Random Decrement Technique Research Articles

Low-Cost, Low-Power Edge Computing System for Structural Health Monitoring in an IoT Framework.

A complete low-power, low-cost and wireless solution for bridge structural health monitoring is presented. This work includes monitoring nodes with modular hardware design and low power consumption based on a control and resource management board called CoreBoard, and a specific board for sensorization called SensorBoard is presented. The firmware is presented as a design of FreeRTOS parallelised tasks that carry out the management of the hardware resources and implement the Random Decrement Technique to minimize the amount of data to be transmitted over the NB-IoT network in a secure way. The presented solution is validated through the characterization of its energy consumption, which guarantees an autonomy higher than 10 years with a daily 8 min monitoring periodicity, and two deployments in a pilot laboratory structure and the Eduardo Torroja bridge in Posadas (Córdoba, Spain). The results are compared with two different calibrated commercial systems, obtaining an error lower than 1.72% in modal analysis frequencies. The architecture and the results obtained place the presented design as a new solution in the state of the art and, thanks to its autonomy, low cost and the graphical device management interface presented, allow its deployment and integration in the current IoT paradigm.

Innovative multi-setup modal analysis using random decrement technique: a novel approach for enhanced structural characterization

PurposeThis paper introduces a novel multi-setup merging method and assesses its performance using simulated response data from a Finite Element (FE) model of a five-storey frame and experimental data from a cantilever beam tested in a laboratory setting.Design/methodology/approachIn the research conducted at the Central Building Research Institute (CBRI) in Roorkee, India, a cantilever beam was examined in a laboratory setting. The study successfully extracted the modal properties of the multi-storey building using the merging technique. Identified frequencies and mode shapes provide valuable insights into the building's dynamic behavior, which is essential for structural analysis and assessment. The sensor layout and data merging approach allowed for the capture of relevant vibration modes despite the limited number of sensors, demonstrating the effectiveness of the methodology.FindingsThe results show that reducing the number of sensors can impact the accuracy of the mode shapes. It is recommended to use a minimum of 8 sensor locations (every two floors) for the building under study to obtain reliable benchmark results for further evaluation, periodic monitoring, and damage identification.Originality/valueThe results demonstrate that the developed algorithm can improve the system identification process and streamline data handling. Furthermore, the proposed method is successfully applied to analyze the modal properties of a multi-storey building.

CART-based wide-area damping controller for inter-area oscillations in bulk power system consisting of WAMS data

The paper proposes a WADC approach using CART technique to dampen inter-area oscillations (IAOs) in bulk power systems. In this case, PMU data are filtered to estimate inter-area dynamics in which using pade approximation, a pole-zero IAO compensation block is designed. An online random decrement technique is also developed to identify the coherent groups and damping ratios to activate the WADC for oscillation damping. An offline process is provided to identify 200 critical IAO contingencies and tunes WADC gains using PSO for training CARTs via a set of 200 input inter-area signals and assigning output controlling gains pre-trained data and evaluating the CART estimations through online operation. The WADC approach is validated for oscillation damping on a 39-bus system and a realistic 561-generator Iranian grid. Simulations show 98 % accuracy in achieving sufficient damping ratios (>0.6) across various operating conditions.

Correlation coefficients of vibration signals and machine learning algorithm for structural damage assessment in beams under moving load

This paper presents a novel method of assessing structural damage in beams exposed to moving loads via acceleration signals through experimental studies. In this study, beams are supported on both ends, and their dynamic response to moving loads is assessed. The raw signal has been improved using a random decrement technique. Take measurements from different locations and calculate correlation coefficients between them, then use these as features to evaluate the structure. In order to create a reliable and potential framework for predicting damage efficiently, these features are used as input variables to the machine learning model. The proposed methodology exhibits promising results in accurately discerning and predicting damage in beam structure. It demonstrates a high level of precision to subtle changes in structural integrity when trained by machine learning on the statistical feature extracted from acceleration signals. As a result of this research, methods for detecting structural damage can be made more reliable and efficient by employing machine learning techniques. Additionally, structures operating in dynamic environments can benefit significantly from the proposed methodology.

