Damage In Structural Systems Research Articles

Performance-based active controller design for nonlinear structures using modified black hole optimization

This paper presents a novel approach that facilitates the design of active controllers to mitigate seismically induced damage in structural systems. The proposed method is based on stochastic Modified Black Hole optimization algorithm. Two traditional controllers, namely Proportional-Integral-Derivative (PID) and Linear–Quadratic Gaussian (LQG) controllers were designed, and their performance was demonstrated on a benchmark 20-story steel-framed building. Evaluation criteria were defined to satisfy constraints on various response quantities, including drift, base shear, ductility, residual story drift, and control force. The constraint limits were defined in view of performance-based design requirements for the benchmark structure. The performance of the controllers was contrasted with that of traditional LQG, and significant reductions of all response quantities were achieved for design-level earthquakes.

Analytical Modelling of T-Shaped Monolithic & Independent RCC Buildings

Abstract: During an earthquake the behaviour of the building depends on its overall shape, size & geometry. Now a day’s many buildings are asymmetric in plan and elevation because everyone wants to win the race of aesthetically beautiful and complex structures. Due to irregular distribution of mass, stiffness and strength, it may cause serious damage in structural system. There are various types of irregularities in the buildings depending upon their location and scope. Mainly two types of irregularities as per IS – 1893 (Part-1)-2002. are a) plan or horizontal irregularities b) vertical irregularities. Irregularity in structure makes analysis of the seismic behaviour very complicated. The objective of present study is to analyze and compare the behaviour of T shape monolithic and independent RCC structure under seismic loading. The comparison of both structures is studied by calculating, finding and tabulating comparative values of displacement, base moments and shear values. The study reflects that with change in structure i.e. the behaviour of structure towards earthquake changes, nodal displacement, moment, and base shear values shows drastic changes towards resistivity against seismic forces. The soft computing tool and commercial software STAAD-Pro is used for modelling and analysis also the study done over here thus helps to understand effect of earthquake on both structures for achieving stable and safe structure

Effective medium crack classification on laboratory concrete specimens via competitive machine learning

With the advent rise of automation, it is now possible to trace and detect damage in structural systems with ease. Unfortunately, existing inspection methods continue to suffer on a number of fronts; i.e., laborious, costly, and mediocre performance. In order to bridge this knowledge gap, this work aims to develop an autonomous and open-source deep learning (DL) approach capable of detecting varying scales of damage (i.e., micro and macro cracks) in concrete structures. In pursuit to fine tune and optimize this approach, we carried out an investigation to examine the influence of DL various architectures, network depths, regularization techniques, and transfer learning methods. Based on our investigation, we proposed a new transfer learning method to improve the accuracy of a micro-and macro-crack DL classifier. The deployed DL architecture is based on a much improved VGG-16 model and achieved an accuracy of 99.5%, and an F1-score of 100% when examined against a comprehensive dataset and an accompanying physical testing program.In an effort to facilitate the adoption of our approach, -this newly developed DL classifier (including its codes, data, etc.) is freely available for use and download.

Research on damage diagnosis for civil engineering structures

In the construction of such an important basic engineering project as civil engineering, the main problem faced by constructors is the structural system damage. Therefore, scientific diagnostic methods are needed to find out locations and causes of the damages in time. For this reason, people have carried out in-depth analysis according to the system form and composition mode of civil structure, and have developed a variety of scientific and reliable diagnostic methods combined with its related damage problems. With the rapid development of diagnostic technology, inspectors have a clearer understanding and knowledge of all kinds of damage problems, and can also use a variety of diagnostic methods reasonably to find out.

