Damage Identification in Collocated Structural Systems Using Structural Markov Parameters

Author(s):  
Ramin Bighamian ◽  
Hamid Reza Mirdamadi ◽  
Jin-Oh Hahn

This paper presents a novel approach to damage identification in a class of collocated multi-input multi-output structural systems. In the proposed approach, damage is identified via the structural Markov parameters obtained from a system identification procedure, which is in turn exploited to localize and quantify damage by evaluating relative changes occurring in the mass and stiffness matrices associated with the structural system. To this aim, an explicit relationship between structural Markov parameters versus mass and stiffness matrices is developed. The main strengths of the proposed approach are that it is capable of quantitatively identifying the occurrence of multiple damages associated with both mass and stiffness characteristics in the structural system, and it is computationally efficient in that it is solely based on the structural Markov parameters but does not necessitate costly calculations related to natural frequencies and mode shapes, making it highly attractive for structural damage detection and health monitoring applications. Numerical examples are provided to demonstrate the validity and effectiveness of the proposed approach.

Author(s):  
Ramin Bighamian ◽  
Hamid Reza Mirdamadi ◽  
Jin-Oh Hahn

This paper presents a novel approach to damage identification in a class of collocated multi-input multi-output structural systems. In the proposed approach, damage is identified via the structural Markov parameters obtained from a system identification procedure, which is in turn exploited to localize and quantify damage by evaluating relative changes occurring in the mass and stiffness matrices associated with the structural system. To this aim, an explicit relationship between structural Markov parameters versus mass and stiffness matrices is developed. The main strengths of the proposed approach are that it is capable of quantitatively identifying the occurrence of multiple damages associated with both mass and stiffness characteristics in the structural system, and it is computationally efficient in that it is solely based on the structural Markov parameters but does not necessitate costly calculations related to natural frequencies and mode shapes, making it highly attractive for structural damage detection and health monitoring applications. Numerical examples are provided to demonstrate the validity and effectiveness of the proposed approach.


2013 ◽  
Vol 13 (08) ◽  
pp. 1350043 ◽  
Author(s):  
V. SRINIVAS ◽  
C. ANTONY JEYASEHAR ◽  
K. RAMANJANEYULU

In the present work, computational methodologies based on artificial neural networks and genetic algorithms (GA) have been developed for identification of structural damage utilizing vibration data. The natural frequencies and mode shapes obtained from the finite element analysis for the first few modes have been considered for this purpose. A multi-stage hybrid methodology combining the modal strain energy criteria with GA has also been proposed, which showed improved damage identification capability as compared to the conventional GA, and proved to be computationally efficient. To demonstrate the efficiency of the proposed hybrid approach, numerical studies have been carried out on the truss structure. The efficacy of mode shape expansion in conjunction with GA is demonstrated for damage identification of reinforced concrete beam based on experimental modal data.


2016 ◽  
Vol 16 (1) ◽  
pp. 3-23 ◽  
Author(s):  
Yongfeng Xu ◽  
Weidong Zhu

Mode shapes (MSs) have been extensively used to detect structural damage. This paper presents a new non-model-based damage identification method that uses measured MSs to identify damage in plates. A MS damage index (MSDI) is proposed to identify damage near regions with consistently high values of MSDIs associated with MSs of different modes. A MS of a pseudo-undamaged plate can be constructed for damage identification using a polynomial of a properly determined order that fits the corresponding MS of a damaged plate, if the associated undamaged plate is geometrically smooth and made of materials that have no stiffness and mass discontinuities. It is shown that comparing a MS of a damaged plate with that of a pseudo-undamaged plate is better for damage identification than with that of an undamaged plate. Effectiveness and robustness of the proposed method for identifying damage of different positions and areas are numerically investigated using different MSs; effects of crucial factors that determine effectiveness of the proposed method are also numerically investigated. Damage in the form of a machined thickness reduction area was introduced to an aluminum plate; it was successfully identified by the proposed method using measured MSs of the damaged plate.


