scholarly journals Optimal Sensor Placement for Spatial Structure Based on Importance Coefficient and Randomness

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Haoxiang He ◽  
Honggang Xu ◽  
Xiaobing Wang ◽  
Xiaofu Zhang ◽  
Shaoyong Fan
Keyword(s):  
Spatial Structure ◽  
Structural Parameters ◽  
Sensor Placement ◽  
Time History ◽  
Time Varying ◽  
Optimal Sensor Placement ◽  
Importance Coefficient ◽  
Damage Process ◽  
Vulnerability Theory ◽  
Dynamic Time

The current methods of optimal sensor placement are majorly presented based on modal analysis theory, lacking the consideration of damage process of the structure. The effect of different minor damage cases acting on the total spatial structure is studied based on vulnerability theory in structural analysis. The concept of generalized equivalent stiffness is introduced and the importance coefficient of component is defined. For numerical simulation, the random characteristics for both structural parameters and loads are considered, and the random samples are established. The damage path of each sample is calculated and all the important members on the damage failure path are listed; therefore the sensor placement scheme is determined according to the statistical data. This method is extended to dynamic analysis. For every dynamic time-history analysis, time-varying responses of the structure are calculated by selecting appropriate calculating interval and considering the randomness of structural parameters and load. The time-varying response is analyzed and the importance coefficient of members is sorted; finally the dynamic sensor placement scheme is determined. The effectiveness of the method in this paper is certified by example.

scholarly journals Coverage intensity of optimal sensors for common, isolated, and integrated steel structures using novel approach of FEM-MAC-TTFD

2019 ◽  
Vol 15 (8) ◽  
pp. 155014771985756 ◽  
Author(s):  
Mehdi Firoozbakht ◽  
Hamidreza Vosoughifar ◽  
Alireza Ghari Ghoran
Keyword(s):  
Base Isolation ◽  
Sensor Placement ◽  
Time History ◽  
Shear Wall ◽  
Optimal Sensor Placement ◽  
Modal Assurance Criterion ◽  
Moment Resisting Frame ◽  
Steel Shear Wall ◽  
Significant Difference ◽  
Moment Resisting

The coverage intensity of sensors is the most important issue on structural health monitoring technique. The geometric configuration of sensors must be optimized based on coverage intensity with proper objectives. In this article, a novel algorithm for optimal sensor placement in various steel frames was evaluated. These frames including moment-resisting frame, moment-resisting frame with base isolation, and moment-resisting frame with base isolation with steel shear wall were selected for case studies. This approach was proposed based on combination of common optimal sensor placement algorithm and nonlinear time history analysis. A new method called transformed time history to frequency domain approach was evaluated to transform nonlinear time history analysis results to frequency domain and then the effective frequencies according the maximum range of Fourier amplitude were selected. The modified type of modal assurance criterion values can be achieved from modal assurance criterion with the exact seismic displacement. All of novel optimal sensor placement processes were done through FEM-MAC-TTFD code modeled and developed in MATLAB by authors of this article. The results show that there is good relative correlation between the sensors number and coverage intensity obtained with modal and modified modal assurance criterion approaches for moment-resisting frame system, but for integrated frame such as moment-resisting frame with base isolation and moment-resisting frame with base isolation with steel shear wall, the modified modal assurance criterion approach is better approach. There is no significant difference between coverage intensity of sensors for top joints between modal assurance criterion and modified modal assurance criterion approaches for moment-resisting frame, moment-resisting frame with base isolation, and moment-resisting frame with base isolation with steel shear wall systems ( R2 = 0.994, 0.986, and 0.724, respectively). It was found that if reference point is located in center of frame, there is significant difference between modal assurance criterion and modified modal assurance criterion approaches, and modified modal assurance criterion generated slightly better results.

Optimal Sensor Placement Algorithm for Structural Damage Identification

2020 ◽  
Vol 14 (1) ◽  
pp. 69-81
Author(s):  
C.H. Li ◽  
Q.W. Yang
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Sensor Placement ◽  
Optimization Procedure ◽  
Frame Structure ◽  
Truss Structures ◽  
Optimal Sensor Placement ◽  
Structural Damage Identification ◽  
Placement Algorithm ◽  
Sensor Locations

Background: Structural damage identification is a very important subject in the field of civil, mechanical and aerospace engineering according to recent patents. Optimal sensor placement is one of the key problems to be solved in structural damage identification. Methods: This paper presents a simple and convenient algorithm for optimizing sensor locations for structural damage identification. Unlike other algorithms found in the published papers, the optimization procedure of sensor placement is divided into two stages. The first stage is to determine the key parts in the whole structure by their contribution to the global flexibility perturbation. The second stage is to place sensors on the nodes associated with those key parts for monitoring possible damage more efficiently. With the sensor locations determined by the proposed optimization process, structural damage can be readily identified by using the incomplete modes yielded from these optimized sensor measurements. In addition, an Improved Ridge Estimate (IRE) technique is proposed in this study to effectively resist the data errors due to modal truncation and measurement noise. Two truss structures and a frame structure are used as examples to demonstrate the feasibility and efficiency of the presented algorithm. Results: From the numerical results, structural damages can be successfully detected by the proposed method using the partial modes yielded by the optimal measurement with 5% noise level. Conclusion: It has been shown that the proposed method is simple to implement and effective for structural damage identification.

