Modal Properties Of Structure Research Articles

Cost-Effective Data Acquisition Systems for Advanced Structural Health Monitoring.

With the growing demand for infrastructure and transportation facilities, the need for advanced structural health monitoring (SHM) systems is critical. This study introduces two innovative, cost-effective, standalone, and open-source data acquisition devices designed to enhance SHM through the latest sensing technologies. The first device, termed CEDAS_acc, integrates the ADXL355 MEMS accelerometer with a RaspberryPi mini-computer, ideal for measuring strong ground motions and assessing structural modal properties during forced vibration tests and structural monitoring of mid-rise buildings. The second device, CEDAS_geo, incorporates the SM24 geophone sensor with a Raspberry Pi, designed for weak ground motion measurements, making it suitable for seismograph networks, seismological research, and early warning systems. Both devices function as acceleration/velocity Data Acquisition Systems (DAS) and standalone data loggers, featuring hardware components such as a single-board mini-computer, sensors, Analog-to-Digital Converters (ADCs), and micro-SD cards housed in protective casings. The CEDAS_acc includes a triaxial MEMS accelerometer with three ADCs, while the CEDAS_geo uses horizontal and vertical geophone elements with an ADC board. To validate these devices, rigorous tests were conducted. Offset Test, conducted by placing the sensor on a leveled flat surface in six orientations, demonstrating the accelerometer's ability to provide accurate measurements using gravity as a reference; Frequency Response Test, performed at the Gebze Technical University Earthquake and Structure Laboratory (GTU-ESL), comparing the devices' responses to the GURALP-5TDE reference sensor, with CEDAS_acc evaluated on a shaking table and CEDAS_geo's performance assessed using ambient vibration records; and Noise Test, executed in a low-noise rural area to determine the intrinsic noise of CEDAS_geo, showing its capability to capture vibrations lower than ambient noise levels. Further field tests were conducted on a 10-story reinforced concrete building in Gaziantep, Turkey, instrumented with 8 CEDAS_acc and 1 CEDAS_geo devices. The building's response to a magnitude 3.2 earthquake and ambient vibrations was analyzed, comparing results to the GURALP-5TDE reference sensors and demonstrating the devices' accuracy in capturing peak accelerations and modal frequencies with minimal deviations. The study also introduced the Record Analyzer (RECANA) web application for managing data analysis on CEDAS devices, supporting various data formats, and providing tools for filtering, calibrating, and exporting data. This comprehensive study presents valuable, practical solutions for SHM, enhancing accessibility, reliability, and efficiency in structural and seismic monitoring applications and offering robust alternatives to traditional, costlier systems.

Time-varying modal identification of structures under seismic excitations using a novel time-frequency method

Civil engineering structures are susceptible to damage when subjected to seismic excitations, resulting in presenting nonlinear features and exhibiting time-varying dynamic behavior of structural modal properties. The instantaneous frequency (IF) of structures is thus an essential indicator to evaluate structural damage state. Time-varying filtering empirical mode decomposition (TVF-EMD) holds promise for non-stationary signals decomposition and IF identification due to its robustness against noise interference. However, the application of TVF-EMD is limited by the requirement for manually presetting the decomposition parameters (i.e., B-spline order and bandwidth threshold). In this study, a novel IF identification method based on an improved TVF-EMD algorithm is proposed. In this method, TVF-EMD is firstly improved to accurately decompose the measured responses into mono-component modal responses, which adopts orthogonal coefficient and energy kurtosis as the objective function of salp swarm algorithm (SSA) to adaptively determine the optimal decomposition parameters. Then, the resulting mono-component modal responses are analyzed by local maximum synchrosequencing transform (LMSST) to obtain structural IF under seismic excitations. Examples of non-stationary signals of sinusoidal function, Duffing system, and simulated two-story shear building model under El Centro seismic excitations are used to illustrate the advantages of the proposed method, which proves to be successful in signal decomposition and IF identification. Furthermore, the proposed method is applied to a shaking table test and a real building-Van Nuys hotel under seismic excitations to identify their IF and evaluate structural damage states. The results reveal that the proposed method is efficient for structural IF identification, and can be utilized to qualitatively evaluate the damage severity of structures subjected to strong seismic excitations.

