Structural Identification Technique Research Articles

Research on Structural Damage Identification of Shear Buildings Based on Modal Curvature Change

In the past few decades, various techniques for structural damage identification based on dynamic properties have been widely studied, among which modal curvature is the research hotspot, due to its high sensitivity to local damage. To realize story-level structural damage identification of shear buildings, this paper first derives the patterns of variation in modal curvature of the damaged structure. Then, a new damage index sensitive to local damage called the Modal Curvature Change modified with Softmax (MCCSM for short), is proposed to improve the accuracy of structural damage identification. Afterward, the validity of the damage index is verified by a shaking table test of a scaled RC frame structure model and its corresponding numerical model. The research results show that the damage location indicated by MCCSM corresponds well with the damage phenomenon in the shaking table test and numerical simulation; compared with using the theoretical mode shape, the MCCSM index can give comparable damage identification results using the mode shape obtained through operational modal analysis from vibration time-history data, which proves the feasibility in engineering practices; through numerical simulation, it is proved that the MCCSM index is applicable in a wide range of damage cases with different story position, number of damaged story, and damage extent.

Intelligent Structure Identification and Robust Control Implementation

This study outlines a comprehensive strategy for designing and implementing robust controllers tailored for intelligent structures. This study presents a robust control-based structural identification technique that uses the input/output data of the system to construct a state-space mode and frequency domain. To reduce vibrations, a robust controller is created using the control Simulink model. The identification and robust control of smart structures using Simulink involve a combination of system identification techniques and control design within the MATLAB Simulink environment. The key challenge is dealing with uncertainties and variations in system dynamics. Robust control methods have been employed to suppress the vibrations during dynamic disturbances. These methods are important for mechanical systems operating under stochastic loading conditions.

Development of a Frequency-Adapted Virtual Fields Method as an alternative to the Corrected Force Analysis Technique for dynamic forces and structural parameter identification

In vibroacoustics, inverse methods use the vibratory response of a structure to identify either a load or a structural parameter. The Force Analysis Technique (FAT) and the Virtual Fields Method (VFM), are two inverse methods that have been used in the past to identify loads or structural parameters of flexural beams or plates. The Corrected Force Analysis Technique (CFAT) is another inverse method that corrects the singularity of FAT, making this approach more accurate at higher frequencies. In this study, this principle is applied to the VFM, using polynomial interpolation of the displacement field, in order to adapt the method so that at each frequency, the accuracy of the method is improved. The accuracy of the Frequency-Adapted VFM is demonstrated using numerical simulations. A formal comparison between FAT, CFAT and the VFM is also proposed.

Identification and control of crystalline nuclei facets imparting to the breaking of symmetry selection rules of optical phonon modes in GaP/Si (001)

The complex interfacial structural issues associated with hetero-polar epitaxial integration of GaP on Si(001) are resolved by improving the kinetics of gallium and phosphorous adatoms during the initial nucleation process. This is achieved through the use of Raman spectroscopy as a feedback mechanism. The peak intensity ratio of forbidden (transverse optical)-to-allowed (longitudinal optical) phonons of GaP under unpolarized Raman scattering is contemplated as an initial guide to ascertain the relative density of defect-mediated non-(001) GaP facets. The azimuthal-angle dependent polarized Raman spectroscopy is then applied to identify and quantify the nucleation of higher-index non-(001) ({111} and {112}) faceted islands in the nucleation layer and their propagation in the overgrown structures. The nucleation of GaP adatoms on the Silicon surface at ∼525 °C leads to a smooth surface and substantially suppresses the defect-mediated facets. The optimal mobility and controlled adsorption of adatoms of Ga and P on the substrate surface under intermediate nucleation kinetics foster the charge-neutral interface, and hence the defect-suppressed two-dimensional layer growth of GaP. The proposed Raman spectroscopy methodology emerges as a rapid and economical alternative to existing in situ reflection anisotropy spectroscopy and high-resolution transmission electron microscopy techniques for structural identification. This method of growth optimization can also be applied to other III-V polar semiconductors to achieve high-quality and efficient monolithic integration of devices on existing Si technology.

