Identification Of Structural Parameters Research Articles

Time-varying structural parameter identification for the offshore gangway

The offshore gangway is a new type of offshore transfer equipment, equivalent to an offshore passage. This device makes it convenient for people to transfer between a vessel and an offshore structure. The control algorithm of the gangway system demands adjusting its control parameters to deal with harsh sea conditions and ensure personnel safety. Thence, it is essential to foreknow its time-varying structural parameters, such as the moment of inertia, the mass, the location of the mass center, and so on. However, due to the highly complex structure, these parameters are difficult to obtain through measurement directly. To adaptively identify these time-varying parameters for better controller design, this paper proposes a novel structural parameter identification approach based on the idea of interval division. During the identification process, the entire period is uniformly divided into several intervals. The time-varying model of the offshore gangway system can be represented in each interval by a quadratic regression equation, the coefficients of which can be derived by the least-squares algorithm. Furthermore, to adapt to the different variation frequencies and amplitudes of the parameters, the divided intervals are of distinct sizes according to the characteristics of each identification parameter. In addition, the particle swarm algorithm is employed to obtain the optimal interval sizes, the cost function of which consists of a series of output error terms. Verified by simulation, the method presented in this paper can effectively estimate the structural parameters of the offshore gangway with high accuracy, with mean square errors reduced by at least 2/3 compared to other methods proposed in previous research.

Parameter Identification of Frame Structures by considering Shear Deformation

This paper presents a method to identify the damages in frame structures with slender beams. This method adjusts the parameters of the structure to match the analytical and the measured displacements. The effect of transverse shear deformation on the nodal analytical displacement is analyzed, and the parameter identification of frame structures with slender beams is performed. The results demonstrate that parameter-identification accuracy can be considerably improved by considering the transverse shear deformation in the frame structure with slender beams. The proposed method can accurately identify the damages in frame structures with slender beams using displacement measurements.

Structural Modal Parameters Identification Under Ambient Excitation Using Optimized Symplectic Geometry Mode Decomposition

In the process of structural modal parameters identification under environmental excitation, the employed measured dynamic response signal is usually non-stationary and contains noise. As a novel signal analyzed method, symplectic geometry mode decomposition (SGMD) has been proven to be effective for dealing with non-stationary and noisy signals. However, the traditional SGMD may treat noise as modal information, which inevitably undermines the accuracy of modal identification. To overcome this problem, this paper proposes an optimized SGMD algorithm for structural modal parameter identification. First, the SGMD algorithm is refined with the Hankel matrix, Kurtosis theory, Pearson correlation coefficient, and energy entropy theory. Then, the natural frequencies and damping ratios are identified using the proposed method, which consists of the optimized SGMD algorithm, natural excitation technique (NExT), direct interpolating method, and curve-fitting function. Finally, the applicability of the proposed method under environmental excitation is investigated by two examples: a two-story frame structure and an 88-story office tower. The results demonstrate that the proposed method is effective for identifying the civil structural modal parameters from non-stationary dynamic responses.

Effective removal of humic acid by lignite activated coke via activation of peroxydisulfate: Identification of the key structural parameters

Carbon catalysts are becoming potential substitutes for metal catalysts, however, the high cost and low reusability of carbon materials limit their practical application. Lignite activated coke (LAC) can be used directly for activation of peroxydisulfate (PDS), yet the key structural parameters for such a process remains unclear. In this study, two LACs with great differences were investigated for the removal of humic acid (HA) via activation of PDS. LAC1 has a much lower surface area (238.0 m2/g) than LAC2 (937.7 m2/g), but it has a high-proportioned mesoporous structure with more oxygen functional group on the surface. However, LAC2 has more graphene-like lamellar micropores with low-density O/N group on the surface. Under optimum conditions, the degradation efficiency of both LAC can reach more than 95 %, along with the distinguishing evolution of adsorption and catalysis process. Removal of HA by LAC1 were attributed mainly to its excellent adsorption capacity, whilst LAC2 demonstrate a much higher PDS activation owing to its pore structure and surface chemistry. The main reactive oxygen species were 1O2, which was likely attributed to the existence functional groups including C–N = C and –CO as electron donors. Furthermore, prospects of the application of LAC-based carbocatalysts for persulfates application are also proposed. Overall, this study provides a simple and efficient catalyst for PDS-mediated environment restoration and deepen the mechanistic innovations in 1O2-based nonradical-led oxidative pathways.