Response Law and Influencing Factors of the Soil under Action of Pile Driving Vibration Based on Midas/GTS

2013 ◽  
Vol 353-356 ◽  
pp. 979-983
Author(s):  
Dong Zhang ◽  
Jing Bo Su ◽  
Hui De Zhao ◽  
Hai Yan Wang
Keyword(s):  
Damping Ratio ◽  
Large Diameter ◽  
Time History ◽  
Pile Driving ◽  
Analysis Model ◽  
Vibration Load ◽  
History Analysis ◽  
Study Results ◽  
Propagation Law ◽  
Dynamic Time

Due to the upgrade and reconstruct of a high-piled wharf, the piling construction may cause the damage of the large diameter underground pipe of a power plant nearby. For this problem, a dynamic time-history analysis model was established using MIDAS/GTS program. Based on the analysis of the pile driving vibration and its propagation law, some parameters, such as the modulus of the soil, the Poissons ratio of soil, the action time of vibration load and the damping ratio of the soil that may have an effect on the response law of the soil, were studied. The study results not only serve as an important inference to the construction of this case, but also accumulate experience and data for other similar engineering practices.

scholarly journals A Data Driven Method for Optimal Sensor Placement in Multi-Zone Buildings

2021 ◽  
pp. 110956
Author(s):  
Gowri Suryanarayana ◽  
Javier Arroyo ◽  
Lieve Helsen ◽  
Jesus Lago
Keyword(s):  
Sensor Placement ◽  
Data Driven ◽  
Optimal Sensor Placement

scholarly journals Optimal Sensor Placement for Reliable Virtual Sensing Using Modal Expansion and Information Theory

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3400
Author(s):  
Tulay Ercan ◽  
Costas Papadimitriou
Keyword(s):  
Utility Function ◽  
Measurement Errors ◽  
Structural Model ◽  
Information Gain ◽  
Sensor Placement ◽  
Optimal Sensor Placement ◽  
Modal Expansion ◽  
Expected Information ◽  
Virtual Sensing ◽  
Multidimensional Integral

A framework for optimal sensor placement (OSP) for virtual sensing using the modal expansion technique and taking into account uncertainties is presented based on information and utility theory. The framework is developed to handle virtual sensing under output-only vibration measurements. The OSP maximizes a utility function that quantifies the expected information gained from the data for reducing the uncertainty of quantities of interest (QoI) predicted at the virtual sensing locations. The utility function is extended to make the OSP design robust to uncertainties in structural model and modeling error parameters, resulting in a multidimensional integral of the expected information gain over all possible values of the uncertain parameters and weighted by their assigned probability distributions. Approximate methods are used to compute the multidimensional integral and solve the optimization problem that arises. The Gaussian nature of the response QoI is exploited to derive useful and informative analytical expressions for the utility function. A thorough study of the effect of model, prediction and measurement errors and their uncertainties, as well as the prior uncertainties in the modal coordinates on the selection of the optimal sensor configuration is presented, highlighting the importance of accounting for robustness to errors and other uncertainties.

Health monitoring sensor placement optimization based on initial sensor layout using improved partheno-genetic algorithm

2020 ◽  
pp. 136943322094719
Author(s):  
Xianrong Qin ◽  
Pengming Zhan ◽  
Chuanqiang Yu ◽  
Qing Zhang ◽  
Yuantao Sun
Keyword(s):  
Genetic Algorithm ◽  
Health Monitoring ◽  
Large Scale ◽  
Complex Structure ◽  
Sensor Placement ◽  
Placement Problem ◽  
Optimal Sensor Placement ◽  
Placement Optimization ◽  
Sensor Placement Optimization ◽  
Sensor Locations

Optimal sensor placement is an important component of a reliability structural health monitoring system for a large-scale complex structure. However, the current research mainly focuses on optimizing sensor placement problem for structures without any initial sensor layout. In some cases, the experienced engineers will first determine the key position of whole structure must place sensors, that is, initial sensor layout. Moreover, current genetic algorithm or partheno-genetic algorithm will change the position of the initial sensor locations in the iterative process, so it is unadaptable for optimal sensor placement problem based on initial sensor layout. In this article, an optimal sensor placement method based on initial sensor layout using improved partheno-genetic algorithm is proposed. First, some improved genetic operations of partheno-genetic algorithm for sensor placement optimization with initial sensor layout are presented, such as segmented swap, reverse and insert operator to avoid the change of initial sensor locations. Then, the objective function for optimal sensor placement problem is presented based on modal assurance criterion, modal energy criterion, and sensor placement cost. At last, the effectiveness and reliability of the proposed method are validated by a numerical example of a quayside container crane. Furthermore, the sensor placement result with the proposed method is better than that with effective independence method without initial sensor layout and the traditional partheno-genetic algorithm.

Seismic Vulnerability Analysis of FRP Retrofitting Industrial Buildings

2015 ◽  
Vol 724 ◽  
pp. 353-357
Author(s):  
Jian Zhu ◽  
Ping Tan ◽  
Pei Ju Chang
Keyword(s):  
Seismic Vulnerability ◽  
Response Spectrum ◽  
Time History ◽  
Seismic Intensity ◽  
Fiber Reinforced Polymers ◽  
Infill Wall ◽  
Intensity Index ◽  
Industrial Buildings ◽  
Dynamic Time ◽  
Masonry Infill Wall

This study focus on derivation of such vulnerability curves using Fiber Reinforced Polymers technologies retrofitted conventional RC industrial frames with masonry infill wall. A set of stochastic earthquake waves which compatible with the response spectrum of China seismic code are created. Dynamic time history analysis is used to compute the random sample of structures. Stochastic damage scatter diagrams based different seismic intensity index are obtained. Seismic vulnerability of FRP-reinforced RC industrial frames is lower than unreinforced frames obviously, and seismic capability of frames using FRP technologies is enhanced especially under major earthquake.

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