Substructure Identification Method Research Articles

Seismic Damage Assessment for Isolated Buildings with a Substructure Method

A seismic damage detection method for isolated buildings is proposed based on substructure identification with incomplete contaminated measurements. A concept of a pseudo substructure with virtual conditions is constructed for the proof of the proposed substructure identification method. This identification method is implemented in a two-stage procedure. The interface forces of the target substructure are identified in the first stage and the parameter of the target substructure is updated in the second stage, which can enable the parameter identification of substructures with unknown input. Two computational methods are also proposed to improve the two-stage identification algorithm. A sub-time zone identification method is utilized to reduce the computation effort and the simultaneous identification of the unknown force and initial structural responses is presented in the first-stage identification for a general case in practical engineering. Numerical studies of a shear frame with nonlinear base isolation subject to earthquake ground motion are investigated to validate the proposed seismic damage detection method. A fourteen-storey concrete shear wall building with a two-storey steel frame on top connected by isolation is studied experimentally with shaking table tests to further validate the proposed method. The shear wall structure is taken as the target substructure for damage assessment. The interface force and parameter of the concrete shear wall building are estimated with the proposed method. Results from both the numerical simulations and laboratory tests indicate that the proposed method can estimate seismic isolated structures and detect damage effectively based on only a few accelerometers. It is also demonstrated that the parameter identification results based on the structure response measurement during the earthquake are more accurate than the identification with post-earthquake structural response measurement.

Multi-taper method based substructure identification for shear structures

Quickly and accurately identifying structural status after natural disasters plays crucial roles in disaster rescue. Previously, the authors developed a substructure identification method for shear structures, which uses the frequency responses of short structural acceleration responses to estimate structural parameters inductively. However, the numerical studies found that the method could only provide moderately accurate results. In this paper, a thorough uncertainty analysis is performed to reveal the key factors that influence its identification accuracy. Based on these results, a new substructure method is proposed herein, which utilizes the cross power spectrum densities of structural responses, estimated by the multi-taper method, to formulate substructure identification problems. The error analysis is also conducted for the multi-taper method based method, explaining why this method can significantly improve identification accuracy, compared with the frequency response based method. Moreover, although the multi-taper method based method is originally derived based on stationary structural responses, a further analysis shows that it can be extended to non-stationary responses, greatly broadening the method’s application range. Finally, the simulation study of a 20-story shear structure and the shake table tests on a three-story bench-scaled structure are conducted, which verified that the proposed multi-taper method based method indeed significantly improves the substructure identification accuracy.

Adaptive substructure identification for shear structures with virtual control system

Abstract Improving structural identification accuracy plays a vital role in enhancing the accuracy of structural damage detection. In the previous study, the authors proposed a cross power spectrum density (CPSD) based substructure identification method for a shear structure. The CPSDs of a two-story substructure’s responses are used to formulate a recursive identification problem to estimate the structural parameters. The in-depth identification error analysis revealed that the CPSDs of two key substructure responses near the substructure frequency are critical factors to determine the identification accuracy. In this paper, a virtual control system (VCS) is introduced into the substructure identification process to improve the identification accuracy. The VCS is a self-balanced system, composed by a control device and the self-balanced forces, which offset the actions that the control device apply on the structure. The control device is combined with the structure to form a controlled structure system, which is used to replace the original structure in the substructure identification. The self-balanced forces are treated as the known excitations to the controlled structure. Although the VCS is self-balanced and does not change the original structural responses, it can adjust the dynamic characteristics of the controlled structure via tuning the VCS’s parameters, which makes the original structural responses become favorable to the identification of the controlled structure and, thus, improves the identification accuracy. To make full use of the VCS’s ability to enhance the identification accuracy, an adaptive design procedure is proposed to obtain the optimal parameters of the VCS. Furthermore, a numerical example of an eight-story shear structure is used to demonstrate the effectiveness of the VCS to improve the identification accuracy. Finally, shake table tests are performed on a five-story bench-scaled structural model to verify experimentally the efficacy of the proposed method.