1997 ◽  
Vol 503 ◽  
Author(s):  
H. P. Chen ◽  
N. Bicanic

ABSTRACTA novel procedure for damage identification of continuum structures is proposed, where both the location and the extent of structural damage in continuum structures can be correctly determined using only a limited amount of measurements of incomplete modal data. On the basis of the exact relationship between the changes of structural parameters and modal parameters, a computational technique based on direct iteration and directly using incomplete modal data is developed to determine damage in structure. Structural damage is assumed to be associated ith a proportional (scalar) reduction of the original element stiffness matrices, equivalent to a scalar reduction of the material modulus, which characterises at Gauss point level. Finally, numerical examples for plane stress problem and plate bending problem are utilised to demonstrate the effectiveness of the proposed approach.


2009 ◽  
Vol 24 (3) ◽  
pp. 153-159 ◽  
Author(s):  
Q. W. Yang

Structural damage identification using ambient vibration modes has become a very important research area in recent years. The main issue surrounding the use of ambient vibration modes is the mass normalization of the measured mode shapes. This paper presents a promising approach that extends the flexibility sensitivity technique to tackle the ambient vibration case. By introducing the mass normalization factors, manipulating the flexibility sensitivity equation, the unknown damage parameters and mass normalization factors can be computed simultaneously by the least-square technique. The effectiveness of the proposed method is illustrated using simulated data with measurement noise on three examples. It has been shown that the proposed procedure is simple to implement and may be useful for structural damage identification under ambient vibration case.


2020 ◽  
Vol 10 (8) ◽  
pp. 2869 ◽  
Author(s):  
Zhenpeng Wang ◽  
Minshui Huang ◽  
Jianfeng Gu

To study the variations in modal properties of a reinforced concrete (RC) slab (such as natural frequencies, mode shapes and damping ratios) under the influence of ambient temperature, a laboratory RC slab is monitored for over a year, the simple linear regression (LR) and autoregressive with exogenous input (ARX) models between temperature and frequencies are established and validated, and a damage identification based on particle swarm optimization (PSO) is utilized to detect the assumed damage considering temperature effects. Firstly, the vibration testing is performed for one year and the variations of natural frequencies, mode shapes and damping ratios under different ambient temperatures are analyzed. The obtained results show that the change of ambient temperature causes a major change of natural frequencies, which, on the contrary, has little effect on damping ratios and modal shapes. Secondly, based on a theoretical derivation analysis of natural frequency, the models are determined from experimental data on the healthy structure, and the functional relationship between temperature and elastic modulus is obtained. Based on the monitoring data, the LR model and ARX model between structural elastic modulus and ambient temperature are acquired, which can be used as the baseline of future damage identification. Finally, the established ARX model is validated based on a PSO algorithm and new data from the assumed 5% uniform damage and 10% uniform damage are compared with the models. If the eigenfrequency exceeds the certain confidence interval of the ARX model, there is probably another cause that drives the eigenfrequency variations, such as structural damage. Based on the constructed ARX model, the assumed damage is identified accurately.


2016 ◽  
Vol 16 (06) ◽  
pp. 1550018 ◽  
Author(s):  
S. S. Kourehli

A damage detection and estimation method is proposed for structural health monitoring using incomplete modal data and least squares support vector machine (LS-SVM). To accommodate the use of incomplete modal data, the iterated improved reduction system (IIRS) method has been used to condense the mass and stiffness matrices of the structure. The first two incomplete mode shapes and natural frequencies of a damaged structure are used as input data to the LS-SVM. The coupled simulated annealing (CSA) and standard simplex method using 10-fold cross-validation techniques are adopted to determine the optimal tuning parameters in the LS-SVM model. Three illustrative examples with and without noise in modal data are prepared to evaluate the performance of the proposed method. The results indicated that this method can be reliably used to identify the damages of structures with good accuracy.


