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• Bell swinging registered signals have been compared with these proposed by DIN4178 and some unsafe situations are detected. • By OMA analysis, damping ratio is evaluated under ambient and forced vibrations. • The use of genetic algorithms for the model updating presents a high dispersion of the results. • The optimum lateral confinement has been evaluated to update the FEM model. • A general methodology is proposed to analyze the damage generated by the swing of bells. Recent years have seen a growing interest toward implementation and testing of structural health monitoring techniques for cultural heritage structures, and many scientific papers report on the application of operational modal strategies as an effective knowledge-based tool for vulnerability reduction of masonry buildings. Focusing on historic masonry bell-towers, being such structures particularly prone to earthquake-induced damage, the most part of the studies discuss structural monitoring and vibration-based identification methods with the goal of their seismic protection. As a consequence, while there is great number of researches that investigate masonry towers behaviour under earthquake loads, only a few scientific papers discuss their structural response under service loads such as bell-loads. This issue is also of paramount importance, since in many real cases the bell-ringing has been stopped due to the dynamic interaction phenomena that are activate between the bells and the host structure. With the aim to contribute of improving the knowledge in this field, this paper focuses on a methodology for the study of the dynamic interaction between bells and slender masonry towers. The proposed methodology is divided into four phases: (i) Geometric and structural characterization of the tower and bells; (ii) Evaluation of the dynamic forces generated by the swinging bells; (iii) Experimental campaign to characterize the dynamic properties of the tower by means of operational modal analysis; (iv) Parametric finite element analysis. To illustrate the methodology, a real case of masonry bell-tower in which bell-ringing had to be stopped due to a history of strong vibrations is discussed. The paper includes a method of analysing the dynamic properties of masonry bell-towers, in which the dynamic interaction between the harmonic bell forces and the fundamental tower modes is analysed by means of a calibrated numerical model and the dynamic amplification factor. [ABSTRACT FROM AUTHOR]

Highlights • We present an enhanced method for scaling modes from operational modal analysis. • The new method includes residual terms. • The new method can handle closely coupled and repeated modes. • We verify the method on two sets of experimental data. Abstract Modal models from operational modal analysis (OMA) lack information about the modal scaling (or, modal mass). Many times, this is not a limitation. However, in some engineering applications, e.g. for structural health monitoring or computational model validation, a scaled modal model may be important. The authors of this paper recently presented a new approach for scaling modal models using harmonic excitation. The method was named the OMAH method, and it was shown that the approach is robust and reliable. However, the proposed technique relied on the measurement of the structural response between two arbitrary degrees of freedom (DOFs) for each mode. In the present paper, the method is extended to the case in which several excitation and response DOFs may be taken into account for the scaling. This is analogue to using global parameter estimation methods in classical experimental modal analysis. The proposed multiple-reference technique includes the capability of estimating residual terms to account for modes outside the frequency range of interest. The method is validated on two sets of data from real full scale structures, and the accuracy of the scaling using several response DOFs is shown to improve compared to using only a single excitation and response DOF. The proposed formulation, furthermore, allows one or more excitation DOFs as well as one or more response DOFs to be used for the scaling of the modal model. Thus, one single formulation can be used regardless of whether multiple-references are necessary, or not. [ABSTRACT FROM AUTHOR]

• Compressed Sensing is applied to full-field optical vibration data. • A combined Compressed Sensing/Shape Descriptor method is presented. • Huge data reduction is achieved for purposes of transmission and storage. • Operational modal analysis is demonstrated using recovered full-field data. • Application of the methodology is demonstrated using industrial DIC data. Optically-acquired data, typically from digital image correlation, is increasingly being used in the area of structural dynamics, particularly modal testing and damage identification. One of the problems with such data is its extremely large size. Single images regularly extend to tens or even hundreds of thousands of data points and many thousands of images may be required for a vibration test. Such data must be stored and transmitted efficiently for later remote reconstruction and analysis, typically operational modal analysis. It is this requirement that is addressed in the research presented in this paper. This research builds upon previous work whereby digitised optical data was projected onto an orthogonal basis with coefficients (shape descriptors) of either greater or lesser significance; those deemed to be insignificant, according to a chosen threshold being removed. Data reduction by a combination of shape-descriptor decomposition and compressed-sensing is applied to an industrial printed circuit board and reconstructed for operational modal analysis by ℓ 1 optimisation. [ABSTRACT FROM AUTHOR]