Improving substructure identification accuracy of shear structures using virtual control system

Substructure identification is a powerful tool to identify the parameters of a complex structure. Previously, the authors developed an inductive substructure identification method for shear structures. The identification error analysis showed that the identification accuracy of this method is significantly influenced by the magnitudes of two key structural responses near a certain frequency; if these responses are unfavorable, the method cannot provide accurate estimation results. In this paper, a novel method is proposed to improve the substructure identification accuracy by introducing a virtual control system (VCS) into the structure. A virtual control system is a self-balanced system, which consists of some control devices and a set of self-balanced forces. The self-balanced forces counterbalance the forces that the control devices apply on the structure. The control devices are combined with the structure to form a controlled structure used to replace the original structure in the substructure identification; and the self-balance forces are treated as known external excitations to the controlled structure. By optimally tuning the VCS’s parameters, the dynamic characteristics of the controlled structure can be changed such that the original structural responses become more favorable for the substructure identification and, thus, the identification accuracy is improved. A numerical example of 6-story shear structure is utilized to verify the effectiveness of the VCS based controlled substructure identification method. Finally, shake table tests are conducted on a 3-story structural model to verify the efficacy of the VCS to enhance the identification accuracy of the structural parameters.

Loop substructure identification for shear structures of unknown structural mass using synthesized references

Shear structure is widely adopted to model the dynamics for building structures. Thus, developing accurate and efficient parameter identification methods for a shear structure plays a crucial role in structural health monitoring for building structures. In the authors’ previous studies, a loop substructure identification (LSI) method was proposed for shear structures, in which the dynamic equation of a two-story substructure is utilized to establish a loop identification sequence, directly estimating the substructure’s parameters. To ensure the convergence of the loop identification sequence, some structural control (SC) systems are needed to change the critical structural responses in a favorable way. However, the limited availability of SC systems in general buildings restrains the wide application of this controlled loop substructure identification (CLSI) method. In this paper, the original LSI method is first reformed, which can perform the LSI even if the structural mass is unknown. Then, a new reference selection method is proposed to make the LSI method converge without the help of SC systems. Different synthesized reference responses, formed by a weighted linear combination of measured structural responses, are used to formulate the LSI sequence. Next, an optimization strategy is proposed to determine the optimal weighting factors of the synthesized reference responses, which makes the LSI converged. Finally, the LSI is carried out for a 20-story shear structure, the identification results of which show that the proposed LSI method, combined with the proposed reference selection method, can very accurately estimate the substructure parameters of the shear structure.

Computational derivation of structural alerts from large toxicology data sets.

Structural alerts have been one of the backbones of computational toxicology and have applications in many areas including cosmetic, environmental, and pharmaceutical toxicology. The development of structural alerts has always involved a manual analysis of existing data related to a relevant end point followed by the determination of substructures that appear to be related to a specific outcome. The substructures are then analyzed for their utility in posterior validation studies, which at times have stretched over years or even decades. With higher throughput methods now being employed in many areas of toxicology, data sets are growing at an unprecedented rate. This growth has made manual analysis of data sets impractical in many cases. This report outlines a fully automatic method that highlights significant substructures for toxicologically important data sets. The method identifies important substructures by computationally breaking chemical structures into fragments and analyzing those fragments for their contribution to the given activity by the calculation of a p-value and a substructure accuracy. The method is intended to aid the expert in locating and analyzing alerts by automatic retrieval of alerts or by enhancing existing alerts. The method has been applied to a data set of AMES mutagenicity results and compared to the substructures generated by manual curation of this same data set as well as another computationally based substructure identification method. The results show that this method can retrieve significant substructures quickly, that the substructures are comparable and in some cases superior to those derived from manual curation, that the substructures found covers all previously known substructures, and that they can be used to make reasonably accurate predictions of AMES activity.