Insight to Damage Identification in Truss-Type Structures Using a Second-Order Gradient-Based Algorithm

The aim of structural health monitoring is to detect damage in structural systems in order to timely repair, maintain and prevent structural damage at the earliest possible time. Given the increasing number of these damages, developing new and applied methods to identify failures leading to such damages is very necessary. The purpose of this present study is to develop effective and new method according to updating structures limited element model to solve the nonlinear problem of large-scale truss-type structures damage detection with high number of elements in which in the formed equations system to determine the vector of damage, the number of equations outnumbers the number of unknowns. The proposed technique has been carried out by the aid of one of the most effective numerical optimization methods among algorithms based on gradient called second-order Levenberg–Marquardt algorithm. Also in this study, by using a proposed method based on sensitivity analysis, a linear solution is presented for the problem of nonlinear damage detection. By using structural acceleration response resulted by time history excitation obtained from nodes with the sensor, the structure damage detection will be done by an algorithm during an iterative cycle and updating the sensitivity matrix and the location and extent of damages are obtained in the presence of different noise level or imperfect input data. The damage has been supposed to be a linear reduction in the modulus of elasticity. The obtained results demonstrate the appropriate performance and accuracy of the proposed damage detection method.

Research on damage diagnosis for civil engineering structures

<p>In the construction of such an important basic engineering project as civil engineering, the main problem faced by constructors is the structural system damage. Therefore, scientific diagnostic methods are needed to find out locations and causes of the damages in time. For this reason, people have carried out in-depth analysis according to the system form and composition mode of civil structure, and have developed a variety of scientific and reliable diagnostic methods combined with its related damage problems. With the rapid development of diagnostic technology, inspectors have a clearer understanding and knowledge of all kinds of damage problems, and can also use a variety of diagnostic methods reasonably to find out.</p>

Experimental validation of the proposed technique for condition monitoring of structure using limited noisy modal data

PurposeCondition monitoring (CM) of structures is important from safety consideration. Damage detection techniques, using inverse dynamic approaches, are important tools to improve the mathematical models for monitoring the condition of structure. Uncertainties in the measured data might lead to unreliable identification of damage in structural system. Experimental validation is crucial for establishing its practical applicability. The measurement of dynamic responses at all degrees of freedom (DOFs) of a structure is also not feasible in practice. In addition the effect of these uncertainties and constraint of limited measurement are required to be studied based on experimental validation. This paper aims to discuss these issues.Design/methodology/approachProposed numerical model based on measured natural frequencies and mode shapes is found suitable for CM of framed structures in the framework of finite element model with limited dynamic responses. The structural properties, namely, axial rigidity and bending rigidity are identified at the element level in the updated models of the system. Damage at the element level is identified by comparing the identified structural parameters of the updated model of the system with those of the undamaged state. Proposed numerical model is suitable for practical problem, as it is able to identify the structural parameters with limited modal data of first few modes, measured at selected DOFs.FindingsThe model is able to identify the structural damage with greater accuracy from the noisy dynamic responses even if the extent of damage is small. Experimental studies, on simple cantilever beams, establish the potential of the proposed methods for its practical implementation.Research limitations/implicationsThe greater random noise will lead to unreliable identification of structural parameters as observed. Thus, filtering of noise technique may be required to be adopted prior to consideration of the measured data in the proposed identification approach.Practical implicationsRequirement of higher modal data seems to be difficult in case of real life practical problem. Thus, simulation technique like condensation or SEREP technique may be adopted.Social implicationsStructural health monitoring of infrastructural system is significantly important. CM of those structures from global response with limited measured data seems to be an effective tool to ensure safety and durability of structures.Originality/valueThe modal testing and subsequent extraction of modal data have been carried out at the authors’ laboratory. The numerical code based on inverse dynamic approach has been developed independently with original contribution.

Extended constitutive relation error‐based approach: The role of mass in damage detection

This paper presents an extended constitutive relation error (ECRE)-based method for detecting damage in structural systems. Although most ECRE-based methods for damage detection have relied on an iterative model calibration process, the approach advocated in this study uses residual energy to identify structural damage. Residual energy represents the spatial distribution of discrepancies between model predictions of the undamaged system and experimental measurements obtained from the damaged system. The current study calculates this residual energy by tightly linking experimentally measured vibration data to an associated numerical model. The approach implemented in this paper, referred to as the M-K ECRE-based approach, differs from previous ECRE-based damage detection methods in that it incorporates both unbalanced elastic and unbalanced inertial forces in the calculation of residual energy. The rationale for including unbalanced inertial forces is that damage can also alter the mass distribution of a structure in addition to its influence on the distribution of structural stiffness. By considering unbalanced inertial forces, the proposed approach improves the calculation of residual energy, which in turn leads to an improvement in the method's ability to identify damage. At the end of this study, the efficiency of the M-K ECRE-based approach to identify damage is demonstrated on a scaled two-story steel frame that is subjected to varying types and severities of structural damage.