Dynamic behavior monitoring of twin supertall buildings during Super Typhoon Soksuri using social sensing data

This paper presents the dynamic behavior monitoring of the 300-m-tall Xiamen twin towers during Super Typhoon Soksuri by means of social sensing, which involves two individuals equipped with six smart devices to conduct acceleration measurement and video footage filming. The measured acceleration data are first corrected by the resampling technique since the sampling rate of smart-device-based measurement is unstable, and the corrected building acceleration obtained from different smart devices shows good consistency. Based on the corrected building acceleration responses, the amplitude-dependent and time-variant structural dynamic properties of the monitored twin towers are revealed by the random decrement technique and the Bayesian spectral density approach, respectively. The determined structural dynamic properties are further compared with seven existing prediction models to evaluate their accuracy. Moreover, by employing a computer-vision technique (i.e., the Kanade-Lucas-Tomasi optical-flow approach), the structural dynamic displacement was extracted from the video footage filmed during Soksuri, and its accuracy was further validated through comparison with direct acceleration measurements. This paper aims to demonstrate the feasibility and effectiveness of social sensing for dynamic behavior monitoring of supertall buildings.

Site Characterization and Shallow Shear Velocity through HVSR Measurements around the Hyderabad Metropolitan Region, India

ABSTRACT We investigate the site characterization and shallow shear velocity profiles from the analysis of the Horizontal to Vertical Spectral ratio (HVSR) around the Hyderabad metropolitan region (HMR), which falls under the southern Indian shield. This work uses both the ambient noise and microearthquake data to compute the HVSR, and additionally the Random Decrement technique to compute the HVSR of extracted Rayleigh waves. This study indicates comparable HVSR curves at each station with the three different datasets, from which we obtain the average dominant frequency (f0) and amplification value (A0). They are further used to calculate the seismic vulnerability index value (Kg). We observe that the value of f0 around the HMR is not fixed, but is varying in the range of 3.4 to 18 Hz, whereas the value of A0 is in the range of 1.7 to 12 approximately and Kg in the range of 0.16 to 1.68 approximately, with an exception of ∼ 33 at VKB (Vikarabad) station, which may be due to a local unconsolidated sub-surface structure. Based on the Diffused Field Assumption (DFA), we invert the average HVSR curves and average dispersion curves of Rayleigh waves, and provide the shallow shear velocity profiles up to 300 m, along with an approximate estimate of VS30 (in upper 30 m depth). The estimated VS30 values vary between 911 to 3143 m/s, falling under the classifications A and B (mostly Hard Rock and Rock type) of National Earthquake Hazards Reduction Program (NEHRP) (BSSC, 2001). However, our study shows some stations with shear velocity inversions at shallow depths within 300 m, indicating layers of low velocity, needing further study. In the absence of detailed near-surface findings, these findings are valuable inputs for geotechnical engineering studies and urban-city planning around the HMR, and emphasizes the effectiveness the HVSR method to determine sub-surface topography and/or unknown soil structures as an economical investigation viability.

Condition Monitoring and Quantitative Evaluation of Railway Bridge Substructures Using Vehicle-Induced Vibration Responses by Sparse Measurement

Bridge substructure failure has been responsible for numerous recorded bridge collapses, particularly for small- and medium-span bridges, so it is crucial to effectively monitor the performance of the bridge substructures for efficient maintenance and management. The current vibration-based approaches for quantitatively evaluating bridge substructures rely on in-situ experiments with a multitude of sensors or impact vibration test, making it challenging to implement long-term online monitoring. This paper proposes an accurate, low cost, and practicable method to achieve online quantitative monitoring of railway bridge substructures using only one vibration sensor and operational train-induced vibration responses. The newly derived flexible-base Timoshenko beam models, along with the random decrement technique and Levenberg–Marquardt–Fletcher algorithm, are employed to identify the modal parameters and quantitatively assess the condition of bridge substructures. The proposed method is numerically verified through an established 3D train-bridge-foundation coupling system considering different damage scenarios. In addition, a real-world application is also conducted on the 2nd Songhua River bridge in the Harbin–Dalian high-speed railway, aiming at examining the effectiveness and robustness of the method in condition monitoring of bridge substructure under a complete freeze-thaw cycle. The results indicate that the proposed methodology is effective in extracting the modal parameters and monitoring the state evolution of the bridge substructures, which offers an efficient and accurate strategy for condition monitoring and quantitative evaluation of railway bridge substructures.