Hierarchical Neural Network and Simulation Based Structural Defect Identification and Classification

A vibration data-driven structural defect identification and classification technique is developed using frequency response under random excitation and a hierarchical neural network. A system of artificial neural networks (ANNs) is trained using finite element simulation-based synthetic data to reduce the need for many sensor measurements required otherwise. Principal component analysis (PCA) is employed to compress the high dimensionality of the vibration response data and eliminate the noise effect in the training and testing. Frequency responses data dimension for the structure with defects such a crack from stress concentration, rivet hole expansion, and attached foreign object mass such as ice accumulation in aircraft wing or fuselage are reduced using PCA and fed to a classifier network. The probabilistic decision output from the classifier network and the compressed data are then fed to the next levels of estimator networks, where each network is dedicated to the individual type of defect for the estimation of the defect parameters corresponding to that class of defect. The methodology is applied to a stiffened panel structure. The cracks and rivet hole expansions are introduced in the rivet line of the stiffener, and the foreign object mass is attached to the panel surface. The results show that it is possible to classify the defects and further estimate the defect parameters with good accuracy and reliability. It was observed that the damage classification network had an accuracy of roughly 95%. The damage localization network for crack as well as rivet expansion had average absolute error of around 2. The damage severity network was also able to perform well with a mean absolute error of about 0.34 for crack length detection and 0.22 for expanded rivet damage. However, the damage localization and severity prediction networks were quite challenging to train in the presence of multiple damages and need further development in the network architecture.

A review on recent developments in vibration-based damage identification methods for laminated composite structures: 2010–2022

Laminated composites received great attention in the structural applications due to innumerable advantages over conventional materials. These structures are subjected to environmental and mechanical loading conditions which leads to structural damage. The damage identification at early stage is crucial to overcome catastrophic failures, economic loss, human loss, and its disastrous consequences. Therefore, various structural damage identification techniques have been developed to identify the structural damages stages. These techniques provide the damage information about the systems, including structural health, serviceability, integrity, and safety. Over the past few decades, several review articles have been published on damage detection methods employing vibration characteristics for conventional and composite structures together. There has not been a study where complete concentration is focused on the laminated composite structures alone. This article concentrates on damage identification of laminated composite structures using vibration-based methods integrated with finite element methods, optimization techniques, signal processing methods, and machine learning algorithms over the past thirteen years. In addition, the limitations and applications of various methods have been discussed. This review article will provide the researchers and engineers an insight into the damage detection methods for laminated composite structures.

A chaos game Optimization-based model updating technique for structural damage identification under incomplete noisy measurements and temperature variations

Dealing with structural damage identification (SDI) considering limited noise-contaminated measurements and varying environmental conditions is still a challenge due to the difficulties regarding the localization and quantification of structural damage. If the challenging issue is not considered in SDI process, the SDI techniques may become unreliable and have limitations in their implementation in in-service structures. In this regard, the article proposes an optimization-based model updating technique for SDI taking into consideration both incomplete noisy measurements and temperature variations. First, it is assumed in the finite element model of the monitored structure that the relation between temperature conditions and structural materials is temperature-dependent to synthetically generate damage-induced vibration data under varying environmental temperatures. Then, the inverse SDI problem is recast into a constrained optimization problem and a newly developed algorithm called Chaos Game Optimization (CGO) is adopted for solving this problem with high efficiency. The efficiency of the CGO algorithm is assessed by comparison with other four parameter-less optimization algorithms. Finally, three engineering numerical examples are conducted to demonstrate the performance of the proposed technique. The results of the numerical investigations indicated that for the monitored structures made of metallic material (i.e., steel and aluminum), the proposed technique can accurately localize and quantify single and multi-damage even in incomplete and noisy modal data and environmental temperature changes. Meanwhile, for the monitored structures made of concrete material, the ambient temperature variations significantly affect the damage detectability and localizability of the proposed technique.

A reinforcement learning-based hybrid modeling framework for bioprocess kinetics identification.

Constructing predictive models to simulate complex bioprocess dynamics, particularly time-varying (i.e., parameters varying over time) and history-dependent (i.e., current kinetics dependent on historical culture conditions) behavior, has been a longstanding research challenge. Current advances in hybrid modeling offer a solution to this by integrating kinetic models with data-driven techniques. This article proposes a novel two-step framework: first (i) speculate and combine several possible kinetic model structures sourced from process and phenomenological knowledge, then (ii) identify the most likely kinetic model structure and its parameter values using model-free Reinforcement Learning (RL). Specifically, Step 1 collates feasible history-dependent model structures, then Step 2 uses RL to simultaneously identify the correct model structure and the time-varying parameter trajectories. To demonstrate the performance of this framework, a range of in-silico case studies were carried out. The results show that the proposed framework can efficiently construct high-fidelity models to quantify both time-varying and history-dependent kinetic behaviors while minimizing the risks of over-parametrization and over-fitting. Finally, the primary advantages of the proposed framework and its limitation were thoroughly discussed in comparison to other existing hybrid modeling and model structure identification techniques, highlighting the potential of this framework for general bioprocess modeling.