Long multi-span bridges represent a broad section of the civil roadway infrastructure. Despite being so common, their condition-based maintenance through vibration-based Structural Health Monitoring (SHM) has been scarcely investigated in the literature. The dynamic identification of such structures through Operational Modal Analysis (OMA) is especially challenging due to of their quasi-periodic nature and the common existence of weak inter-span coupling. Even when designed following an isostatic scheme, there always exists a certain degree of coupling between spans due to the continuity of the deck, the pavement and imperfect expansion joints. Hence, the modal poles of the spans typically appear as dense clusters with closely spaced frequencies and mode shapes with similar wavelengths, which significantly hinders the identification of physical poles through stabilization diagrams. In this light, this paper proposes a model-based machine learning approach to conduct and interpret the OMA results of partially continuous multi-span bridges. The proposed method is a hierarchical clustering approach that leverages on the analytical solution of the vertical free vibration response of multi-span girders with weak inter-span rotational coupling, allowing the estimation of the modal features of any bridge configuration ranging from simply supported to perfectly continuous conditions. Detailed parametric analyses and discussions are presented to appraise the correlation between the inter-span rotational coupling and the clustering of the modal poles of multi-span bridges, as well as the influence of damage conditions of varying severity and extension. On this basis, a model-based cut-off distance threshold for hierarchical clustering of stable poles is proposed to assist the automation of the global OMA of multi-span bridges. The developed formulation is tested in a real-world in-operation seven-spans reinforced concrete girder bridge, the Trigno V Bridge in Italy. • New analytical solutions for free vibration of partially-continuous multi-span bridges are derived. • The effects of inter-span rotational coupling upon the modal properties is analysed. • An innovative model-assisted clustering method to conduct automated OMA is proposed. • The effects of damage with varying severity and extension are investigated. [ABSTRACT FROM AUTHOR]

An optically based sensing system that can measure the displacement and strain over essentially the entire area of a utility-scale blade leads to a measurement system that can significantly reduce the time and cost associated with traditional instrumentation. This paper evaluates the performance of conventional three dimensional digital image correlation (3D DIC) and three dimensional point tracking (3DPT) approaches over the surface of wind turbine blades and proposes a multi-camera measurement system using dynamic spatial data stitching. The potential advantages for the proposed approach include: (1) full-field measurement distributed over a very large area, (2) the elimination of time-consuming wiring and expensive sensors, and (3) the need for large-channel data acquisition systems. There are several challenges associated with extending the capability of a standard 3D DIC system to measure entire surface of utility scale blades to extract distributed strain, deflection, and modal parameters. This paper only tries to address some of the difficulties including: (1) assessing the accuracy of the 3D DIC system to measure full-field distributed strain and displacement over the large area, (2) understanding the geometrical constraints associated with a wind turbine testing facility (e.g. lighting, working distance, and speckle pattern size), (3) evaluating the performance of the dynamic stitching method to combine two different fields of view by extracting modal parameters from aligned point clouds, and (4) determining the feasibility of employing an output-only system identification to estimate modal parameters of a utility scale wind turbine blade from optically measured data. Within the current work, the results of an optical measurement (one stereo-vision system) performed on a large area over a 50-m utility-scale blade subjected to quasi-static and cyclic loading are presented. The blade certification and testing is typically performed using International Electro-Technical Commission standard (IEC 61400-23). For static tests, the blade is pulled in either flap-wise or edge-wise directions to measure deflection or distributed strain at a few limited locations of a large-sized blade. Additionally, the paper explores the error associated with using a multi-camera system (two stereo-vision systems) in measuring 3D displacement and extracting structural dynamic parameters on a mock set up emulating a utility-scale wind turbine blade. The results obtained in this paper reveal that the multi-camera measurement system has the potential to identify the dynamic characteristics of a very large structure. [ABSTRACT FROM AUTHOR]

