Instantaneous modal identification under varying structural characteristics: A decentralized algorithm

Quqa, Said; Landi, Luca; Diotallevi, Pier Paolo

doi:10.1016/j.ymssp.2020.106750

Cited by 30 publications

(32 citation statements)

References 70 publications

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“…In this paper, a filter bank formed of the bandpass filters

b_{k} = d_{k} * r_{k}

is considered, with

d_{k}

and

r_{k}

denoting the

k

‐th equivalent decomposition and reconstruction filters associated with an

n

‐th level stationary wavelet packet transform (SWPT). The size and performance of these filters were investigated in a previous work, 28 showing their applicability to near‐real‐time identification procedures.…”

Section: Modal Assurance Distributionmentioning

confidence: 99%

“…The

boldM

matrix represents a “mask” of 1 and 0 values that selects high‐valued areas in the MAD. Morphological operations, such as erosion, can be applied to

boldM

if a high transformation level is selected for signal decomposition since filter overlaps may origin spurious ODS similarities 28 . In a morphological operation, each element

μ_{k} false[t false]

is interpreted as a pixel in the time‐frequency distribution and is adjusted based on the value of neighboring pixels 29 .…”

Section: Modal Assurance Distributionmentioning

confidence: 99%

“…It is worthy to note that the process described above can be seen as the construction of a signal‐adaptive filter bank with band‐variable filters capable of extracting decoupled modal responses through cutoff frequencies selected based on the clustering process. This process can be, therefore, intended as an adaptive generalization of the clustered filter bank procedure proposed in a previous work 28 for applications with decentralized smart sensor networks.…”

Section: Modal Assurance Distributionmentioning

confidence: 99%

See 2 more Smart Citations

Seismic structural health monitoring using the modal assurance distribution

Quqa

Landi

Diotallevi

2021

Earthq Engng Struct Dyn

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Thanks to emerging techniques in the field of signal processing and due to improvements in smart sensing systems which enable the event‐triggered acquisition of high‐fidelity data at the occurrence of strong ground motion events, seismic structural health monitoring has grown considerably in the last few decades. In this paper, the modal assurance distribution, an alternative time‐frequency representation of the modal features of multivariate and multicomponent signals, is extended for application to short‐term nonstationary vibrational structural responses in which the system may manifest its nonlinear behavior. A general procedure for the extraction of the decoupled normal modes is presented, which allows the identification of instantaneous modal parameters in order to investigate in detail the structural behavior during earthquakes. Valuable information that cannot be recovered by means of traditional criteria can thus be exploited for accurate damage assessment. The results obtained for two case studies consisting of a numerical model with softening nonlinear behavior and a full‐scale experimental reinforced concrete benchmark show the potential and applicability of the method proposed for the integrity assessment of civil structures.

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“…In this paper, a filter bank formed of the bandpass filters

b_{k} = d_{k} * r_{k}

is considered, with

d_{k}

and

r_{k}

denoting the

k

‐th equivalent decomposition and reconstruction filters associated with an

n

Section: Modal Assurance Distributionmentioning

confidence: 99%

“…The

boldM

matrix represents a “mask” of 1 and 0 values that selects high‐valued areas in the MAD. Morphological operations, such as erosion, can be applied to

boldM

if a high transformation level is selected for signal decomposition since filter overlaps may origin spurious ODS similarities 28 . In a morphological operation, each element

μ_{k} false[t false]

is interpreted as a pixel in the time‐frequency distribution and is adjusted based on the value of neighboring pixels 29 .…”

Section: Modal Assurance Distributionmentioning

confidence: 99%

Section: Modal Assurance Distributionmentioning

confidence: 99%

See 1 more Smart Citation

Seismic structural health monitoring using the modal assurance distribution

Quqa

Landi

Diotallevi

2021

Earthq Engng Struct Dyn

Self Cite

View full text Add to dashboard Cite

show abstract

“…The application of these algorithms provides near real-time estimates of the modal parameters that can be used to extract instantaneous information about the structural state. In Reference [26], a novel decentralized approach for instantaneous identification of modal parameters was presented, involving a computational cost of O(pN) per input sample, onboard each node, where p is the number of identified modes and N is the length of the filters employed in the procedure. In Reference [27], Quqa et al used instantaneous values of the modal parameters computed through the identification method proposed in Reference [26] to estimate a local approximation of the instantaneous values of the interpolation error [5].…”

Section: Introductionmentioning

confidence: 99%

Statistical Approach for Vibration-Based Damage Localization in Civil Infrastructures Using Smart Sensor Networks

2021

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One of the most discussed aspects of vibration-based structural health monitoring (SHM) is how to link identified parameters with structural health conditions. To this aim, several damage indexes have been proposed in the relevant literature based on typical assumptions of the operational modal analysis (OMA), such as stationary excitation and unlimited vibration record. Wireless smart sensor networks based on low-power electronic components are becoming increasingly popular among SHM specialists. However, such solutions are not able to deal with long data series due to energy and computational constraints. The decentralization of processing tasks has been shown to mitigate these issues. Nevertheless, traditional damage indicators might not be suitable for onboard computations. In this paper, a robust damage index is proposed based on a damage sensitive feature computed in a decentralized fashion, suitable for smart wireless sensing solutions. The proposed method is tested on a numerical benchmark and on a real case study, namely the S101 bridge in Austria, a prestressed concrete bridge that has been artificially damaged for research purposes. The results obtained show the potential of the proposed method to monitor the conditions of civil infrastructures.

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“…The presence of damage is detected by variations in these parameters in the damaged configuration compared to the reference state (undamaged) evaluated before the event (sometimes seconds before an event for continuously monitored structures). There are several approaches that allow such comparisons [30][31][32][33][34][35][36][37][38][39][40][41][42] to be made. It has been observed that methods based on the analysis of variations in the modal shapes and/or their derivatives, such as the mode curvature, are very effective [1] and can also be used as a diagnostic tool for structural and/or non-structural damage localization and quantification.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Instantaneous modal identification under varying structural characteristics: A decentralized algorithm

Cited by 30 publications

References 70 publications

Seismic structural health monitoring using the modal assurance distribution

Seismic structural health monitoring using the modal assurance distribution

Statistical Approach for Vibration-Based Damage Localization in Civil Infrastructures Using Smart Sensor Networks

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

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