Vibration-Based SHM of Ordinary Buildings: Detection and Quantification of Structural Damage

2015 ◽  
Author(s):  
Alessio Pierdicca ◽  
Francesco Clementi ◽  
Diletta Maracci ◽  
Daniela Isidori ◽  
Stefano Lenci
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Residual Life ◽  
Numerical Models ◽  
Natural Aging ◽  
Real Structure ◽  
Industrial Building ◽  
Ground Acceleration ◽  
Earthquake Scenario ◽  
Structural Health

One of the most important issues in civil and in mechanical engineering is the detection of structural damages, which are defined as changes of material properties, of boundary conditions and of system connectivity, which adversely affect the system’s performances. The damage identification process generally requires establishing existence, localization, type and intensity of the damage. During its service life, a structure, besides his natural aging, can be subjected to earthquakes. These events may have a deep impact on building safety and a continuous monitoring of the structure health conditions, through Structural Health Monitoring (SHM) techniques, is necessary in many cases. Within this a background, the purpose of this work is to propose an integrated novel approach for the diagnosis of structures after a seismic event. The proposed monitoring system is based on recording the accelerations of the real structure during a seismic input, and the reintroduction of them into a numerical model, suitably tuned, in order to outline a possible post-earthquake scenario. This approach provides an estimation of the health of the building and of its residual life, and to detect and quantify the damage, some of the crucial aspects of SHM. Actually, we also get both online and self-diagnosis of the structural health. The technique is applied to a real structure, an industrial building liable of some seismic vulnerabilities. It it did not undergo an earthquake, so we have not recordered accelerations, and get them from a different numerical models subjected to the ground acceleration of a realistic earthquake.

Artificial Neural Networks for Structural Health Monitoring of Helicopter Harsh Landings

2013 ◽  
Vol 390 ◽  
pp. 192-197
Author(s):  
Giorgio Vallone ◽  
Claudio Sbarufatti ◽  
Andrea Manes ◽  
Marco Giglio
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Numerical Models ◽  
Damage State ◽  
Operational Conditions ◽  
Structural Health ◽  
Artificial Neural

The aim of the current paper is to explore fuselage monitoring possibilities trough the usage of Artificial Neural Networks (ANNs), trained by the use of numerical models, during harsh landing events. A harsh landing condition is delimited between the usual operational conditions and a crash event. Helicopter structural damage due to harsh landings is generally less severe than damage caused by a crash but may lead to unscheduled maintenance events, involving costs and idle times. Structural Health Monitoring technologies, currently used in many application fields, aim at the continuous detection of damage that may arise, thereby improving safety and reducing maintenance idle times by the disposal of a ready diagnosis. A landing damage database can be obtained with relatively little effort by the usage of a numerical model. Simulated data are used to train various ANNs considering the landing parameter values as input. The influence of both the input and output noise on the system performances were taken into account. Obtained outputs are a general classification between damaged and undamaged conditions, based on a critical damage threshold, and the reconstruction of the fuselage damage state.

scholarly journals Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring

2020 ◽  
Vol 9 (1) ◽  
pp. 14-23 ◽  
Author(s):  
Meisam Gordan ◽  
Zubaidah Binti Ismail ◽  
Hashim Abdul Razak ◽  
Khaled Ghaedi ◽  
Haider Hamad Ghayeb
Keyword(s):  
Data Mining ◽  
Exact Solution ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Damage Identification ◽  
Optimization Problems ◽  
Engineering Optimization ◽  
Structural Health ◽  
Structural Damage Identification

In recent years, data mining technology has been employed to solve various Structural Health Monitoring (SHM) problems as a comprehensive strategy because of its computational capability. Optimization is one the most important functions in Data mining. In an engineering optimization problem, it is not easy to find an exact solution. In this regard, evolutionary techniques have been applied as a part of procedure of achieving the exact solution. Therefore, various metaheuristic algorithms have been developed to solve a variety of engineering optimization problems in SHM. This study presents the most applicable as well as effective evolutionary techniques used in structural damage identification. To this end, a brief overview of metaheuristic techniques is discussed in this paper. Then the most applicable optimization-based algorithms in structural damage identification are presented, i.e. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA) and Ant Colony Optimization (ACO). Some related examples are also detailed in order to indicate the efficiency of these algorithms.