Energy Ratio Variation-Based Structural Damage Detection Using Convolutional Neural Network

In the field of structural health monitoring (SHM), with the mature development of artificial intelligence, deep learning-based structural damage identification techniques have attracted wide attention. In this paper, the convolutional neural network (CNN) is used to extract the damage feature of simple supported steel beams. Firstly, the transient dynamic analysis of the steel beam is carried out by finite element software, and the acceleration response signals under different damage scenarios are obtained. Then, the acceleration response signal is decomposed by wavelet packet decomposition (WPD) to extract the wavelet packet band energy ratio variation (ERV) index as the training sample of CNN. Subsequently, the vibration experiment of a simple supported steel beam was carried out, and the results were compared with the numerical simulation results. The characteristic indexes were obtained by making corresponding changes to the vibration signal, and then, the experimental data were input into the CNN to predict the effect of damage detection. The results show that the method can successfully detect the intact structure, single damage, and multiple damages with an accuracy of 95.14% under impact load, and the performance is better than that of support vector machine (SVM), with good robustness.

Influence of the ground on the structural identification of a bell-tower by ambient vibration testing

The article revolves around the structural identification technique through dynamic in situ tests trying to unravel the contribution of Soil Structure Interaction (SSI). The issue is addressed by considering the bell tower of Santa Sofia in Benevento (Italy) as a case study. Together with the homonymous church, the monastery and the fountain, the bell tower is part of a monumental complex included in the UNESCO World Heritage List since 2011. As the tower is completely separated from the other buildings of the complex, from a structural viewpoint it is quite a simple structure but likely to be affected by SSI. The latter phenomenon in some way could dictate the tower static and dynamic response, thus affecting the overall structural dynamic identification process. Invasive investigations are not usually allowed on cultural heritage due to preservation requirements. Thus, the paper proposes a simple but effective procedure for obtaining the data set required for both structure and soil characterization through noninvasive in-situ tests. The dynamic response of the subsoil at the tower site was firstly identified by reproducing the experimental amplification function provided by geophysical tests. Later, the results of an ambient vibration test performed on the bell tower have been interpreted and used to calibrate two refined 3D finite element models of the tower, one having the structure with a restrained base and another with a base compliant with the reality found. In the latter case, a suitable portion of the soil below the tower was included in the analysis domain. The contribution of the ground has been ascertained to move towards a reliable dynamic identification of the built cultural heritage. • Procedure for structure and soil data set characterization by noninvasive in-situ tests. • The importance of SSI on the correct identification of the structure behavior. • Combined geotechnical and structural optimization procedure to validate a FE model.

Structural Assessment under Uncertain Parameters via the Interval Optimization Method Using the Slime Mold Algorithm

Damage detection of civil and mechanical structures based on measured modal parameters using model updating schemes has received increasing attention in recent years. In this study, for uncertainty-oriented damage identification, a non-probabilistic structural damage identification (NSDI) technique based on an optimization algorithm and interval mathematics is proposed. In order to take into account the uncertainty quantification, the elastic modulus is described as unknown-but-bounded interval values and the proposed new scheme determines the upper and lower bounds of the damage index. In this method, the interval bounds can provide supports for structural health diagnosis under uncertain conditions by considering the uncertainties in the variables of optimization algorithm. The model updating scheme is subsequently used to predict the interval-bound of the Elemental Stiffness Parameter (ESP). The slime mold algorithm (SMA) is used as the main algorithm for model updating. In addition, in this study, an enhanced variant of SMA (ESMA) is developed, which removes unchanged variables after a defined number of iterations. The method is implemented on three well-known numerical examples in the domain of structural health monitoring under single damage and multi-damage scenarios with different degrees of uncertainty. The results show that the proposed NSDI methodology has reduced computation time, by at least 30%, in comparison with the probabilistic methods. Furthermore, ESMA has the capability to detect damaged elements with higher certainty and lower computation cost in comparison with the original SMA.

System identification of fuzzy relation matrix models by semi-tensor product operations

In order to facilitate the representation of fuzzy relation matrix (FRM) models, a new system identification technique is proposed in this work to recognize the architecture and parameters of FRM models based on the semi-tensor product (STP) operation. Firstly, a fuzzy STP algorithm is defined for fuzzy inference. Secondly, a novel FRM framework is proposed for system parameter identification. Thirdly, the recognized FRM parameters are optimized to improve fuzzy system performance by the use of a hybrid training method based on the least squares estimator and the recursive Levenberg-Marquaedt algorithm. The effectiveness of the proposed structure and parameter identification techniques is verified by simulation of a multi-steps-ahead prediction modeling. Simulation results show that the proposed fuzzy STP technology is efficient for system identification, and the proposed matrix expression can be used to design multi-input multi-output (MIMO) systems with fuzzy FRM models.