Highlights • First review paper focused on transmissibility-based system identification. • Fundamental concepts of transmissibility and its basic properties are summarized. • The literature review categorizes transmissibility functions as local and global. • Application of transmissibility function in modal analysis, damage detection and model updating are reviewed. • Gaps requiring additional work and possible future trends are highlighted. Abstract The difficulty of achieving controlled input has led to the development of new output-only structural health monitoring (SHM) approaches. Without measuring the input or assuming a specific input model, a transmissibility function (TF) as a mathematical representation of the output-to-output relationship has proven to be an attractive tool in SHM. The state of the art and challenges to TF analysis that cast SHM in the context of a system identification (SI) paradigm are reviewed and discussed in this study. This review starts with an overview of the fundamentals of TFs by illustrating its categories, connections with frequency response functions (FRFs), and basic properties. By categorizing TFs as local and global, this literature review limits the practice of various methodologies to the following key aspects: modal analysis, damage detection, and model updating. A brief treatment of the basic ideas is presented while a comprehensive and critical explanation of their contents is not attempted. Based on the review, existing studies are discussed, highlighting gaps requiring additional work and possible future trends for TF-based system identification. [ABSTRACT FROM AUTHOR]

Validating numerical models are one of the main aspects of engineering design. However, correlating million degrees of freedom of numerical models to the few degrees of freedom of test models is challenging. Reduction/expansion approaches have been traditionally used to match these degrees of freedom. However, the conventional reduction/expansion approaches are only limited to displacement, velocity or acceleration data. While in many cases only strain data are accessible (e.g. when a structure is monitored using strain-gages), the conventional approaches are not capable of expanding strain data. To bridge this gap, the current paper outlines a reduction/expansion technique to reduce/expand strain data. In the proposed approach, strain mode shapes of a structure are extracted using the finite element method or the digital image correlation technique. The strain mode shapes are used to generate a transformation matrix that can expand the limited set of measurement data. The proposed approach can be used to correlate experimental and analytical strain data. Furthermore, the proposed technique can be used to expand real-time operating data for structural health monitoring (SHM). In order to verify the accuracy of the approach, the proposed technique was used to expand the limited set of real-time operating data in a numerical model of a cantilever beam subjected to various types of excitations. The proposed technique was also applied to expand real-time operating data measured using a few strain gages mounted to an aluminum beam. It was shown that the proposed approach can effectively expand the strain data at limited locations to accurately predict the strain at locations where no sensors were placed. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new damage detection methodology for beams. It applies wavelet analysis to locate the damage from changes in the mode shapes (geometric based analysis). The proposed methodology requires the mode shapes of a reference undamaged state as well as those of the potentially damaged one. Once obtained, a continuous wavelet transform is applied to the difference of the mode shape vectors to obtain information of the changes in each of them. Finally, the results for each mode are added up to compute an overall result along the structure. For the addition, the wavelet coefficients of each mode are weighted according to the corresponding variation of the natural frequency. By doing so, emphasis is given on those modes that are more likely to be affected by damage. On the other hand, mode shapes that have not changed their natural frequencies are disregarded. The proposed methodology also includes mathematical techniques to avoid wavelet transform edge effect, experimental noise reduction in mode shapes and creation of new virtual measuring points. It has been validated by experimental analysis of steel beams with cracks of different sizes and at different locations. The results show that the method is sensitive to little damage. The paper analyses the severity threshold of damage and the required number of sensors to obtain successful results. [Copyright &y& Elsevier]