Monitoring-based identification of nonlinear aerodynamic damping in multi-mode vortex-induced vibrations of a stay cable

New issues of wind-induced cable vibration, i.e., multi-mode vibrations, increasingly occur in long-span bridges, which are closely associated with underlying damping features. In this study, an analytical method is proposed to identify nonlinear aerodynamic damping in multi-mode wind-induced cable vibrations in fields, involving the assessment of total and mechanical damping. The method is available to extract a series of mono-component resonances/decays from the measured signals based on the analytical mode decomposition (AMD) and random decrement technique (RDT), and then to determine the damping parameters using the Hilbert transform (HT) combined with the smoothing spline or linear least-square fitting. Furthermore, this method is applied to address the vortex-induced vibrations (VIV) of an ultra-long stay cable on the Sutong Bridge. Firstly, the vibrations are reported in terms of a typical event and one-year statistics. Subsequently, the damping parameters are identified to illustrate the in-situ aeroelasticity. The results show that the cable exhibits high-frequency vibrations primarily in the 41st to 43rd modes, and the aerodynamic damping exhibits significant amplitude-dependent nonlinearity. Finally, the damping identifications are employed to reveal the onset mechanism for the vibrations, and a Scruton number (Sc) criterion, i.e., Sc > 5.4, is suggested for mitigating the vibrations.

Influence of the modal damping ratio calculation method in the analysis of dynamic events obtained in structural health monitoring of bridges

This paper includes a review of the several calculation methods available to calculate the modal damping ratio from dynamic events obtained in structural health monitoring of bridges. A comparative analysis among methods involving the logarithmic decrement, bandwidth applied over the spectrum, adjustment to the theoretical curve and random decrement technique is included. Additionally, an alternative calculation method based on signal spectra reduction has been formulated, whose main advantage lies in the fact that it directly allows the study of multiple-degrees-of-freedom systems, which describe typical structures, without the need for applying filters; this method gives extremely precise values despite simplification of the calculations. First, a comparative analysis is carried out on a set of theoretically simulated waves. In these cases, although there are differences in detail, the obtained results are reasonably close to the theoretical values. Second, an analysis of the registered accelerograms during a test campaign of a real structure, the viaduct “The Arches of Alconétar”, is performed. It has been observed that in this case, unlike what happens with the theoretical simulations, the obtained results significantly vary depending on the selected calculation method. For this reason, it is important to know which method has been used to calculate the modal damping ratio in structural health monitoring systems, as well as to be cautious in setting thresholds.

Modal identification with uncertainty quantification of large‐scale civil structures via a hybrid operation modal analysis framework

SummaryIn operational modal analysis (OMA), only structural responses are typically available. In this context, bias and variance (uncertainty) errors may exist in modal estimates (especially damping estimates), resulting in inaccurate determination of the modal properties of large‐scale structures under harsh excitations. To this end, a hybrid OMA framework based on the modal decoupling, the natural excitation technique, the random decrement technique (RDT), and improved eigensystem realization algorithm (ERA) with the automated stabilization diagram is presented to perform high‐accuracy modal estimates with uncertainty quantification for large‐scale structures under normal and severe ambient excitations. The accuracy and effectiveness of the hybrid framework for identifying the modal parameters are validated by numerical simulation study of a framework structural model. Furthermore, the hybrid framework is applied to analyze recorded acceleration responses of a supertall building with 600‐m height under normal excitations and typhoon condition to verify its applicability in field measurements. The numerical simulation and field measurement studies demonstrate that the hybrid framework can not only perform precise modal estimations with uncertainty quantification through a single ambient vibration measurement but also effectively reveal the variations of modal properties of supertall structures under harsh excitations from multiple perspectives. This paper aims to enhance the reliability and accuracy of modal estimation for engineering structures and further provide insight into the variations of dynamic properties of large‐scale civil structures under severe excitations.

