmed and embedded into computational fluid software. For both two typical freezing rain conditions, the effects of temperature and wind speed on iced geometries were studied. The aerodynamic characteristics and galloping instability of bridge cables with different iced geometries were also investigated. These preliminary aerodynamic simulations can provide the basis for the wind-induced vibration analysis of the whole structure. Full article
(This article belongs to the Special Issue Wind Resistance and Health Monitoring of Symmetrical Bridge Structures)
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13 pages, 5045 KiB  
Article
Case Study of Deep Learning Model of Temperature-Induced Deflection of a Cable-Stayed Bridge Driven by Data Knowledge
by Zixiang Yue, Youliang Ding, Hanwei Zhao and Zhiwen Wang
Symmetry 2021, 13(12), 2293; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13122293 - 02 Dec 2021
Cited by 10 | Viewed by 1955
Abstract
A cable-stayed bridge is a typical symmetrical structure, and symmetry affects the deformation characteristics of such bridges. The main girder of a cable-stayed bridge will produce obvious deflection under the inducement of temperature. The regression model of temperature-induced deflection is hoped to provide [...] Read more.
A cable-stayed bridge is a typical symmetrical structure, and symmetry affects the deformation characteristics of such bridges. The main girder of a cable-stayed bridge will produce obvious deflection under the inducement of temperature. The regression model of temperature-induced deflection is hoped to provide a comparison value for bridge evaluation. Based on the temperature and deflection data obtained by the health monitoring system of a bridge, establishing the correlation model between temperature and temperature-induced deflection is meaningful. It is difficult to complete a high-quality model only by the girder temperature. The temperature features based on prior knowledge from the mechanical mechanism are used as the input information in this paper. At the same time, to strengthen the nonlinear ability of the model, this paper selects an independent recurrent neural network (IndRNN) for modeling. The deep learning neural network is compared with machine learning neural networks to prove the advancement of deep learning. When only the average temperature of the main girder is input, the calculation accuracy is not high regardless of whether the deep learning network or the machine learning network is used. When the temperature information extracted by the prior knowledge is input, the average error of IndRNN model is only 2.53%, less than those of BPNN model and traditional RNN. Combining knowledge with deep learning is undoubtedly the best modeling scheme. The deep learning model can provide a comparison value of bridge deformation for bridge management. Full article
(This article belongs to the Special Issue Wind Resistance and Health Monitoring of Symmetrical Bridge Structures)
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