Bayesian structural parameter identification from ambient vibration in cultural heritage buildings: The case of the San Jerónimo monastery in Granada, Spain

The deterioration of Cultural Heritage assets caused by the natural hazards is a pressing issue in many countries. Therefore, reliable models based on the large-scale structural response of the assets is key to assess their resilience. However, reliable models such as large and detailed Finite Element (FE) models, require a large number of data and input parameters. This paper proposes a Bayesian learning approach to identify the main parameters of a FE model with quantified uncertainty based on ambient vibration data. As a novelty when compared with other Bayesian structural parameter identification methods from ambient vibration data, here the likelihood function is formulated in a principled way considering information from both frequencies and modes using a probabilistic version of the Modal Assurance Criterion for the modes. This method is embedded into a parameterised computational model to automate the simulation process, and a real case study for a sixteenth century heritage building in Granada (Spain) is presented. The results show the suitability and effectiveness of the proposed Bayesian approach in identifying the most plausible values of the uncertain model parameters in a rigorous probabilistic way, but also in obtaining the modelled frequencies and the modal assurance criterion values with quantified uncertainty.

Output-only structural identification with random decrement technique

Most of existing time domain methods fail to identify the unknown structural parameter in the ambient condition, because the force location is unknown and the number of unknown forces is usually larger than the number of sensors. A novel output-only structural identification method based on the random decrement (RD) technique and covariance matrix adaptation evolutionary strategy (CMA-ES) is presented. The RD functions of a structure under ambient excitation are extracted and treated as free vibration responses for structural parameter identification. The unknown structural parameters are identified by minimizing the discrepancy between the measured and calculated RD functions with the CMA-ES. Unlike traditional output-only methods, which require the estimation of the input excitation in the process, the proposed method does not require the identification of the input forces, thereby significantly reducing the computational cost. Numerical studies on two- and three-dimensional trusses and experimental studies on a steel frame are presented to demonstrate the accuracy and efficiency of the proposed approach. The effects of measurement noise and measurement duration on the identified results are examined. Results show that the proposed method can obtain satisfactory results even with 20% measurement noise and structural damage less than 10% can be detected. Comparison studies with the genetic algorithm (GA) and particle swarm optimization (PSO) are presented to verify the computational efficiency of the proposed method. Results also show that the proposed CMA-ES has a faster convergence speed than the GA and PSO method. The results demonstrate that the proposed technique can be used to detect damage severity and location in structures under ambient conditions.

Method for the calculation of total dipole moment for organic mass of brown and hard coals

The article considers the study of the patterns of changes in the properties of coals in the metamorphism series and the identification of structural and chemical parameters that allow not only to correctly describe them, but also to use them as an express method of assessment. It is known that physical processes such as adhesion and cohesion play an important role in the processes of coal processing, since the adhesion of dissimilar or homogeneous particles brought into contact is due to intermolecular interaction. Determination of adhesive and cohesive properties of various substances, including solid fossil fuels, is not standardized in hardware. The total dipole moment is one of the indicators characterizing the intermolecular interaction. A method for calculating the total dipole moment based on an additive approach is presented. The calculated values of the total dipole moment of the organic mass of solid fossil fuels in the series “brown coal - hard coal - anthracite” showed a high correlation with the reflectance of vitrinite. As a result of mathematical data processing, equations were obtained for calculating the total dipole moment for brown and hard coals. The application of the equations obtained based on statistical processing of a large database and the calculation according to the proposed method can be used in calculating the parameters of such coal processing processes as briquetting, extraction, flotation, etc.

Structural modal parameter identification based on 2D spectral analysis

This paper proposes a 2D spectral analysis method based on damped Capon (dCAPON) and damped APES (dAPES) for structural modal parameter identification. The frequency and damping are estimated from the 2D dCAPON energy spectrum first, and then the amplitude (to construct the modal shape) corresponding to the frequency-damping point is calculated by the dAPES. The raw evenly-spaced computing grid is optimized into a banded hierarchically-refined one, greatly improving the computational efficiency. The performance of the proposed method is studied and it is investigated through the numerical and experimental data of a benchmark structure. The proposed method provides a simple and direct approach to estimating structural modal parameters, particularly for frequency and damping ratio.

Structural Modal Parameter Identification Method Based on the Delayed Transfer Rate Function under Periodic Excitations

The dynamic response transfer rate function (TRF) is increasingly used in the field of structural modal parameter identification because it does not depend on the white noise assumption of the excitation. In this paper, the interference of periodic excitation on structural modal parameter identification using TRF is analyzed theoretically for a class of civil engineering structures with obvious periodic components in excitation, and then an identification method of structural real modal parameters is proposed. First, a delayed TRF is constructed, and the pseudo-frequency response function is further obtained to identify the periodic spurious poles of the whole system. Then, the effective identification of the real modal parameters of the structure is achieved by comparing the system poles identified via conventional TRF. Finally, the feasibility and robustness of the proposed method were verified using a calculation example with four-degrees-of-freedom system. In addition, the modal parameters of a structure under periodic excitation were effectively identified by taking a pumping station as an example, and the results show that the method accurately identified the structural modal parameters when the excitation contained periodic components, which has wider prospects for technical applications.