Modal identification of building structures using vision-based measurements from multiple interior surveillance cameras

Surveillance cameras are ubiquitous within modern building systems, providing a wealth of data that, to date, has not been utilized for structural health monitoring and condition assessment. In this paper, a vision-based vibration-monitoring technique is introduced, which leverages existing cameras within building structures to extract modal information and assess for damage. Cameras already installed within buildings, such as interior surveillance cameras, are used to measure structural responses (inter-story drifts and rotations) by tracking the cameras’ motion relative to the floor below. By synchronizing vision-based inter-story measurements captured at multiple stories, a complete three-dimensional representation of the building’s motion is recovered. Output-only system identification (frequency domain decomposition) is applied to these vision-based measurements to extract modal information—frequencies and mode shapes. An experimental parametric study on a lab-scale three-story structure is conducted to assess the efficacy of the vision-based monitoring technique for structural modal identification. The primary set of structure/camera geometries considered is representative of actual surveillance camera installations. Broadband white noise is used to excite the structure uni- and bidirectionally, and the resulting ambient and transient responses are measured. The accuracy of the modal information extracted by means of the proposed vision-based technique is assessed through comparison with modal data from accelerometer-based measurements, quantified by the modal assurance criteria. Moreover, the precision, robustness, and repeatability of the method as well as its shortcomings are established and are described in this paper.

Constrained observability techniques for structural system identification using modal analysis

The characteristics of civil structures inevitably suffer a certain level of damage during its lifetime and cheap, non-destructive and reliable methods to assess their correct performance are of high importance. Structural System Identification (SSI) using measured response is the way to fine why performance is not correct and identify where the problems can be found. Different methods of SSI exist, both using static and vibration experimental data. However, using these methods is not always possible to decide if available measurements are sufficient to uniquely obtain the unknown. A (SSI) method that uses constrained observability method (COM) has already been developed based on the information provided by the monitoring of static non-destructive tests - using deflections and rotations under a known loading case. The method assures that all observable variables can be obtained with the available measured data. In the present paper, the problem of determining the actual characteristics of the members of a structure such as axial stiffness, flexural stiffness and mass using vibration data is analyzed. Subsets of natural frequencies and/or modal shapes are used. To give a better understanding of the proposed method and to demonstrate its potential applicability, several examples of growing complexity are analyzed, and the results show how constrained observability techniques might be efficiently used for the dynamic identification of structural systems using dynamic data. These lead to significant conclusions regarding the functioning of an SSI method based on dynamic behavior.

A structural damping identification technique for coatings on blisks based on improved component mode mistuning model

Abstract Since the structural damping characteristics of damping coatings are much higher than that of the blisk substrate, the structural damping differences of coatings in each sector directly affect structural damping of the coated blisk. Therefore, for the coated blisk, it is very important to identify the structural damping differences caused by coating. A novel method for structural damping identification of coatings on the coated blisk is proposed. The component mode mistuning (CMM) model is improved for establishing reduced order model (ROM) of coated blisks. In the model, based on the consideration of blisk substrate, the coatings on each sector are decomposed into the mistuned substructures which are independent of each other. Based on this model, the identification formula for structural damping coefficients of coatings on each sector is deduced. A numerical case is given to illustrate the identification procedure. The vibration test methods for obtaining input parameters of damping identification are demonstrated by an actual case. The damping identifications of numerical case and actual case are carried out. The identification results shown that the identification method had high identification accuracy and can be well applied for the damping identification of the hard coating and viscoelastic coating.

The Quest for Phenolic Compounds from Macroalgae: A Review of Extraction and Identification Methodologies.

The current interest of the scientific community for the exploitation of high-value compounds from macroalgae is related to the increasing knowledge of their biological activities and health benefits. Macroalgae phenolic compounds, particularly phlorotannins, have gained particular attention due to their specific bioactivities, including antioxidant, antiproliferative, or antidiabetic. Notwithstanding, the characterization of macroalgae phenolic compounds is a multi-step task, with high challenges associated with their isolation and characterization, due to the highly complex and polysaccharide-rich matrix of macroalgae. Therefore, this fraction is far from being fully explored. In fact, a critical revision of the extraction and characterization methodologies already used in the analysis of phenolic compounds from macroalgae is lacking in the literature, and it is of uttermost importance to compile validated methodologies and discourage misleading practices. The aim of this review is to discuss the state-of-the-art of phenolic compounds already identified in green, red, and brown macroalgae, reviewing their structural classification, as well as critically discussing extraction methodologies, chromatographic separation techniques, and the analytical strategies for their characterization, including information about structural identification techniques and key spectroscopic profiles. For the first time, mass spectrometry data of phlorotannins, a chemical family quite exclusive of macroalgae, is compiled and discussed.