Operational modal identification of structures based on improved empirical wavelet transform

When empirical wavelet transform (EWT) is used to identify the modal parameters of civil engineering structures, the frequency band division is generally not accurate due to the noise effect on the Fourier spectrum. This phenomenon will lead to modal mixing and false modes in the analysis results. Therefore, this article establishes a signal frequency band division method by taking advantages of maximal spectrum technique and spectral skewness index. Combining the random decrement technique (RDT) and the least square method of single component modal parameter identification, an adaptive approach based on the improved EWT for operational modal parameter identification of civil engineering structures under stationary environmental excitations is proposed. The traditional frequency band division method based on EWT can not completely and effectively divide the meaningful frequency bands. While the proposed frequency band division technology based on the spectral skewness index can prevent the phenomenon of insufficient division and excessive division and realize adaptive frequency band division. The effectiveness of the proposed approach is validated by a numerical three-story frame and a field three-span concrete box girder bridge under ambient vibrations. The modal identification results from both numerical and experimental validations demonstrate that the proposed approach can effectively and accurately decompose the vibration responses and identify the structural modal parameters under operational conditions.

Support condition identification of wind turbines based on a statistical time-domain damping parameter

Owing to the harsh environment, the support conditions of wind turbines inevitably degrade/change over their lifetime, however, the evolution mechanism is not yet well understood. Although the damping parameters are sensitive to structural support and connection conditions, they are difficult to measure and quantify, which is a challenging inverse problem. This study aims to develop an approach to obtain a statistical time-domain damping parameter (STDP) based on operational vibration signals, and to utilize the parameter to identify support conditions of wind turbines. The proposed approach transforms operational vibration signals to free vibration signals by using the random decrement technique and then performs nonparametric statistical analysis to quantify the statistically significant changes in the damping characteristics of a structure. The effectiveness of the STDP method is verified by two challenging cases of bolted connection damage and soil-structure interaction condition changes. The regression analysis demonstrates the ability of the STDP method for the identification of structural overall damping. In contrast with classic modal analysis methods, the proposed method provides a monotonic relationship between the STDP and support conditions, which is significant for structural condition identification.

Vibration signature effects on damping identification of a RC bridge under ambient vibrations

Experimental tests for dynamic identification of reinforced concrete (RC) bridges by means of Operational Modal Analysis (OMA) are increasingly used in common engineering practice. Nevertheless, especially when measurements are carried out under in-service conditions (i.e. under traffic induced vibrations), some drawbacks should be carefully considered, especially in the damping-ratio quantification. As a matter of fact, the estimation is affected by several factors: i) the length of the signals, ii) the non-stationarity of the input process, and iii) the dependence on the vibration amplitude. Even if the damping ratio is a key parameter in the bridge dynamics, a major part of these aspects has not been yet fully investigated to estimate reliable values.Starting from a dynamic test program on a short-span RC bridge with half-joints in Italy, this paper investigates the issues mentioned above for the damping ratio estimation of the first two modes focusing on: i) the influence of the signal length, ii) the effects of the signals properties, and iii) their correlation with the vibration amplitude. Both the Stochastic Subspace Identification technique fed with the signal covariance (SSI-cov) and the Random Decrement technique (RD) have been used to compute the damping ratios from the collected signals. This paper shows how a convergence of the results cannot be attained by simply increasing the sample size, suggesting that the nature of the vibration itself influences the damping values. A negative, although weak, correlation between the damping ratio and the power of the signals indicates that several factors play a crucial role in the damping estimation in the short span RC bridges with half-joints.