An Output-Only, Energy-Based, Damage Detection Method Using the Trend Lines of the Structural Acceleration Response

Using the trendlines of an acceleration response as a tool to decompose a structural response is a new topic that was proposed by authors in 2020. This paper provides a numerical/experimental investigation of using a Savitzky–Golay filter (SGF) in a method to calculate the trendline and decompose building acceleration responses when subjected to a seismic load. Hence, this paper proposes an output-only, energy-based, damage detection method in which the trend lines of a building’s structural acceleration responses are used to locate the damage. For this purpose, an adjusted SGF is utilized to calculate an especial trend line for each floor’s acceleration response of the building structural model. The energy of these trend lines is then calculated and normalized. Two damage indices are used, of which, the second one is being proposed for the first time in this paper. The accuracy of the proposed method is numerically and experimentally investigated using a five-floor building structural model subjected to white noise excitation through a shake table. The results prove that the proposed method is capable of accurately locating and quantifying structural damages with a severity of more than 10% in a noisy environment. In view that the proposed method locates the damage with no need of determining the structural modal properties or parameters, it can be categorized as an online and quick structural damage detection method.

A novel methodology for modal parameter identification of arch dam based on multi-level information fusion

Operational modal analysis plays an important role in the structural health monitoring and safety diagnosis of arch dam. However, due to background noise, it is difficult to accurately extract the effective characteristics information of arch dam from the vibration responses whose amplitudes are too small under ambient vibration, and the deviation caused by traditional identification methods will directly affect the estimation accuracy for structural modal parameters. Therefore, a novel methodology for modal parameter identification of arch dam based on multi-level information fusion is proposed in this paper. The proposed method is based on multi-sensor data-level fusion to identify the structural natural frequency and damping ratio, which greatly preserves and extracts structural modal properties in the vibration responses. Meanwhile, structural mode shapes are identified based on dynamic feature-level fusion, which significantly improves the identification accuracy. The effectiveness and feasibility of the proposed method are verified by the modal results of digital signals and simulated signals in the 7-DOF system. Prototype engineering case shows that the closely spaced and high-frequency modes can be decomposed and identified by the proposed method, and this method has a higher identification accuracy, which can provide a new idea for modal parameter identification of arch dam.

Daily and seasonal effects of environmental temperature and humidity on the modal properties of structures

Daily and seasonal effects of environmental temperature and humidity on the modal properties of structures

Investigation of time-varying natural frequencies of high-rise buildings under harsh excitations using a high-resolution combined scheme

High-rise buildings subjected to harsh excitations like typhoons or earthquakes usually present nonlinear features and exhibit time-varying behavior of structural modal properties. Under such circumstances, variation in structural natural frequencies can serve as a critical indicator for structural condition assessment. However, limited by noises contained in measurement records and time-frequency resolution of signal analysis, most instantaneous frequency identification methods are difficult to provide accurate estimation of time-varying natural frequencies of high-rise buildings. To address this issue, a combined scheme based on the variational mode decomposition method and adaptive superlets transform (VMD-ASLT) for time-varying natural frequency identification is presented in this paper. Through numerical simulation studies and field measurements of dynamic responses of a 508 m high skyscraper, the noise robustness and accuracy of the combined scheme for time-varying structural natural frequency estimation are validated. Moreover, the combined scheme is further utilized to investigate the time-varying features of natural frequencies of the skyscraper under typhoon and earthquake excitations. This paper aims to provide an effective tool for time-varying structural natural frequency estimation and further reveal the variation of the dynamical behavior of high-rise buildings under harsh excitations.