Abstract: The paper presents the dynamic structural health monitoring activities on Saint Torcato church, in Guimarães, Portugal, which has significant structural problems due to soil settlements. Cracks can be observed on the main and the lateral façades, the bell-towers are leaning, and the arches in the nave exhibit a failure mechanism with cracks and vertical deformations. These phenomena are progressing and a structural intervention is planned. A monitoring system has been installed to control the current condition and to assess the success of the future intervention. The paper shows the monitoring results with an emphasis in the dynamic analysis carried out before the structural strengthening, namely with respect to: experimental tests with output-only techniques for frequencies, damping and mode shapes estimation, FE model updating analysis and dynamic monitoring. The automatic system identification process uses ambient vibration signatures in combination with cluster analysis and rule-based approach for the interpretation of the results of the Stochastic Subspace Identification method. [Copyright &y& Elsevier]

Model-based damage detection entails the calibration of damage-indicative parameters in a physics-based computer model of an undamaged structural system against measurements collected from its damaged counterpart. The approach relies on the premise that changes identified in the damage-indicative parameters during calibration reveal the structural damage in the system. In model-based damage detection, model calibration has traditionally been treated as a process, solely operating on the model output without incorporating available knowledge regarding the underlying mechanistic behavior of the structural system. In this paper, the authors propose a novel approach for model-based damage detection by implementing the Extended Constitutive Relation Error (ECRE), a method developed for error localization in finite element models. The ECRE method was originally conceived to identify discrepancies between experimental measurements and model predictions for a structure in a given healthy state. Implementing ECRE for damage detection leads to the evaluation of a structure in varying healthy states and determination of discrepancy between model predictions and experiments due to damage. The authors developed an ECRE-based damage detection procedure in which the model error and structural damage are identified in two distinct steps and demonstrate feasibility of the procedure in identifying the presence, location and relative severity of damage on a scaled two-story steel frame for damage scenarios of varying type and severity. [ABSTRACT FROM AUTHOR]

Abstract: Automated modal parameter identification of civil engineering structures has been analyzed in a previous paper. An original algorithm, named LEONIDA, working in frequency domain, has been presented and a number of test cases have been discussed in order to point out advantages and drawbacks. It has been demonstrated that LEONIDA represents a promising and reliable tool, in particular for modal testing. Conversely, integration of such a procedure into a fully automated structural health monitoring (SHM) system has shown that it can be used as modal information engine, but length of record durations, amount of computational burden and response time lead to recognize that serious drawbacks and limitations exist for a class of applications, such as continuous monitoring of structures in seismically prone areas. In fact, a fast assessment of relevant structure health conditions in the early post-earthquake phase is becoming of interest in different European areas. In such a context, the statistical treatment of measured dynamic properties could be certainly useful, but it requires the collection of an extensive amount of local and global data in a short time. As a consequence, availability of reliable, robust and fairly fast data processing procedures for modal tracking is fundamental whenever really effective and useful SHM systems are adopted to support civil protection activities during seismic sequences. This applies mainly to strategic structures, whose health conditions must be rapidly assessed after any seismic event, in order to securely manage rescue operations. In the present paper, the main issues related to a fast, robust and reliable modal tracking for emergency management are outlined. Then, an automated modal tracking strategy for SHM applications in earthquake prone regions is described. It is based on the knowledge of the experimental mode shapes and a revised concept of spatial filtering. Results of sample applications of the proposed procedure refer to simulated data and to real measurements collected by a SHM system. The latter are representative of operational conditions and of the transient response due to the ground motion induced by the recent L''Aquila earthquake mainshock. Discussion of results will point out advantages and limitations of the data processing strategy. [Copyright &y& Elsevier]