Damage identification for jacket offshore platforms using Transformer neural networks and random decrement technique

A deep learning method is proposed to identify damages in jacket offshore platforms, which integrates the random decrement technique (RDT) and Transformer neural network. RDT is utilized to process the noisy strain response signals from various measurement points on the platform under random excitation, and the Transformer network is used to localize the damages and assess their severities. Numerical simulations and experimental studies are used to demonstrate that the proposed method is effective. The numerical model applied in this study is a jacket platform, including single and multiple damage cases in the entire section and partial section (two-fifths of the entire section) with minor severities (<5%), and different noise levels are considered. Additionally, the effect of measurement positions on identification accuracy of minor and major damage is discussed. Furthermore, the proposed method is validated by experimental studies using a jacket platform model fixed on a shaking table, where different severities of single and multiple damages are considered. Results of the numerical and experimental studies demonstrate that the proposed RDT-Transformer approach can accurately determine the location of structural damage and identify its severity.

Structural damping estimation from live monitoring of a tall modular building

AbstractThe damping ratio is a key indicator of an individual structure's susceptibility to dynamic loads, including the level of discomfort experienced by the occupants of a tall building subjected to wind loading. While computational models, laboratory studies and empirical data can provide estimates of structural damping, the most reliable way to evaluate true damping ratio values is through modal identification using data from field tests on full‐scale finished structures. As an innovative form of construction, high‐rise modular buildings have not been the subject of previous vibration monitoring investigations, implying an absence of essential structural dynamics information. This paper assesses the reliability of four modal identification methods for estimating the damping ratio of a structure using ambient acceleration response data recorded from the world's tallest modular structure, the Ten Degrees building in Croydon, South London. The methods considered are two implementations of the Bayesian fast Fourier transform (BFFT), the random decrement technique (RDT), and a hybrid of the RDT which first decomposes the ambient data into sub‐signals using analytical mode decomposition (AMD‐RDT). Each method is applied to response data collected during 10 significant wind loading events to evaluate the inherent modal properties of the structure, with the computed damping ratio values compared between methods and events. By reporting the first measured damping ratios for a tall modular structure, the paper makes an important contribution to knowledge about the vibration properties of an emerging form of construction.

Vibration-based temporary monitoring of a 253 m tall skew-plan building in Istanbul

A 253 m tall office building in Istanbul with a parallelogram footprint, which has 62 stories in total, 54 tower stories above and 8 podium stories below grade, was monitored using 92 channels of accelerometers deployed on 20 different floors for about 4 days. The structural system of the building consists of reinforced concrete (RC) core shear walls with peripheral composite columns and two-story tall RC outriggers between floors 29 and 31. First 12 natural vibration periods and mode shapes of the building together with the modal directions for the translational modes were identified from the ambient vibration records. These dynamic properties were reproduced with the three-dimensional finite element model developed using gross section properties for all structural members without a need for model updating. The fundamental period of the building at the time of testing, 5.3 s, is expected to lengthen to 5.9 s and 7.8 s upon cracking in structural members for the prescribed service-level and design-level evaluations, respectively, in line with the recent performance-based design guidelines for tall buildings. Damping ratios for the first six vibration modes, with median values of 0.6% and coefficients of variation in the order of 0.3–0.4, were identified through statistical analysis using the random decrement technique. The simulated peak floor accelerations, when the building was subjected to the 2019 Mw 5.8 Marmara Sea earthquake ground motions recorded in the vicinity of the building, showed that ASCE 7-16 in-structure floor acceleration amplifications are exceeded at the lower floors but not reached at the upper floors.

Stereovision-based vibration measurement of stay cable using synchronized multi-camera setup and video motion magnification

This paper proposes a new framework for 3D noncontact stereovision-based displacement measurement in conjunction with Phase-based Video Motion Magnification (PVMM) to capture in-plane and out-of-plane vibrations of stay cables. In the proposed method, cable vibrations are captured by a pair of synchronized digital cameras, then PVMM method is applied to magnify micro vibrations of the cable (ambient responses) recorded by video cameras. Next, a camera calibration technique is proposed to convert the 2D image coordinates to 3D global coordinates, followed by the stereo calibration between the two cameras to synchronize the target points. Afterwards, a Centroid-based Bounding-Box Tracking (CBBT) technique is proposed and used to track the movements of the cable in each video frame. As a result, time-history displacements of the cable are extracted from the magnified videos. Finally, natural frequencies and damping ratios of the cable are extracted from the in-plane and out-of-plane displacements using Wavelet-screening Random Decrement Technique (WS-RDT). The proposed methodology has been validated through a couple of wind-tunnel tests of cable vibrations. The results have demonstrated a good agreement between the proposed method and conventional accelerometer-based measurement results.