A Kalman filter based ARX time series modeling for force identification on flexible manipulators

Multi-axial flexible manipulators are structures typically employed in the maintenance of large hydropower equipment such as turbine runners. This kind of multi-process robot was specifically designed for repair jobs related to material removal via grinding or polishing. In general, the estimation of the contact forces between the grinding cup and the workpiece is crucial for improving the robot’s performance and minimizing unwanted vibration at the end effector. However, due to working in a harsh environment, the direct force measurements cannot be implemented. This paper introduces a new method using a Kalman filter based on a time series Auto-Regressive with eXogenous excitation (ARX) modelling to deal with the inverse problem, where ARX model is modified by employing ambient vibration data only. The recursive least squares algorithm is adopted to reduce the effect of high random noises, thus allowing to identify the excitation forces in multiple directions under grinding operations. The proposed method was illustrated via different examples with numerical simulations and experiments on a cantilever beam and on a flexible manipulator. The numerical simulations allow for investigating the robustness of the method against noise, while its ability to identify the forces and the modal properties of structures is also presented.

Parametric system identification of large‐scale structure using decoupled synchronized signals

SummaryLarge‐scale structures are subjected to environmental loads or frequent seismic motion with irremediable effect. These loads have often multidirectional actions on structures, and it couples their responses and leads to multiple‐input multiple‐output (MIMO) problem. The complexity of MIMO model and the relative time‐delays in sensing networks are among major sources of error in dynamic properties identification in large scale structures. This study proposed a parametric‐time domain method to reduce the negative effect of these problems. For this purpose, the contribution of each input in the output signals is determined using QR decomposition and converts a MIMO problem into multiple single‐input multiple‐output (SIMO) ones. In this regard, an Autoregressive Moving Average with eXogenous (ARMAX) model is implemented on decoupled signals for modal identification. Further, for time synchronization of records, a cross‐correlation function has been used to achieve more precise results. The method was employed real strong‐motion response recorded by different sensors at a high rise 64‐story concrete building. Results demonstrate the promising precision of the proposed algorithm for identifying current structural modal properties under real earthquake excitations. Hence, structures can be monitored efficiently along seismic experiences to detect any possible variations in their structural features. The comparison between the output of the proposed method and previous study indicates a considerable improvement on accuracy of the estimated model property particularly on mode shapes.

Damage Detection and Localization on Real Structures Subjected to Strong Motion Earthquakes Using the Curvature Evolution Method: The Navelli (Italy) Case Study

In recent years, structural health monitoring (SHM) has received increasing interest from both research and professional engineering communities. This is due to the limitations related to the use of traditional methods based on visual inspection for a rapid and effective assessment of structures and infrastructures when compared with the great potential offered by newly developed automatic systems. Most of these kinds of systems allow the continuous estimation of structural modal properties that are strictly correlated to the mechanical characteristics of the monitored structure. These can change as a result of material deterioration and structural damage related to earthquake shaking. Furthermore, a suitable configuration of a dense sensor network in a real-time monitoring system can allow to detect and localize structural and non-structural damage by comparing the initial and a final state of the structure after a critical event, such as a relevant earthquake. In this paper, the modal curvature evaluation method, used for damage detection and localization on framed structures, considering the mode curvature variation due to strong earthquake shaking, is further developed. The modified approach is validated by numerical and experimental case studies. The extended procedure, named “Curvature Evolution Method” (CEM), reduces the required computing time and the uncertainties in the results. Furthermore, in this work, an empirical relationship between curvature variation and damage index has been defined for both bare and infilled frames.

Ambient vibration testing and operational modal analysis of monopole telecoms structures

A structural health monitoring (SHM) system was developed to study the ambient response of monopole communication structures in the UK operated by Arqiva Ltd. The exercise had several purposes that included the evaluation of the SHM system itself and the system identification procedures applied to the data, followed by analysis of the evaluated modal properties to validate the current analytical models, structural assessments and standardised design procedures advising on dynamics actions. This paper describes the instrumentation and procedures used during monitoring of a lightweight flexible 14.5 m tubular tapered monopole supporting an array of mobile telecoms antennas. A Bayesian OMA (BAYOMA) approach is implemented to identify structural modal properties under different time windows as comparison for further assessments. Results from stochastic subspace identification are also obtained and compared. The correlation between modal properties and monitoring wind-response data reveals specific tendencies such as nonlinear stiffness behaviour, the existence of aerodynamic damping and typical directionality of the mode shapes with future implications for reformulation of current methods of assessing dynamics on monopole.