Substructure identification for plane frame building structures

In previous studies (Zhang and Johnson, 2012, 2013) [1,2], the authors proposed a substructure identification method for shear structures. This method is extended herein to accommodate more complicated plane frame building structures. Via a model reduction technique, a multi-story multi-bay plane frame is converted into a beam-like model, having one translational and two rotational degrees-of-freedom (DOFs) for each floor. Based on this model, an inductive substructure identification method is formulated, using the cross power spectral densities between the frame floor horizontal accelerations and a reference response, to identify the equivalent story stiffness and damping parameters of the frame from top to bottom inductively. An identification error analysis reveals how structural responses affect the identification accuracy. Exploiting this result, a reference selection rule is proposed to select the best reference response and further improve the identification accuracy. Finally, a five-story two-bay frame structure is used to demonstrate the efficacy of the proposed substructure identification, very accurately identifying the structural parameters even under the influence of large measurement noise; based on the estimated parameters, a hypothesis testing method is used to detect and locate structural damage, simulated by stiffness reduction of some columns.

Response Modification to Improve the Parameter Identification of Shear Structures: An Experimental Verification

Structural control systems are installed in structures to mitigate large structural vibration during strong earthquakes or high-speed winds. During the majority of time when no such hazards are present, the role of these control systems can be temporarily shifted to facilitate more accurate characterization of structural health. This novel use of control devices provides another benefit of installing control systems in buildings at minimum extra cost. The authors proposed, in previous studies, a substructure identification method applicable to shear structures. The error analysis proved that the identification accuracy of this method can be improved if the responses of the substructure being identified are changed in a specific way by using feedback vibration control. Exploiting these results, an alternative control algorithm is designed to improve the identification accuracy. A numerical simulation demonstrates that the new controlled identification method, using semiactive braces, can greatly improve the accuracy of structural parameter estimation. To verify experimentally that dynamic response changes can improve identification accuracy, shake table tests are performed on a two-story shear structure model with passive modifications; the results show that the structural parameters are more accurately identified with these changes than without.

Controlled loop substructure identification for shear structures

SUMMARY In this paper, a substructure identification method is proposed to estimate the parameters of any story in a shear structure by using only limited floor accelerations. A two-story standard substructure is used to divide a shear structure into substructures; two identification problems are derived for the standard substructure, each of which identifies the parameters of one story given that the parameters of the other are known. By connecting these two identification problems in a loop manner, the loop substructure identification (LSI) method formulates a sequence of estimation problems to directly identify both story parameters of the standard substructure. If the structural masses are unknown, this loop identification method can still be applied to estimate mass-normalized structural parameters as well as the relative mass distribution of the structure. An error analysis is carried out for the two identification problems, the results of which are used to derive the condition for convergence of the LSI. The result shows that the loop identification sequence is conditionally converged and some structural responses play a vital role in determining the convergence. To ensure the convergence and the subsequent accurate estimates, the controlled LSI method is proposed, which utilizes specially designed structural control systems to temporarily change the structural dynamic closed-loop responses and achieve converged accurate identification results. A five-story shear building is used to verify the convergence condition and to demonstrate that the specially designed control systems can provide the converged estimation results. Copyright © 2013 John Wiley & Sons, Ltd.

An online substructure identification method for local structural health monitoring

This paper proposes a substructure isolation method, which uses time series of measured local response for online monitoring of substructures. The proposed monitoring process consists of two key steps: construction of the isolated substructure, and its identification. The isolated substructure is an independent virtual structure, which is numerically isolated from the global structure by placing virtual supports on the interface. First, the isolated substructure is constructed by a specific linear combination of time series of its measured local responses. Then, the isolated substructure is identified using its local natural frequencies extracted from the combined responses. The substructure is assumed to be linear; the outside part of the global structure can have any characteristics. The method has no requirements on the initial state of the structure, and so the process can be carried out repetitively for online monitoring. Online isolation and monitoring is illustrated in a numerical example with a frame model, and then verified in a cantilever beam experiment.