Damage detection in structural systems by improved sensitivity of modal strain energy and Tikhonov regularization method

In this article, new methods for detecting damage in structural systems are presented. These methods are categorized as damage localization and damage quantification, respectively. Hence, direct changes of modal strain energy are applied to identify locations of damage. Moreover, some restraints such as incomplete measured modes and simple assumptions in structural modeling may cause failure in the results of damage localization. Therefore, a correlation-based method is utilized to obviate these limitations and precisely detect damage sites. Subsequently, an improved sensitivity of modal strain energy is generated to determine damage severities. To achieve appropriate results in damage quantification, Tikhonov regularization approach is utilized instead of classical methods such as applying penalty function and current inverse problem techniques. Applicability and effectiveness of proposed methods are numerically verified using two practical examples consisting of a planner truss and a portal frame, respectively. Eventually, numerical results indicate that the proposed damage localization approach provides an influential algorithm for precisely identifying damage sites. Furthermore, obtained damage severities show that utilizing the sensitivity of modal strain energy and also solving the damage equation by Tikhonov regularization makes it possible to accurately determine damage extents in the case of incomplete modal data.

Seismic Performance Evaluation of R c-Framed Buildings - An Approach To Torsionally Asymmetric Buildings

At present scenario many buildings are asymmetric in plan and/or in elevation based on the distribution of mass and stiffness along each storey throughout the height of the building. Most recent earthquakes have shown that the irregular distribution of mass, stiffness and strengths may cause serious damage in structural systems. This research quantifies the performance of the torsionally balanced and torsionally unbalanced buildings also called as symmetric and asymmetric buildings by subjecting to pushover analysis. The buildings have un-symmetrical distribution of stiffness in storeys. In this paper the effort is made to study the effect of eccentricity between centre of mass (CM) and centre of stiffness (CR) and the effect of stiffness of infill walls on the performance of the buildings. The performance of the buildings is assessed as per the procedure prescribed in ATC-40 and FEMA-356. Four building models are considered for study, which are constructed on hard soil in seismic zone Ш of India (as per IS: 1893-2002(9)), one symmetric and 3 asymmetric in stiffness distribution. Infills were modeled using equivalent strut approach. Static analysis (for gravity and lateral loads) and non-linear pushover analysis (assigning the hinge properties to beams and column sections) were performed. It is concluded that the performance of the models in which the stiffness of walls considered is found better when compared with the models in which the stiffness of walls ignored. And with increase in eccentricity, the performance point of the structure will be more but due to increase in stiffness, structure may fail in brittleness.

Damage detection under ambient vibration by harmony search algorithm

Damage in structural systems induced by vibrations, alternating load cycles, temperature changes, corrosion, etc., constitute a serious technical problem. Smart methods of control and structural health monitoring (SHM) for large structures are, therefore, highly needed. In certain structural applications, moreover, a lack of access to the damaged area imposes an additional constraint on damage identification procedures. One method that may fulfill those requirements is dynamic nondestructive testing, which consists of monitoring changes in the structure’s natural frequencies, vibration modes and damping.In this paper, a new approach for vibration-based (SHM) procedures is presented, in an ambient vibration context; this method combines a time domain modal identification technique (SSI) with the evolutionary harmony search algorithm. A series of numerical examples with different damage scenarios and noise levels have been carried out under impact and ambient vibration. Thereafter, an experimental study of three cantilever beams with several different damage scenarios is conducted and the proposed methodology has shown potential for use in the damage diagnosis assessment of the remaining structural life.