A new approach of ensemble learning in fully automated identification of structural modal parameters of concrete gravity dams: A case study of the Koyna dam

This study proposes a new automatic modal identification algorithm that identifies the system's modal parameters based on output-only data. The identification process is based on a stochastic subspace algorithm. Stochastic subspace algorithms only need the model degree to identify the modal parameters of the system. A model degree is an appropriate degree of the state-space model and it is estimated by using the Singular Value Criterion. Several clustering algorithms with different approaches have been used. The most powerful data fusion algorithm, which is called Dempster-Schaffer Theory is applied for integrating the results of the algorithms and preventing divergence of results. Furthermore, it focused on measuring the uncertainty of the identified modal parameters. In the proposed modal identification algorithm, only one data set is used for identification. In this situation, the variety of data is small and limited, and statistical resampling methods seem to be a good option. Establishing the above cycle enables the identification algorithm to identify modal parameters and quantify their uncertainty based only on a data set. This algorithm uses many fields, such as data collection, signal processing techniques, model reduction, feature extraction, clustering and data fusion. The Koyna gravity dam is selected as a case study to evaluate the performance of the developed algorithm. In the first step, a comparative study is carried out to verify the developed finite element model of the Koyna dam. Afterwards, the dam is excited by the Koyna earthquake, and dynamic responses of the dam are collected from the minimum number of points required for system identification. It is noteworthy the number of modes that are identified in this work is equal to 4 vibration modes. The maximum difference between the natural frequencies identified by the present algorithm and the finite element model does not exceed 4.33%. Also, the average difference is less than two percent, which indicates the high accuracy of the system identification algorithm. Also, all the vibration modes in the desired frequency range have been correctly identified. The results indicate that modal parameters with remarkable precision have been extracted by the modified algorithm.

Identification of a Family of Glycoside Derivatives Biologically Active against Acinetobacter baumannii and Other MDR Bacteria Using a QSPR Model.

As the rate of discovery of new antibacterial compounds for multidrug-resistant bacteria is declining, there is an urge for the search for molecules that could revert this tendency. Acinetobacter baumannii has emerged as a highly virulent Gram-negative bacterium that has acquired multiple resistance mechanisms against antibiotics and is considered of critical priority. In this work, we developed a quantitative structure-property relationship (QSPR) model with 592 compounds for the identification of structural parameters related to their property as antibacterial agents against A. baumannii. QSPR mathematical validation (R2 = 70.27, RN = -0.008, a(R2) = 0.014, and δK = 0.021) and its prediction ability (Q2LMO= 67.89, Q2EXT = 67.75, a(Q2) = -0.068, δQ = 0.0, rm2¯ = 0.229, and Δrm2 = 0.522) were obtained with different statistical parameters; additional validation was done using three sets of external molecules (R2 = 72.89, 71.64 and 71.56). We used the QSPR model to perform a virtual screening on the BIOFACQUIM natural product database. From this screening, our model showed that molecules 32 to 35 and 54 to 68, isolated from different extracts of plants of the Ipomoea sp., are potential antibacterials against A. baumannii. Furthermore, biological assays showed that molecules 56 and 60 to 64 have a wide antibacterial activity against clinically isolated strains of A. baumannii, as well as other multidrug-resistant bacteria, including Staphylococcus aureus, Escherichia coli, Klebsiella pneumonia, and Pseudomonas aeruginosa. Finally, we propose 60 as a potential lead compound due to its broad-spectrum activity and its structural simplicity. Therefore, our QSPR model can be used as a tool for the investigation and search for new antibacterial compounds against A. baumannii.

FRF-based probabilistic modal parameter identification of structures with known seismic input

In this study, an FRF based Bayesian approach is presented for modal identification of civil structures with known seismic input. The proposed method utilizes the statistical properties of frequency response function (FRF) of the observed structure which is obtained from the acceleration response (output) and the seismic input recorded at the base level. In this context, first, the joint pdf of prediction error term and seismic input is derived. Then, applying a linear vector transformation, the output term in the derived joint pdf is defined as depending on the FRF and the seismic input. Finally, the marginal probability of FRF is obtained as a complex ratio distribution from the transformed joint pdf. The posterior probability function for modal parameters is obtained by using Bayes’ theorem. The modal parameters are obtained by maximum likelihood estimation of the constructed probability density function. Finally, the posterior uncertainties for modal parameters are calculated. Two numerical and two real data examples are presented to illustrate the computational efficiency of the proposed methodology against input–output and output only techniques.