Evaluation of contact force distribution along a curve, based on measured electric potentials

The paper presents the first results of an exploratory research work, aiming the experimental evaluation of the mechanical contact between conforming surfaces of metallic bodies. The proposed procedure—that is based on the measurement of electric potentials—is able to determine the actual contact pattern and estimate the force distribution on the opposing surfaces. In the present paper, the contact properties along a curve are evaluated, but the basic principles supporting the solution of the more general surface contact problem are also laid down. The goal of this research work is to provide a reliable basis for the investigation of contact-related effects on the modal properties of structures and machines. The distribution of electric contact resistivity can also be assessed using this method, opening the way to the refined characterization of electric switches and connectors. During the application of the method, measured electric voltage values are processed by a neural network trained on a data set generated by finite element software. The resulting electric contact conductivity distribution is used to determine the contact force distribution, taking into account the equilibrium of forces and moments. The results are also checked by available alternative experimental methods.

Asymptotic identification uncertainty of well-separated modes in operational modal analysis with multiple setups

Operational modal analysis (OMA) aims at identifying structural modal properties with (output-only) ambient vibration data. In the absence of loading information, the identification (ID) uncertainty of modal properties becomes a valid concern in quality control and test planning. One recent development that addresses this aspect is ‘uncertainty law’, which aims at understanding how ID uncertainty depends on test configuration. Mathematically, uncertainty laws in OMA are asymptotic expressions for the ‘posterior’ (i.e., given data) variance of modal parameters. Analogous to the laws of large numbers in statistics, they are often derived assuming long data, small damping, and high signal-to-noise ratio. Following a Bayesian approach, this work develops the uncertainty law for OMA with multiple setup data, a common strategy to produce a ‘global’ mode shape covering a large number of locations with a small number of sensors in individual setups. It advances over previous results for single setup data, and is motivated by questions, e.g., how does the quality of global mode shape depend on sensor locations and setup schedule? Focusing on the case of fixed reference and distinct rovers, analytical study of the eigenvalue properties of mode shape covariance matrix reveals characteristic spatial patterns where principal uncertainty takes place, which can be of local or global nature. The theory is validated with synthetic, laboratory and field test data. By virtue of the Cramer-Rao bound, up to the same modeling assumptions, the uncertainty law dictates the achievable precision limit of OMA regardless of identification method.

A modified teaching–learning optimization algorithm for structural damage detection using a novel damage index based on modal flexibility and strain energy under environmental variations

In this paper, a novel two-stage structural damage detection method using modal flexibility and strain energy-based index (MFSEBI) and modified teaching–learning-based optimization (MTLBO) algorithm is proposed. In the first stage, a novel damage index (MFSEBI) is proposed based on the combination of two structural modal properties including modal strain energy of elements and diagonal members of the structural flexibility matrix to identify the suspected damaged elements. The performance of this indicator is compared with the performance of an indicator derived from the modal strain energy. As the change in the structural modal properties is also affected by environmental variations, in this study, the effect of varying environmental conditions on the performance of the MFSEBI is also examined. In the second stage, the modal response of the structure is used and subsequently updated by MLTBO to estimate the damage extents of the suspected elements. At this stage, the MTLBO algorithm is proposed by enhancing the teaching and learning quality of the TLBO algorithm using new updating mechanisms of each learner position. The performance of the MTLBO and TLBO algorithms is compared in terms of the accuracy of the results and convergence rate. The results of three numerical examples indicate a better performance of the MFSEBI index in damage localization using a smaller number of mode shapes. These results also demonstrate the higher accuracy of the estimated damage extents and the faster convergence rate of the objective function using MTLBO, even with the consideration of measurement noise effects.