A pseudo-modal structural damage index based on orthogonal empirical mode decomposition

A structural damage identification technique hinged on the combination of orthogonal empirical mode decomposition and modal analysis is proposed. The output-only technique is based on the comparison between pre- and post-damage free structural vibrations signals. The latter are either kinematic (displacements, velocities or accelerations) or deformation measures (strains or curvatures). The response data are decomposed by means of the orthogonal empirical mode decomposition to derive a finite set of orthogonal intrinsic mode functions; the latter are used as a multi-frequency and data-driven basis to build pseudo-modal shapes. A new damage index, the so-called pseudo-mode index, is introduced to compare the response obtained for the two states of the structural system and detect potential damages. The performance of the devised index in detecting a localised damage is shown through numerical and experimental tests on two structural models, namely a 4-degrees-of-freedom system and a two-hinged parabolic arch.

Signal processing techniques for structural health monitoring of super high-rise buildings

Along with the accelerating urbanization and the increasing population density, large numbers of super high-rise buildings have been built around the world in the past decades. Together with the successive construction of these buildings, the structural safety issues have received more and more attention from all walks of life. During the operation period of super high-rise buildings, the long-term effects of environmental degradation and abnormal loads usually lead to the occurrence of damages in local areas of the building structure, which after long time accumulation, would inevitably cause the degradation of structural performance, or even structural failures, severely threatening life and property to the country and people. Therefore, various structural damage identification techniques are applied to conduct structural health monitoring (SHM) for super high-rise buildings that under construction and completed in order to discover possible structural damages in time, and carry out safety assessments with disaster warnings for possible dangers and unfavorable conditions of the structures for determination of reasonable and economic maintenance periods. This paper gives a brief review on the existing signal processing techniques used for damage identification and SHM of super high-rise buildings, and further summarizes the future research trend of these techniques.

The Diversity of Cyanobacterial Toxins on Structural Characterization, Distribution and Identification: A Systematic Review.

The widespread distribution of cyanobacteria in the aquatic environment is increasing the risk of water pollution caused by cyanotoxins, which poses a serious threat to human health. However, the structural characterization, distribution and identification techniques of cyanotoxins have not been comprehensively reviewed in previous studies. This paper aims to elaborate the existing information systematically on the diversity of cyanotoxins to identify valuable research avenues. According to the chemical structure, cyanotoxins are mainly classified into cyclic peptides, alkaloids, lipopeptides, nonprotein amino acids and lipoglycans. In terms of global distribution, the amount of cyanotoxins are unbalanced in different areas. The diversity of cyanotoxins is more obviously found in many developed countries than that in undeveloped countries. Moreover, the threat of cyanotoxins has promoted the development of identification and detection technology. Many emerging methods have been developed to detect cyanotoxins in the environment. This communication provides a comprehensive review of the diversity of cyanotoxins, and the detection and identification technology was discussed. This detailed information will be a valuable resource for identifying the various types of cyanotoxins which threaten the environment of different areas. The ability to accurately identify specific cyanotoxins is an obvious and essential aspect of cyanobacterial research.

An engineering approach to model-class selection for measurement-supported post-earthquake assessment

Structural identification of existing structures is a subject of increasing interest in the civil-engineering community because of its potential to use measurement data to enhance asset-management decision making. An important structural-identification application is residual-capacity assessment of earthquake-damaged structures. Known to be potentially slow and subjective, current assessment practices rely mostly on expert-conducted visual inspection. Structural-identification techniques can help overcome shortcomings of visual inspection through improving estimates of residual capacity. Physics-based models are needed to predict structural behavior under future loading (extrapolation). Especially for earthquake-engineering simulations, a large variety of prediction models and techniques exists. While engineers often prefer simplified behavior models for assessment, data-interpretation applications usually involve detailed model classes. Neither choice is appropriate for all situations. This paper contains a proposal for more rational model-class selection than typically employed in current practice. Model-class selection criteria are described and illustrated using two cases. Knowledge of the earthquake signal is identified as the main criterion to select model classes and analysis tools. Displacement-demand predictions are reduced by up to 91% using structural identification techniques and are validated for all tested model classes by observed behavior under aftershocks. Applicability of this model-class selection is most attractive for post-earthquake assessment of residual capacity (not damage detection) where there is a reduced availability of measurement data, such as when there is no continuous monitoring data. This strategy provides useful support to engineers for key decisions related to asset management and structural resilience.