2020 ◽  
pp. 147592172092697
Author(s):  
Zhao Chen ◽  
Hao Sun

Identifying damage of structural systems is typically characterized as an inverse problem which might be ill-conditioned due to aleatory and epistemic uncertainties induced by measurement noise and modeling error. Sparse representation can be used to perform inverse analysis for the case of sparse damage. In this article, we propose a novel two-stage sensitivity analysis–based framework for both model updating and sparse damage identification. Specifically, an [Formula: see text] Bayesian learning method is first developed for updating the intact model and uncertainty quantification so as to set forward a baseline for damage detection. A sparse representation pipeline built on a quasi-[Formula: see text] method, for example, sequential threshold least squares regression, is then presented for damage localization and quantification. In addition, Bayesian optimization together with cross-validation is developed to heuristically learn hyperparameters from data, which saves the computational cost of hyperparameter tuning and produces more reliable identification result. The proposed framework is verified by three examples, including a 10-story shear-type building, a complex truss structure, and a shake-table test of an eight-story steel frame. Results show that the proposed approach is capable of both localizing and quantifying structural damage with high accuracy.


2021 ◽  
Vol 95 (3) ◽  
pp. 76-108
Author(s):  
N.V. FEDOROVA ◽  
◽  
S.YU. SAVIN ◽  

During the entire life cycle, the facilities are experienced to force and environmental actions of various nature and intensity. In some cases, such influences can lead to a loss of the bearing capacity of the structural elements of a building, which in turn can lead to a disproportionate failure of the entire structural system. Such phenomenon was called progressive collapse. Major accidents at facilities, such as the collapse of a section of the Ronan Point high-rise residential building (London, 1968), the Sampoong department store (Seoul, 1995), the Transvaal Park pavement (Moscow, 2004), the World Trade Center (New York, 2011) and others, clearly demonstrated the urgency of this problem. In this regard, the regulatory documents of the USA, Great Britain, EU, China, Australia, Russia and other countries established requirements for the need to calculate structural systems of buildings for resist to progressive collapse after sudden localized structural damage. However, the steady increase in the number of new publications on the problem of progressive collapse observed in the world scientific literature indicates that the results of such studies do not yet provide exhaustive answers to all questions related to this phenomenon. In this regard, the proposed review article is aimed at systematizing, generalizing and analyzing new research results on resistance to progressive collapse of facilities, identifying new trends and proposing new research directions and tasks to improve the level of structural safety of design solutions for buildings and structures. In order to achieve this goal, the following aspects were considered: the nature of the impacts leading to progressive collapse; features of modeling the progressive collapse of structural systems of buildings and structures; mechanisms of resistance to progressive collapse and criteria for evaluation of a progressive collapse resistance. Particular attention in the scientific review is paid to the analysis of works related to a new direction of research in the area under consideration, associated with the assessment of the bearing capacity of eccentrically compressed elements of structural systems, the effect on their resistance to progressive collapse of the parameters of the loading mode, degradation of material properties and the topology of the structural system. The significance of the proposed scientific review is that, along with the well-known and new results presented in the English-language scientific literature, it summarizes and analyzes the original approaches, methods and research results published in Russian-language scientific publications, primarily included in the RSCI Web of Science.


2019 ◽  
Vol 9 (1) ◽  
pp. 2-14 ◽  
Author(s):  
Hao Zhou ◽  
Ehsan Rezazadeh Azar

Purpose Steel and reinforced concrete are among the most common structural materials used in the construction industry. Cost and the speed of construction have been usually the main criteria when selecting a building’s structural system, whereby the environmental impact of the structural material is sometimes ignored. Availability of an easy-to-use tool for environmental assessment of the structural alternatives could encourage this evaluation in the decision making. The purpose of this paper is to introduce an automated tool for the environmental assessment of the on-site construction processes of a building structural system, which calculates the energy consumption and carbon emissions of the structural system as a parameter for comparison. Design/methodology/approach This assessment tool is implemented using a building information modeling (BIM) platform to extract structural elements and their key attributes, such as type, geometrical and locational data. These data are processed together with a productivity database to calculate machine hours, and then predefined energy and carbon inventories are used to assess the energy consumption of the structural system in the erection/installation stage. Findings This assessment tool provides an automated and easy-to-use approach to estimate energy consumption and carbon emissions of different structural systems that are modeled in a BIM platform. The results of this tool were within the ranges reported by the available studies. Originality/value This research project presents a novel approach to use BIM-based attributes of the structural elements to calculate the required efforts, i.e. machine hours, and assess their energy consumption and carbon emissions during construction processes.


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