Substructure Identification for Shear Structures with Nonstationary Structural Responses

AbstractIn previous studies by the authors, a substructure identification method for shear structures was proposed to identify all structural story stiffness and damping parameters from top to bottom inductively. In the method derivation, the structural responses were required to be wide sense stationary to convert structural dynamic equations to differential equations in the correlation functions of structural responses, which are used to formulate substructure identifications. In this paper, this method is extended to accommodate nonstationary structural responses. A different derivation procedure is adopted to formulate substructure identifications directly from the Fourier transform of the structural dynamic equations, resulting in a formulation nearly the same as its stationary response predecessor. An identification error analysis for the substructure identification method reveals how structural responses affect the identification accuracy. On the basis of this result, a smart reference selection ru...

Substructure identification for shear structures II: Controlled substructure identification

The companion paper proposed a substructure identification method for shear structures and derives the method's error analysis, demonstrating that the identification errors are closely related to two important structural responses: (i) the frequency response of the interstory acceleration of the identified story and (ii) the frequency response ratio between interstory accelerations of two adjacent stories. The identification accuracy of this substructure method can be improved by amplifying the first response and reducing the second response near the substructure natural frequency. Herein, a controlled substructure identification method is proposed, which utilizes two kinds of structural control systems, an active mass driver system, and a passive interstory brace system, to temporarily shift the system dynamics to improve the accuracy of parameter identification. The optimal parameters of two control systems are obtained by solving specially designed optimization problems. Because the accuracy of the controlled substructure identification does not directly depend on complex modal properties of the closed-loop controlled structure but only on the controlled structural responses, the controlled substructure identification method is quite robust to control system errors. Numerical simulations demonstrate that both control methods did greatly improve the accuracy of the identification and were robust to fairly large feedback measurement noise. Copyright © 2012 John Wiley & Sons, Ltd.

Substructure identification for shear structures I: Substructure identification method

A substructure identification method for shear structures is proposed herein. A shear structure is partitioned into many simple substructures; an inductive identification procedure is derived to estimate the structural parameters from top to bottom. In each identification step, the dynamic equilibrium of a one-floor substructure is utilized to construct a substructure identification problem, estimating the story stiffness and damping coefficient. An identification error analysis for least-square error identification problems, based on the linearization of the least-square error problem, is proposed and applied to the substructure identification method. The results show that the identification errors are closely related to two important structural responses: the frequency response of the interstory acceleration of the identified story and the frequency response ratio between adjacent interstory accelerations. Further, these responses are critical to the substructure identification only near a certain frequency, the substructure natural frequency of the identified story substructure. The larger the first response is and/or the smaller the second response is, the more accurate the estimation results will be. A numerical example of a five-story shear structure is given to demonstrate the effectiveness of the proposed substructure identification method and to verify the identification error analysis results. From the results of the identification error analysis, a companion paper proposes a controlled substructure identification method, utilizing specially designed algorithms for structural control devices to change the two key structural responses that affect the identification accuracy, to improve the accuracy of the substructure identification. Copyright © 2012 John Wiley & Sons, Ltd.

Substructure identification for shear structures: cross-power spectral density method

In this paper, a substructure identification method for shear structures is proposed. A shear structure is divided into many small substructures; utilizing the dynamic equilibrium of a one-floor substructure, an inductive identification problem is formulated, using the cross-power spectral densities between structural floor accelerations and a reference response, to estimate the parameters of that one story. Repeating this procedure, all story parameters of the shear structure are identified from top to bottom recursively. An identification error analysis is performed for the proposed substructure method, revealing how uncertain factors (e.g. measurement noise) in the identification process affect the identification accuracy. According to the error analysis, a smart reference selection rule is designed to choose the optimal reference response that further enhances the identification accuracy. Moreover, based on the identification error analysis, explicit formulae are developed to calculate the variances of the parameter identification errors. A ten-story shear structure is used to illustrate the effectiveness of the proposed substructure method. The simulation results show that the method, combined with the reference selection rule, can very accurately identify structural parameters despite large measurement noise. Furthermore, the proposed formulae provide good predictions for the variances of the parameter identification errors, which are vital for providing accurate warnings of structural damage.