scholarly journals Experimental Evaluation of Miniature Impedance Board for Loosening Monitoring of the Threaded Pipe Connection

2021 ◽  
Vol 9 ◽  
Author(s):  
Yabin Liang ◽  
Yixuan Chen ◽  
Zuocai Zhang ◽  
Qian Feng
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Damage Identification ◽  
Low Cost ◽  
Impedance Measurement ◽  
Measurement Procedure ◽  
Practical Applications ◽  
Electromechanical Impedance ◽  
Structural Health

Electromechanical impedance (Electromechanical impedance)-based methods as potential nondestructive evaluation (NDT) techniques have been widely used in the field of structural health monitoring (SHM), especially for the civil, mechanical, and aerospace engineering fields. However, it is still difficult to apply in practical applications due to the limitations of the impedance measurement hardware, which is usually expensive, bulky, and heavy. In this paper, a small, lightweight, and low power consumption EMI-based structural health monitoring system combined with the low-cost miniature impedance board AD5933 was studied experimentally to investigate its quantifiable performance in impedance measurement and structural damage identification. At first, a simple impedance test with a free PZT patch was introduced to present the impedance calibration and measurement procedure of AD5933, and then its calibration performance was validated by comparing the signature with the one measured by a professional impedance analyzer (WK6500B). In order to further validate the feasibility and effectiveness of the AD5933 board in practical applications, a threaded pipe connection specimen was assembled in the laboratory and then connected with the AD5933 to acquire its impedance signatures under different loosening severities. The final results demonstrated that the impedance measured by the AD5933 show a good consistency with the measurements by the WK6500B, and the evaluation board could be successfully utilized for the loosening severities identification and quantitatively evaluation.

Experimental Identification of Structural Damage by Neural Networks

1995 ◽  
Author(s):  
Rosario Ceravolo ◽  
Alessandro De Stefano ◽  
Michel Grosjacques ◽  
Donato Sabia
Keyword(s):  
Neural Networks ◽  
Dynamic Response ◽  
Structural Damage ◽  
Dynamic Behaviour ◽  
Damage Identification ◽  
Numerical Models ◽  
Analysis Data ◽  
Simply Supported ◽  
Response Data ◽  
Experimental Identification

Abstract The problems involved in structural damage identification by means of pattern recognition neural techniques are addressed. As is known, mechanical system recognition can be achieved by making appropriate use of these connectionistic instruments. Recognition takes place on the basis of an incomplete set of data contained in the system’s dynamic response. Some methods, previously developed by the authors on the basis of numerical models, are validated through the use of noisy data. To this end, an experimental research was carried out on simply supported beams. The tests made it possible to acquire a wealth of dynamic response data from beams damaged artificially to produce defects of varying entity and position. The data obtained, appropriately normalised, are used as inputs for supervised neural networks. In particular, frequency analysis data are able to provide a summary characterisation of the distortion in a structure’s dynamic behaviour. Different signal processing and analysis procedures are compared in order to identify the best resolution and sensitivity capabilities in the diagnostic performance of neural networks.

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.

Sparse damage detection via the elastic net method using modal data

2021 ◽  
pp. 147592172110219
Author(s):  
Rongrong Hou ◽  
Xiaoyou Wang ◽  
Yong Xia
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Damage Identification ◽  
Measurement Data ◽  
Elastic Net ◽  
Sensitivity Matrix ◽  
Regularization Technique ◽  
Modal Data ◽  
Regularization Techniques ◽  
Net Method

The l1 regularization technique has been developed for damage detection by utilizing the sparsity feature of structural damage. However, the sensitivity matrix in the damage identification exhibits a strong correlation structure, which does not suffice the independency criteria of the l1 regularization technique. This study employs the elastic net method to solve the problem by combining the l1 and l2 regularization techniques. Moreover, the proposed method enables the grouped structural damage being identified simultaneously, whereas the l1 regularization cannot. A numerical cantilever beam and an experimental three-story frame are utilized to demonstrate the effectiveness of the proposed method. The results showed that the proposed method is able to accurately locate and quantify the single and multiple damages, even when the number of measurement data is much less than the number of elements. In particular, the present elastic net technique can detect the grouped damaged elements accurately, whilst the l1 regularization method cannot.

Sign in / Sign up

Export Citation Format

Share Document