• A robust fuzzy-driven approach is derived for fully IO/OO automated modal analysis. • The methodology is applicable using any growing model-order identification method. • A new indicator called nMTN is derived for emphasizing weakly excited modes. • The efficiency of the strategy is demonstrated on actual shaking-table tests results. A crucial step when identifying the modal signature of systems using growing order parametric methods consists in discriminating spurious modes from physical modes. In this paper, a three-stages clustering strategy is presented in a fuzzy framework for automating this selection process in the context of Input/Output and Output-Only identification. The novelty and strong point of the approach lies in the first stage where, after computation of single mode validation indicators, a modified fuzzy c-means clustering procedure is developed for performing a first partition. It is shown how the membership function obtained for the cluster of physical modes can be interpreted as a new synthetic modal indicator and helps with pole-splitting detection, outlier rejection and generally improves the final modal parameters estimation. The developed methodology does not involve any user-specified threshold and can be used for discriminating modes produced by any methodology consisting in fitting a growing order model to experimental data of any type. In this paper, accelerations measured during the SMART2013 shaking-table test campaign are processed using data-driven state-space identification algorithms. The automated selection process is used for tracking the modal signature of a trapezoidal shaped reinforced-concrete specimen using in turn stochastic and combined deterministic-stochastic algorithms, defining for the latter the movement of the shaking table as input. Variations in the modal signature are then correlated to the damage actually observed on the specimen and a comparison between Output-Only and Input/Output results is made in order to estimate the interaction between the specimen and the whole shaking table device. [ABSTRACT FROM AUTHOR]

In the last decades the need for an effective seismic protection and vulnerability reduction of cultural heritage buildings and sites determined a growing interest in structural health monitoring (SHM) as a knowledge-based assessment tool to quantify and reduce uncertainties regarding their structural performance. Monitoring can be successfully implemented in some cases as an alternative to interventions or to control the medium- and long-term effectiveness of already applied strengthening solutions. The research group at the University of Padua, in collaboration with public administrations, has recently installed several SHM systems on heritage structures. The paper reports the application of monitoring strategies implemented to avoid (or at least minimize) the execution of strengthening interventions/repairs and control the response as long as a clear worsening or damaging process is detected. Two emblematic case studies are presented and discussed: the Roman Amphitheatre (Arena) of Verona and the Conegliano Cathedral. Both are excellent examples of on-going monitoring activities, performed through static and dynamic approaches in combination with automated procedures to extract meaningful structural features from collected data. In parallel to the application of innovative monitoring techniques, statistical models and data processing algorithms have been developed and applied in order to reduce uncertainties and exploit monitoring results for an effective assessment and protection of historical constructions. Processing software for SHM was implemented to perform the continuous real time treatment of static data and the identification of modal parameters based on the structural response to ambient vibrations. Statistical models were also developed to filter out the environmental effects and thermal cycles from the extracted features. [ABSTRACT FROM AUTHOR]

• The problem of damage identification in nonproportionally damped systems is analysed. • Two structures are modelled to track global and local complexity with increasing damage. • The pointwise change of the real and imaginary parts of the modes is examined. • The complex modal analysis is extended to a real structure experimentally damaged. • A new index based on complex eigenmode variation is proposed for damage localization. Real structures mode shapes estimated by modal analysis techniques have a common feature: in most cases they are complex, and their level of complexity can be soundly influenced by the presence and extent of physical damage, which also affects the distribution of energy dissipation mechanisms within the structures. Starting from the contributions available in the literature, the present paper investigates, from a numerical and experimental point of view, the correlation existing between localized damage and variation of global modal complexity indices conventionally employed to quantify the nonproportionality of damping in structural systems. Finally, driven by the inferences made through numerical and experimental test cases by tracking the variation of complex modes over multiple and progressive damage scenarios, a new index for damage localization and quantification is formulated and validated against real data. [ABSTRACT FROM AUTHOR]