Correlation-Based Damage Identification and Quantification Using Modal Kinetic Energy Change

Many existing damage identification or quantification methods can be employed only if the internal and external mass changes are negligible when tested at two different states of a structure. This paper presents a new Modal Kinetic Energy (MKE)-based method to detect and quantify damage using modal properties of structures, which can be employed even in situations when mass change is not more than a certain extent. A new damage sensitivity parameter has been developed using measured modal characteristics of baseline structure. The MKE change (MKEC) concept is then employed to locate damage and to estimate relative perturbation at each element. The relative damage extent vector is estimated by searching the best correlation between the analytical and experimental MKEC vectors with the help of genetic algorithm optimization tool. The extent of damage is calculated after computing damage scaling coefficient using measured eigenvalue change vector. A numerical study is carried out on a simply supported single span beam to confirm its performance under various test conditions. The robustness of the proposed MKE method and the significance of mass variation in the damage detection approach are evaluated by comparing the damage quantification results with a traditional approach. Finally, the proposed damage detection method is applied on a two-span simply supported beam for single and multiple damage scenarios by extracting the modal properties experimentally. The results revealed that the proposed approach is capable of detecting and estimating single and multiple damages with reasonable accuracy even in moderate noise contaminated and mass change environments.

A multi-sensor sub-Nyquist power spectrum blind sampling approach for low-power wireless sensors in operational modal analysis applications

A novel multi-sensor power spectrum blind sampling (PSBS) approach is proposed supporting low-power wireless sensor networks (WSN) for Operational Modal Analysis (OMA) applications. The developed approach relies on arrays of wireless sensors, employing deterministic non-uniform in time multi-coset sampling to acquire structural response acceleration signals at sub-Nyquist sampling rates, treated as realizations of stationary random processes without making any assumption about the average signal frequency content and spectral support. The acquired compressed measurements are transmitted to a central server and collectively processed via a PSBS technique, herein extended to the multi-sensor case, to estimate the power spectral density matrix of an underlying spatially correlated stationary response acceleration random process directly from the compressed measurements. Structural modal properties are then extracted through standard frequency domain decomposition (FDD). The efficacy of the proposed approach to resolve closely-spaced modes is numerically tested for various data compression levels using noisy response acceleration signals of a white-noise excited finite element model of a space truss as well as field-recorded acceleration time-histories of an instrumented bridge under operational loading. It is shown that accurate mode shapes based on the modal assurance criterion can be obtained from as low as 89% less measurements compared to conventional non-compressive FDD at Nyquist sampling rate. Further, significant gains in energy consumption and battery lifetime prolongation of the order of years are estimated, assuming wireless sensors operating on multi-coset sampling at different data compression levels. It is, therefore, concluded that the proposed PSBS approach could provide long-term structural health monitoring systems with low-maintenance cost once wireless sensors with multi-coset sampling capabilities become commercially available.

Correlation of Structural Modal Properties of Go-Kart Frame Structure Using Different Type of Joint in Finite Element Modeling

A go-kart structure is the small four wheeled vehicle that usually used in sports and recreation. The frame of go-kart chassis structure usually consists of joints such as welded joints. In d~sign process, finite element analysis is always used in order to predict the behavior of the designed structure under different kind of loading or force before fabricating the actual structure. Joints in structure are usually difficult to model because of inaccuracy in assuming the properties of joints. Therefore, it is come to agreement that finite element analysis is not always accurate. In this work, attempts to correlate several methods of modeling in order to find the best method to model the welded joint available on the go-kart structure is presented. Natural frequencies obtained through finite element analysis are compared to the benchmark data which is the experimental value of natural frequencies. Comparison is made between rigid body element, beam element (CBEAM and CBAR), rod element (CTUBE) and nodes equivalence strategy. By using the data gathered experimentally, the findings suggested that in general that the beam element (CBAR and CBEAM) is the most suitable element to be used in modeling welded joints in structures apart from other rigid elements.

Scalable Structural Modal Identification Using Dynamic Sensor Network Data with STRIDEX