An edge-computing framework for operational modal analysis of offshore wind-turbine tower

With massive construction of offshore wind turbines (OWT), structural health monitoring (SHM) techniques have been applied to estimate the working conditions and provide early warnings of structural damage. This study proposes an edge-computing-based SHM system for operational modal analysis (OMA) of an OWT. ThssCe system comprised an open-source data acquisition (DAQ) device, a Raspberry Pi single-board computer, and associated drivers, protocols, and algorithms. The DAQ device can measure the acceleration response of an operating OWT subjected to environmental excitation and transmit raw signals to the Raspberry Pi according to the UDP protocol. The data are further processed to obtain the first-order frequency via an OMA algorithm developed by combining Kalman filtering, the random decrement technique (RDT), and the stochastic subspace identification (SSI) method. The proposed SHM system was implemented in a scaled OWT model to verify its feasibility for field use. The experimental results indicate that (1) the edge-computing-based SHM system can accurately measure the response of the OWT; (2) the proposed algorithm can estimate the modal information of the OWT during operation; (3) the variation in the first-order frequency induced by lateral loads can be identified by the SHM system. Unlike a centralized SHM system, the proposed edge-computing-based SHM system does not require massive data transmission, making it promising for offshore environments.

Amplitude dependence of natural frequency and damping ratio for 5 supertall buildings with moderate-to-strong typhoon-induced vibrations

Modal parameters play a significant role in the wind-resistant design and vibration control of high-rise buildings. Results from previous studies demonstrate that the natural frequency and damping ratio of high-rise buildings usually exhibit nonlinear variation characteristics with the increase of structural amplitude. However, due to lacking relevant field measurements, the features of natural frequency and damping ratio for super-tall buildings under large wind-induced amplitudes have been less explored. This paper presents a field study on typhoon-induced structural responses of five supertall buildings whose structural heights all exceed 300 m. The peak acceleration responses at the measurement height of most buildings exceeded 10 cm/s2 (gal), with the maximum response reaching 20 gal. Two frequently used methods, i.e., the stochastic subspace identification (SSI) and random decrement technique (RDT) methods are utilized to compare and analyze the amplitude-dependent characteristics of target modal parameters. It is found that with the increase of wind-induced structural vibration amplitude, the fundamental natural frequency generally first decreased and then leveled off, while the damping ratio for the first-order mode of the buildings except the one equipped with a tuned mass damper (TMD) system first increased while then tended to decrease. More interestingly, the critical normalized structural vibration amplitude associated with the maximum damping ratio is found to be in the range of 10−4∼10−5, which agrees well with the theory of “critical tip drift ratio”. Potential explanations for the above phenomena are presented. Furthermore, the obtained results are further compared with existing statistical models, and associated discrepancies suggest the complexities of the dynamic features of supertall buildings.

Investigating the operational modal response and updating the finite element model of a prototype stone structure

In this work, the analysis of the dynamic behavior of a prototype stone structure has been presented in two ways: (1) the first way is to instrument the structure with two three-component velocity sensors to measure the structure's response to ambient solicitations. An acquisition and processing chain has been developed in our laboratory to record the acquired vibratory signals. The data processing is obtained using different methods of operational modal analysis (OMA) such as Frequency Domain Decomposition (FDD), Random Decrement Technique (RDT), and Stochastic Subspace Identification (SSI). The results obtained by these methods allowed us to extract the modal parameters (damping and natural frequency) of the structure. (2) The second way consists of characterizing the structure's dynamic behavior in a numerical way using Finite Element Modeling (FEM). However, it has often been noted that the experimental dynamic characteristics obtained do not agree with those of the numerical model. The present study aims to extract the dynamic parameters experimentally and numerically and to update the finite element model for stone construction. A good agreement was found between the two approaches.