In recent years, a new research direction in structural condition assessment has been focusing on developing automated or semi-automated procedures to identify a structure's modal parameters from its response measurements. This is because long-term structural monitoring systems rely on the implementation of system identification methodologies that often involve the intervention of an expert user with an acquired experience in the field. This paper aims to offer a semi-automated methodology for extracting the modal parameters independently of the chosen parametric system identification technique with minimum user involvement in the parameter selection process. Here, the framework is applied to two different parametric system identification algorithms: Data-Driven Stochastic Subspace Identification (DD-SSI) and Output Only Observer Kalman Filter (O/O OKID). The procedure can be represented as a multi-stage strategy where unsupervised tools and three clustering options are offered to the user to reach a reliable estimate of the modal parameters. The proposed procedure is validated with an application in the operational modal analysis of an existing hospital structure located in Italy. The results demonstrated excellent accuracy and robust performance of the methodology, even in the presence of closely spaced modes. The proposed procedure helps to improve the data analysis process in continuous monitoring, where usually, the algorithm's parameters need to be constantly updated by the user. • Semi-automated operational modal analysis with low user interaction • Methodology independent from the parametric system identification algorithm adopted • Three clustering techniques available to the user: two k-means approaches and DBSCAN • Application on the analysis of vibration data recorded on a RC hospital structure • Validation using two system identification algorithms: DD-SSI and O/O OKID [ABSTRACT FROM AUTHOR]

• Three closely spaced sensors to locate Acoustic Emission in complex plates. • A novel single sensor modal analysis approach, effective in complex structures. • Feasible for application in a wireless sensor node. • Testing conducted on real aircraft structures. The use of three piezoelectric sensors in a small array has previously been investigated to assess its ability to locate Acoustic Emission (AE) sources in simple aluminium and composite structures. It has been theorised that this approach could be used within wireless systems to monitor AE in aircraft, as the close spacing of the sensors removes the need for power-intensive time synchronisation between nodes and reduces excessive cabling. There are, however, two major limitations to this, primarily that the localisation techniques used are only viable within simple structures and additionally that the high computational requirements of the continuous wavelet transform which the technique utilises is infeasible for a low power system. This paper presents modifications to this approach, including the novel second differential method for single sensor modal analysis, which enables the technique to locate AE events within complex structures. A mapping technique is also presented, which accounts for complexity and varying wave velocity in composite structures. Testing is presented which uses artificial sources on a range of structures, from simple plates to an Airbus A350 aircraft wing, with a high level of accuracy and repeatability shown. [ABSTRACT FROM AUTHOR]

• A new automated procedure for the Operational Modal Analysis. • Introduction of a setup procedure to reduce the modelling errors. • Introduction of a Modal Quality Index. • Application and discussion of the results on two historic masonry towers. Many efforts have been made in the last decade to define Automated Operational Modal Analysis (AOMA) procedures able to process large datasets from long-term monitoring systems. However, some issues are still open and further studies on this topic are needed; in particular following points should be better investigated: i) controlling the tuning parameters to minimize the modelling errors, ii) defining suitable automatization method for processing large datasets and iii) validating the extracted modal parameters. These points are investigated in this paper with the aim of enhancing the current AOMA procedures based on the Stochastic Subspace Identification (SSI) techniques, and the following novelties are introduced: i) a minimization approach for the tuning of the initial parameters in the SSI algorithm, ii) a statistical method to automatically define the cut-off threshold in the hierarchical clustering phase, and iii) a Modal Quality Index (MQI) – ranging from 0 to 1 – to validate the identified modes. The above novelties represent key aspects that allow a real-time check of the modal parameters provided by a monitoring system within a Continuous Structural Health monitoring (CSHM) framework. [ABSTRACT FROM AUTHOR]

Abstract: For reliable performance of vibration-based damage detection algorithms, it is important to distinguish abnormal changes in modal parameters caused by structural damage from normal changes due to environmental fluctuations. This paper firstly addresses the modeling of temperature effects on modal frequencies of a PSC box girder bridge located on the A1 motorway in France. Based on a six-month monitoring experimental program, modal frequencies of the first seven mode shapes and temperatures have been measured at three hour intervals. Neural networks are then introduced to formulate regression models for quantifying the effect of temperature on modal parameters (frequencies and mode shapes). In 2009, this bridge underwent a strengthening procedure. In order to assess the effect of strengthening on the vibration characteristics of the bridge, modal properties had to be corrected from temperature influence. Thus, the first goal is to assess the changes on the vibration signature of this bridge induced by the strengthening. For this purpose, classical statistical analysis and clustering methods are applied to the data recorded over the period after strengthening. The second goal is to evaluate the influence of temperature effects on the clustering results. It comes that the temperature correction significantly improves the confidence in the novelty detection and in the strengthening efficiency. [Copyright &y& Elsevier]