ИССЛЕДОВАНИЕ СТРУКТУРЫ СЛОЖНЫХ ПОВЕРХНОСТЕЙ ДЕТАЛЕЙ ПОСЛЕ ДРОБЕСТРУЙНОЙ ОБРАБОТКИ

The article shows that in order to determine the parameters of the surface structure, it is necessary to use optical research methods. An optoelectronic complex has been developed that allows determining the amplitude and average step of the correlation surface depending on the modes and conditions of shot blasting. A measurement technique and a software package for determining the parameters of the surface structure are proposed. The identification of structural parameters with the roughness parameters of complex surfaces was carried out.

Design and Development of Structural Parameter Identification Software for Nonlinear Vibration Isolators

This paper developed an object-oriented software to facilitate parameter calibration of the quasi-zero stiffness (QZS) vibration isolator for engineering applications. Considering its advantages of convenient programming and high noise resistance, we employ the harmonic balance method to construct the identification core algorithm. The developed software consists of data pre-processing, parameter identification, and post-processing modules. The data pre-processing module imports and classifies all the required data. Then, the parameter identification module conducts preliminary nonlinearity analysis so that nonlinear structural parameter identification can be successfully carried out. After parameter identification, the data post-processing module displays and stores the identified results. The performance of the software is tested by a numerical example of a two-degree-of-freedom QZS vibration isolator, proving the software can give accurate identification results within a few user-friendly graphical interfaces. Based on this software, we hope that the application and promotion of nonlinear system parameter identification become more convenient.

An Interval Model Updating Method Based on Meta-Model and Response Surface Reconstruction

In this paper, a new interval finite element model updating method is proposed for interval identification of structural parameters based on meta-model and response surface reconstruction. The lower and upper bounds of the uncertain structural parameters are determined by solving the optimization problem which minimizes the difference between the interval of the predicted and measured responses. The response surface models are reconstructed based on the resampling technique for mapping the relationship between a single input and a single output. Then the accurate interval of the responses during the iteration step can be efficiently estimated using the vertex method. Meanwhile, the Gaussian process regression model (GPRM) is constructed as the meta-model to replace the finite element model for calculating the responses of the system to improve computational efficiency. Several numerical and experimental examples are investigated to elucidate the feasibility of the proposed method in the interval identification of structural parameters. Obtained outcomes have demonstrated that the proposed method outperforms many existing approaches in the literature, especially for the nonlinear monotonously non-increasing problem.

Wavenumber identification of 1D complex structures using Algebraic Wavenumber Identification (AWI) technique under complex conditions

The dynamic characterization of complex structures raises the need for a robust experiment-based wavenumber identification method, especially when they are tested under complex conditions. To this end, this paper presents an optimized Algebraic Wavenumber Identification (AWI) method and describes the dynamic behavior of three complex structures under a series of complex conditions through AWI. The first novelty of this paper is to optimize AWI in terms of computation efficiency and accuracy by two solutions: (a) the multiple integral of AWI, which acts as a filter, is explicitly solved by the least squares fitting method, significantly reducing the computation cost, especially when multiple samples are used as input parameters; (b) a sampling strategy is provided to further improve the robustness of the AWI to measurement errors. On this basis, an AWI implementation procedure is proposed for experimental tests under complex conditions. The second novelty of this paper is on the applications of AWI in wave propagation parameters identification of complex structures, including the damping loss factor estimation of a viscoelastic beam and a honeycomb sandwich beam, the band gap identification of a meta-structure, and experimental dispersion curves extraction of these three complex structures. The third novelty of this paper is the experimental validation of AWI under a series of complex conditions, including signal noise, non-periodic sampling, and uncertainty of measuring points’ geometric coordinates. The experimental and numerical results have been compared to two popular inverse methods, namely, Inhomogeneous Wave Correlation (IWC) and INverse COnvolution MEthod (INCOME), demonstrating the validity of the AWI implementation procedure proposed in this paper.