Bayesian identification of a cracked plate using a population-based Markov Chain Monte Carlo method

Estimating damage in structural systems is a challenging problem due to the complexity of the likelihood function describing the observed data. From a Bayesian perspective a complicated likelihood means efficient sampling of the posterior distribution is difficult and standard Markov Chain Monte Carlo samplers may no longer be sufficient. This work describes a population-based Markov Chain Monte Carlo approach for efficient sampling of the damage parameter posterior distributions. The approach is shown to accurately estimate the state of damage in a cracked plate structure using simulated, free-decay response data. The use of this approach in identifying structural damage has not previously been explored.

Structural damage detection using an efficient correlation-based index and a modified genetic algorithm

An efficient optimization procedure is proposed to detect multiple damage in structural systems. Natural frequency changes of a structure are considered as a criterion for damage presence. In order to evaluate the required natural frequencies, a finite element analysis (FEA) is utilized. A modified genetic algorithm (MGA) with two new operators (health and simulator operators) is presented to accurately detect the locations and extent of the eventual damage. An efficient correlation-based index (ECBI) as the objective function for the optimization algorithm is also introduced. The numerical results of two benchmark examples considering the measurement noise demonstrate the computational advantages of the proposed method to precisely determine the sites and the extent of multiple structural damage.

Improving the Performance of Structural Damage Detection Methods Using Advanced Genetic Algorithms

A frequency response function-based damage identification method is presented that accurately identifies both the location and severity of damage in structural systems using a limited amount of measurement information. Damage is identified by minimizing the error between measured and analytically computed frequency response functions obtained through finite element model updating. The impact that the type of genetic algorithm representation has on performance is evaluated for a fixed representation and an implicit redundant representation, which simplifies the search by exploiting the unstructured nature of damage identification. The performance of the proposed damage identification method is evaluated for beam and frame structures that consider different damage scenarios and measurement layouts. The impact of measurement noise on performance is also investigated. The damage identification method developed using the implicit redundant genetic algorithm provides greater accuracy in identifying the location and severity of damage in all case studies even in the presence of noise. For larger frame structures, the implicit redundant genetic algorithm performed well, while no valid results were obtained using the fixed genetic algorithm representation.

Detecting damage through the processing of dynamic shapes measured by a PSD-triangular laser sensor

There exist several studies in the literature which have shown the potentiality offered by certain vibration-based techniques aimed at detecting damage in structural systems. In all these existing techniques noise (distributed and/or outliers) plays a significant role and can make the difference between a successful or an unsuccessful application. In spite of such a mentioned remarkable influence, the studies aimed at investigating the influence of noise on the success of the techniques are not as rich in experimental details as they are in numerical simulations. In this work an extensive set of experiments aimed at evaluating the feasibility of certain diagnosing techniques is provided. This work should also be considered as the experimental validation of certain analytical and numerical simulations carried out in the past [Gentile, A., Messina, A., 2003. On the continuous wavelet transforms applied to discrete vibrational data for detecting open cracks in damaged beams. International Journal of Solids and Structures 40, 295–315; Messina, A., 2004. Detecting damage in beams through digital differentiator filters and continuous wavelet transforms. Journal of Sound and Vibration 272, 385–412] within the frame of real measurements based on a particular laser technology; in addition, the mentioned validating experiments illustrate certain peculiarities not shown in the past, and, finally, valuable benchmarks are provided for testing future diagnosing techniques in numerical simulations. The experimental set-up consists of both commercial and electronic circuits appropriately designed and realized, whose significance, in the measuring system is accurately described in order to increase the signal/noise ratio of the dynamical measurements.

