Self-Diagnosing and Warning System of Wind-Induced Damage for Large-Span Spatial Steel Structure

2014 ◽  
Vol 971-973 ◽  
pp. 911-914
Author(s):  
Da Hai Gu ◽  
Chun Xiao ◽  
Xing Fei Zhang ◽  
Xue Ping Hao ◽  
Hui Liu
Keyword(s):  
Virtual Instrument ◽  
Structural Damage ◽  
Warning System ◽  
Steel Structure ◽  
Engineering Project ◽  
Large Span ◽  
Property Loss ◽  
Real Time Information ◽  
Database Sharing ◽  
Tensile Damage

The damage of Large-span Spatial Steel Structure (LSSS) can destroy the entire engineering project, and wind-induced weld tensile damage is a structural damage, which is more likely to occur. Some damaged nodes may not immediately endanger the safety of the structure. To a certain extent, the damage can lead to the collapse of the structure, resulting in large number of casualties and property loss. In order to discover and prevent these dangers in advance, an experiment about Large-span spatial wind-induced damage self-diagnosing and warning system is designed in this paper. Firstly, the equivalent structure model that can reflect the structural damage is built; secondly, some nodes’ load information is obtained by sensors and is delivered to computer; thirdly, the information of damage is analyzed; finally, virtual instrument and database sharing technology is used to establish a damage self-diagnosing and warning system. The results show that the system can monitor wind-induced weld tensile damage and remotely send real-time information to users.

scholarly journals Research on Early Warning System of Consulting Risk for Power Engineering Project

2021 ◽  
Vol 769 (4) ◽  
pp. 042014
Author(s):  
Jingfeng Ou ◽  
Jianwen Yuan ◽  
Jing Tian ◽  
Wei Zhang ◽  
Wenyan Cao ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Power Engineering ◽  
Engineering Project

scholarly journals A Novel Parallel Auto-Encoder Framework for Multi-Scale Data in Civil Structural Health Monitoring

Algorithms ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 112 ◽  
Author(s):  
Ruhua Wang ◽  
Ling Li ◽  
Jun Li
Keyword(s):  
Deep Learning ◽  
Dimension Reduction ◽  
Structural Damage ◽  
Natural Frequencies ◽  
Steel Structure ◽  
Mode Shapes ◽  
Multi Scale ◽  
Training Samples ◽  
Sparsity Constraint ◽  
Deep Learning Network

In this paper, damage detection/identification for a seven-storey steel structure is investigated via using the vibration signals and deep learning techniques. Vibration characteristics, such as natural frequencies and mode shapes are captured and utilized as input for a deep learning network while the output vector represents the structural damage associated with locations. The deep auto-encoder with sparsity constraint is used for effective feature extraction for different types of signals and another deep auto-encoder is used to learn the relationship of different signals for final regression. The existing SAF model in a recent research study for the same problem processed all signals in one serial auto-encoder model. That kind of models have the following difficulties: (1) the natural frequencies and mode shapes are in different magnitude scales and it is not logical to normalize them in the same scale in building the models with training samples; (2) some frequencies and mode shapes may not be related to each other and it is not fair to use them for dimension reduction together. To tackle the above-mentioned problems for the multi-scale dataset in SHM, a novel parallel auto-encoder framework (Para-AF) is proposed in this paper. It processes the frequency signals and mode shapes separately for feature selection via dimension reduction and then combine these features together in relationship learning for regression. Furthermore, we introduce sparsity constraint in model reduction stage for performance improvement. Two experiments are conducted on performance evaluation and our results show the significant advantages of the proposed model in comparison with the existing approaches.

Design Study of Steel Structure Fire-Retardant Coatings under High Temperature (Fire) Conditions

2012 ◽  
Vol 594-597 ◽  
pp. 849-859
Author(s):  
Man Li Ou ◽  
Wei Jun Cao ◽  
Long Min Jiang ◽  
Hui Cao
Keyword(s):  
Mechanical Properties ◽  
High Temperature ◽  
Fire Resistance ◽  
Fire Protection ◽  
Structural Damage ◽  
Fire Retardant ◽  
Steel Structure ◽  
Steel Structures ◽  
Strength And Stiffness ◽  
Fire Conditions

As the result of great changes occurring to mechanical properties under high temperature (fire) conditions, steel structures will soon lose the strength and stiffness and lead to structural damage. Through analysis of the steel structure fire resistance design methods under the conditions of high temperature (fire), this article explores the most used fire protection methods in steel structures—brushing or painting fire-resistant coatings, studies the fire-resistance theory of steel structure under fire conditions; in addition, the author proposes the reasonable thickness of the steel structure fire retardant coating of fire-resistant design through design examples.