Modal parameter identification and seismic assessment of historical timber structures under near-fault and far-fault ground motions

Timber, one of the oldest structural materials, is an essential material in the construction of traditional structures due to its undeniable advantages. However, wood is sensitive to environmental effects, and hence, historical timber structures experience various degradations and destructions during their service life. This highlights the necessity of investigating the structural responses of such structures using experimental techniques and calibrated finite element models. This paper presents vibration-based system identification of two historical timber structures and an in-depth comparison of the effects of near-fault and far-fault ground motions on seismic responses. Ambient vibration tests were carried out using the operational modal analysis method. Enhanced Frequency Domain Decomposition and Stochastic Subspace Identification methods were used for experimental modal parameters estimation. Also, the initial finite element models were calibrated considering the ambient vibration test results to correctly assess the seismic responses of the selected structures under near-fault and far-fault ground motions. The distributions of maximum amplitudes (displacements, internal forces, and absolute accelerations) under earthquake records with near-fault and far-fault are comparatively presented. All numerical results indicated that both near-fault ground motions and their far-fault counterparts played major roles in terms of the seismic responses of selected historical timber structures. In addition, these results illustrate that both had the potential to increase displacements and, internal forces at load-bearing elements, and leading to notable structural responses and destructive effects.

An unsupervised learning algorithm for computer vision-based blind modal parameters identification of output-only structures from video measurements

Operational modal analysis (OMA) is required to maintain large-scale and necessary civil infrastructures. The non-contact method of OMA using digital image correlation and point tracking algorithms requires a speckle pattern placed on the structure. Alternatively, advanced computer vision methods like optical flow and phase-based video motion magnification (PBVMM) techniques are used to measure modal parameters. Despite the importance of PBVMM, the users should know the range of frequencies in which the natural structure frequency lies. A methodology based on an unsupervised machine learning technique is developed to extract the modal parameters blindly from its recorded digital video. The proposed methodology uses complex steerable pyramids and an unsupervised machine learning technique, also known as principal component analysis, and analytical mode decomposition with a random decrement technique to blindly extract the modal parameters of a structure. This study validated the proposed methodology using a multi-degree of freedom (DOF) numerical model. The results are compared with theoretical and estimated values and are in good agreement. Furthermore, it is implemented on a laboratory benchmark SDOF, MDOF, and real-time videos of the London Millennium and Tacoma Narrows bridges for blindly extracting the modal frequencies and damping ratios.

Working Mode Identification Method for High Arch Dam Discharge Structure Based on Improved Wavelet Threshold–EMD and RDT Algorithm

Prototype vibration response data of high arch dam discharge structures inevitably mix various noises under the discharge excitation, which adversely affects the accuracy of the working modal identification of the structure. To effectively filter noise and reduce modal aliasing for better identification accuracy, this study proposes an improved modal threshold identification method based on an improved wavelet threshold–empirical mode decomposition (EMD) and random decrement technique (RDT) algorithm for high arch dam discharge structures. On the basis of the measured vibration response data of the dam, the wavelet threshold is adopted to filter out most of the high-frequency white noise and to reduce the boundary accumulation effect of EMD decomposition. Detrended fluctuation analysis (DFA) is utilized to filter white noise and low-frequency flow noise after EMD decomposition. The natural frequency and damping ratio of the structure system are obtained by the improved RDT algorithm. The engineering examples show that the proposed method can accurately filter the measured vibration response signal noise of the discharge structure, retain the signal characteristic information, improve the accuracy of working modal recognition of the structural vibration response, avoid the complex ordering process of the system, and ease the working modal parameter identification of high arch dam discharge structures. This method can be applied to the mode identification of other large structures, as well.

An improved NExT method for modal identification with tests validation

The Natural Excitation Technique (NExT) is widely used in structural modal parameter identification. However, the cross-correlation function is susceptible to the mode shape of each order, which is obtained by the traditional NExT method combined with other modal identification methods between the two measuring points, resulting in large errors of the identified frequency and damping ratio of various modes. Therefore, in this study, to obtain the modal responses of the target order correctly and eliminate interaction of various modes, a new combined NExT method with improved Empirical Mode Decomposition (EMD) method is developed. The improved NExT method is introduced firstly, then, the structural modal parameters are identified by five different methods, which are Ibrahim Time Domain Technique, Sparse Time Domain Algorithm, Autoregressive Moving Average timing method, Least Square Complex Exponential and Eigensystem Realization Algorithm respectively. The correction and improvement of the new method are verified through a simple-supported beam experiment and a shaking table test of a 12-story benchmark tall building at Tongji University. The structural modal parameters are identified and compared through adopting traditional NExT method and the proposed new NExT algorithm. Results illustrate that the improved NExT algorithm in this paper has a higher accuracy and effectiveness than the traditional one in structural modal parameter identification.