AbstractThis article uses the formulation of the structural identification using expectation maximization (STRIDE) algorithm for compatibility with the truncated physical model (TPM) to enable scalable, output‐only modal identification using dynamic sensor network (DSN) data. The DSN data class is an adaptable and efficient technique for storing measurements from a very large number of sensing nodes, which is the case in mobile sensor networks and BIGDATA problems. In this article, the STRIDEX output‐only identification algorithm is proposed for the stochastic TPM to estimate structural modal properties (frequencies, damping ratios, and mode shapes) directly from DSN data. The spatial information produced by this novel algorithm, called STRIDEX (“X” for extended), is scalable, as demonstrated in a strategy to construct high‐resolution mode shapes from a single DSN data set using a series of independent identification runs. The ability to extract detailed structural system information from DSN data in a computationally scalable framework is a step toward mobile infrastructure informatics in a large urban setting. The performance of the STRIDEX algorithm is demonstrated, using the simulated response of a 5,000 DOF structure, and experimentally, using measurements from two mobile sensor cars, which scanned about 8,000 points on a beam specimen in the laboratory. In the experimental results, a mobile sensor is shown to provide over 120 times more mode shape points than a fixed sensor.

Biomechanically influenced mobile and participatory pedestrian data for bridge monitoring

Future structural health monitoring systems are evolving toward crowdsourced, autonomous, sustainable forms based on which damage-indicative structural features can be identified. Unlike conventional sensor systems, they serve as non-stationary, mobile, and distributed sensor network components. For example, smartphone sensors carried by pedestrians decouple from the structure of interest, making it difficult to measure structural vibration. Taking bridges as instances, smartphone sensor data contain not only the bridge vibration but also the pedestrians’ biomechanical features. In this article, pedestrians’ smartphone data are used to conduct force estimation and modal identification for structural health monitoring purposes. Two major pedestrian activities, walking and standing, are adopted to estimate walk-induced forces on structures and identify modal parameters, respectively. First, vibration time history of a walking pedestrian combined with pedestrian weight is a measure of dynamic forces imposed on the structure. Second, standing pedestrian’s smartphone sensors provide spectral peaks which are mixtures of structural and biomechanical vibrations. Eliminating biomechanical content reveals structural modal properties which are sensitive to structural integrity. This study presents the first structural health monitoring application recruiting pedestrians in a testbed bridge monitoring example. Orchestrating pervasive and participatory pedestrian data might bring new frontiers to structural health monitoring through a smart, mobile, and urban sensing framework.

Seismic FDD modal identification and monitoring of building properties from real strong-motion structural response signals

In the present study, output-only modal dynamic identification and monitoring of building properties is attempted successfully by processing real earthquake-induced structural response signals. This is achieved through an enhanced version of a recently-developed refined Frequency Domain Decomposition (rFDD) approach, which in the earlier implementation was adopted to analyse synthetic seismic response signals only. Despite that short duration, nonstationary seismic response data and heavy structural damping shall not fulfil traditional Operational Modal Analysis assumptions, the present rFDD response-only algorithm allows for the effective estimation of strong-motion natural frequencies, mode shapes, and modal damping ratios, with real seismic response signals. The present rFDD enhancement derives from a preprocessing time-frequency analysis and from an integrated approach for Power Spectral Density matrix computation, which constitute crucial innovative issues for the treatment of real earthquake response data. A monitoring case study is analysed by taking the real strong-motion response records from a seven-storey reinforced concrete building in Van Nuys, California, from 1987 to the latest 2014 events (Center of Engineering Strong Motion Data database), as recorded before, during and after the 1994 Northridge earthquake, which severely damaged the building (then retrofitted). This paper proves the effectiveness of the proposed enhanced rFDD algorithm as a robust method for monitoring current structural modal properties under real earthquake excitations. This shall allow for identifying possible variations of structural features along experienced seismic histories, providing then a fundamental tool towards Earthquake Engineering and Structural Health Monitoring purposes.

Frequency-independent radiation modes of interior sound radiation: An analytical study

Global active control methods of sound radiation into acoustic cavities necessitate the formulation of the interior sound field in terms of the surrounding structural velocity. This paper proposes an efficient approach to do this by presenting an analytical method to describe the radiation modes of interior sound radiation. The method requires no knowledge of the structural modal properties, which are often difficult to obtain in control applications. The procedure is exemplified for two generic systems of fluid–structure interaction, namely a rectangular plate coupled to a cuboid cavity and a hollow cylinder with the fluid in its enclosed cavity. The radiation modes are described as a subset of the acoustic eigenvectors on the structural–acoustic interface. For the two studied systems, they are therefore independent of frequency.