Embedment of structural monitoring algorithms in a wireless sensing unit

Complementing recent advances made in the field of structural health monitoring and damage detection, the concept of a wireless sensing network with distributed computational power is proposed. The fundamental building block of the proposed sensing network is a wireless sensing unit capable of acquiring measurement data, interrogating the data and transmitting the data in real time. The computational core of a prototype wireless sensing unit can potentially be utilized for execution of embedded engineering analyses such as damage detection and system identification. To illustrate the computational capabilities of the proposed wireless sensing unit, the fast Fourier transform and auto- regressive time-series modeling are locally executed by the unit. Fast Fourier transforms and auto- regressive models are two important techniques that have been previously used for the identification of damage in structural systems. Their embedment illustrates the computational capabilities of the prototype wireless sensing unit and suggests strong potential for unit installation in automated structural health monitoring systems.

On‐line identification of non‐linear hysteretic structural systems using a variable trace approach

AbstractIn this paper, an adaptive on‐line parametric identification algorithm based on the variable trace approach is presented for the identification of non‐linear hysteretic structures. At each time step, this recursive least‐square‐based algorithm upgrades the diagonal elements of the adaptation gain matrix by comparing the values of estimated parameters between two consecutive time steps. Such an approach will enforce a smooth convergence of the parameter values, a fast tracking of the parameter changes and will remain adaptive as time progresses. The effectiveness and efficiency of the proposed algorithm is shown by considering the effects of excitation amplitude, of the measurement units, of larger sampling time interval and of measurement noise. The cases of exact‐, under‐, over‐parameterization of the structural model have been analysed. The proposed algorithm is also quite effective in identifying time‐varying structural parameters to simulate cumulative damage in structural systems. Copyright © 2001 John Wiley & Sons, Ltd.

Damage Detection in Structures by Modal Vibration Characterization

A nondestructive methodology is presented for detecting structural damage in structural systems. The procedure is based on using experimentally measured modes and frequencies in conjunction with vibratory residual forces and a weighted sensitivity analysis to estimate the extent of mass and/or stiffness variations in a structural system. Determination of the residual forces and weighted sensitivity analysis involves the use of an analytical model that is correlated to the experimental baseline data from a reference state. This reference state defines the undamaged structural configuration. The method is demonstrated by using a ten-bay space truss as an experimental test bed for various damage scenarios. The experimental results show that the method can accurately predict the location and severity of stiffness change as well as any change in mass for different damage scenarios. The use of an analytical model that is correlated to the baseline test data is shown to improve the prediction; however, reasonable results are also obtained using an uncorrelated analytical model.

Dynamic characterization of a long span bridge: A finite element based approach

Aging bridges coupled with increasing traffic loads are producing a severe toll on the nation's infrastructure. This has made it necessary to take a closer look at the health of existing bridges and develop automated damage identification methods if possible. Recent works in the field of structural dynamics have shown that damage detection techniques utilizing parameters like mode shapes, modal frequencies and damping ratios can be used to identify damage in structural systems. It is, however, important to be able to establish a baseline model for the structure first, and then a model updating technique can be utilized to evaluate the condition of the structure from time to time. It is with this goal in mind that the authors have decided to establish the process for obtaining a baseline model for a long span bridge. Based on the actual design drawings of a bridge, finite element (FE) models of the bridge in question are developed using SDRC-IDEAS. Three models of the bridge are simulated using Normal Mode Dynamics solver in SDRC-IDEAS to obtain the modal parameters of interest, in this case the modal frequencies and the mode shapes. A modal assurance criteria (MAC) is utilized to compare the different simulated mode shapes and, finally, the modal frequencies that have been obtained from the FE analysis are compared to frequencies that have been obtained from some preliminary field tests.

Damage Detection in Structures Based on Feature‐Sensitive Neural Networks

Detection of damage in structural systems is formulated as an inverse problem and solved by a new approach utilizing neural networks. Damage is modeled through reduction in the stiffness of structural elements, and manifests itself in the form of variations in observable static displacements under prescribed loads. A modified counterpropagation neural network is used to develop the inverse mapping between a vector of the stiffness of individual structural elements and the vector of the global static displacements under a testing load. It is shown that the network functions as an associative memory device capable of satisfactory diagnostics even in the presence of noisy or incomplete measurements. Numerical examples involving frame and truss structures show that the network approximations are fully acceptable from a practical standpoint.