Application of Health Monitoring Technology in Steel Structure Engineering of Jinan Olympic Sports Center

2014 ◽  
Vol 578-579 ◽  
pp. 1170-1176 ◽  
Author(s):  
Xue Jun Zhou ◽  
Rong Qian Yang ◽  
Xiao Ma ◽  
Yuan Xu
Keyword(s):  
Health Monitoring ◽  
Steel Structure ◽  
Steel Structures ◽  
World Health ◽  
Monitoring Technology ◽  
Large Span ◽  
Complex Structural ◽  
And Behavior ◽  
Structure Result ◽  
Structure Engineering

Complex structural pattern and behavior of Large-span steel structure result in famous research of health monitoring technology of this kind structure all over the world. Health monitoring to key parts of large-span steel structures during the construction and service process grasp the stress situation, which can ensure the safety of structures. In this paper, health monitoring project of Jinan Olympic Sports Center is introduced and the basis of test points’ layout is elaborated in detail. The result shows that the system designed is running stable, which means it has certain application value to other health monitoring to major engineering.

STRUCTURAL DAMAGE DETECTION BY MULTI-SCALE CROSS-SAMPLE ENTROPY

2015 ◽  
Vol 2 (1) ◽  
Author(s):  
Jui-Chang Liang ◽  
Ming-Jing Wang ◽  
Tzu-Kang Lin
Keyword(s):  
Structural Damage ◽  
Damage Index ◽  
Steel Structure ◽  
Vibration Signal ◽  
Sample Entropy ◽  
Ambient Vibration ◽  
Multi Scale ◽  
Damage Location ◽  
Damage Condition ◽  
Accuracy Rates

This study proposes a structural health monitoring (SHM) system based on multi-scale entropy (MSE) and multi-scale cross-sample entropy (MSCE). By measuring the ambient vibration signal from a structure, the damage condition can be rapidly evaluated via a MSE analysis. The damage location can then be detected by analyzing the signals of different floors under the same damage condition via a MSCE analysis. Moreover, a damage index is proposed to efficiently quantify the SHM process. A numerical simulation of a four-story steel structure is used to verify that the damage location and condition can be detected by the proposed SHM algorithm, and the location can be efficiently quantified by the damage index. Based on the results, the damage condition can be correctly assessed, and accuracy rates of 60% and 86% for the damage location can be achieved using the MSCE and damage index methods, respectively.

Simulation Study on Construction Process of Complex Spatial Steel Structure Based on the Construction Mechanics

2012 ◽  
Vol 226-228 ◽  
pp. 1209-1213
Author(s):  
Wei Lin Zhang ◽  
Zhi Xin Wu ◽  
Bai Hui Chen
Keyword(s):  
Finite Element ◽  
Steel Structure ◽  
Frame Structure ◽  
Construction Process ◽  
Element Analysis ◽  
Large Span ◽  
Critical Control Points ◽  
Construction Step ◽  
Construction Mechanics ◽  
Construction Condition

The construction process of the large span spatial steel structure was studied by finite element method and the time-varying mechanics. The construction process of complex spatial frame structure was divided into the installation and dismount supporting phase, and each stage was divided into 15 steps in which mainly simulated and analyzed the whole construction process by "life-death" element. The critical control points of each construction step were identified, the stress state, interval value and the maximum stress in the structure of each construction condition were conformed; rod deformation in construction process was analyzed in construction program. The result showed that the large span spatial steel structure construction scheme was numerical simulated and calculated by the method of finite element analysis and the mechanical state, deformation and safety of building structure were predicted accurately during construction.

Finite Element Modeling for Health Monitoring and Condition Assessment of Large-Span Steel Roof Unloading Process

2012 ◽  
Vol 166-169 ◽  
pp. 1370-1374
Author(s):  
Ya Xiong Liang ◽  
Xiu Li Wang ◽  
Hai Min Zhong
Keyword(s):  
Finite Element ◽  
Health Monitoring ◽  
Health Assessment ◽  
Steel Structure ◽  
Internal Force ◽  
Large Span ◽  
Structural Health Assessment ◽  
Finite Element Software ◽  
On Line ◽  
Steel Roof

The health monitoring and diagnosis of the major engineering structure is increasingly extensive attention from all the community. In particular, for the complex large-span steel roof unloading process, it is important especially. The unloading process will cause the change of structure stiffness include the internal force redistribution. The real-time and on-line monitoring have been applied to Xining stadium of the stress in the process of unloading for the purpose of structural health assessment in the paper, so as to achieve the purpose of the early warning of the problems which may arise in construction process. At the same time, through the comparison of the finite element software ANSYS analogue simulation and the value of the actual, it is obtained for the quality problem of steel structure in the process of unloading.

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