Abstract: Continuous monitoring of structural vibrations is becoming increasingly common as sensors and data acquisition systems become more affordable, and as system and damage identification methods develop. In vibration-based structural health monitoring, the dynamic modal parameters of a structure are usually used as damage-sensitive features. The modal parameters are often sensitive to changing environmental conditions such as temperature, humidity, or excitation amplitude. Environmental conditions can have as large an effect on the modal parameters as significant structural damage, so these effects should be accounted for before applying damage identification methods. This paper presents results from a continuous monitoring system installed on the Dowling Hall Footbridge on the campus of Tufts University. Significant variability in the identified natural frequencies is observed; these changes in natural frequency are strongly correlated with temperature. Several nonlinear models are proposed to represent the relationship between the identified natural frequencies and measured temperatures. The final model is then validated using independent sets of measured data. Finally, confidence intervals are estimated for the identified natural frequencies as a function of temperature. The ratio of observed outliers to the expected rate of outliers based on the confidence level can be used as a damage detection index. [Copyright &y& Elsevier]

Abstract: This tutorial paper aims to introduce the topic of operational modal analysis to non-specialists on the subject. First of all, it is stressed the relevance of this experimental technique particularly in the assessment of important civil infrastructure. Then, after a synthesis of required theoretical background, three of the most powerful algorithms for output-only modal identification are presented. The several steps of these identification procedures are illustrated with the processing of data collected on a concrete arch bridge with a span of 280m. As the use of operational modal analysis in the context of structural health monitoring is a subject under active research, this theme is also introduced and briefly exemplified with data continuously recorded at the same bridge. [Copyright &y& Elsevier]

Abstract: Modal-based damage detection algorithms are well-known techniques for structural health assessment, but they are not commonly used due to the lack of automated modal identification and tracking procedures. Development of such procedures is not a trivial task since traditional modal identification requires extensive interaction from an expert user. Nevertheless, computational efforts have to be carefully considered. If fast on-line data processing is crucial for quickly varying in time systems (such as a rocket burning fuel), a number of vibration-based condition monitoring applications are performed at very different time scales, resulting in satisfactory time steps for on-line data analysis. Moreover, promising results in the field of automated modal identification have been recently achieved. In the present paper, a literature review on this topic is presented and recent developments concerning fully automated output-only modal identification procedures are described. Some case studies are also reported in order to validate the approach. They are characterized by different levels of complexity, in terms of mode coupling, dynamic interaction effects and level of vibration. Advantages and drawbacks of the proposed approach will be pointed out with reference to available experimental results. The final objective is the implementation of a fully automated system for vibration-based structural health monitoring of civil engineering structures and identification of adequate requirements about sensor number and layout, record duration and hardware characteristics able to ensure a reliable low-cost health assessment of constructions. Results of application of the proposed methodology to modal parameter estimation in operational conditions and during ground motions induced by the recent L’Aquila earthquake will be finally presented and discussed. [Copyright &y& Elsevier]

When we talk about “smart structures” we can think about different properties and capabilities which make a structure “intelligent” in a certain sense. Originally, the expression “smart” was used in the context that a structure can react and adapt to certain environmental conditions, such as change of shape, compensation of deformations, active vibration damping, etc. Over the last year, the expression “smart” has been extended to the field of structural health monitoring (SHM), where sensor networks, actuators and computational capabilities are used to enable a structure to perform a self-diagnosis with the goal that this structure can release early warnings about a critical health state, locate and classify damage or even to forecast the remaining life-time. This paper intends to give an overview and point out recent developments of vibration-based methods for SHM. All these methods have in common that a structural change due to a damage results in a more or less significant change of the dynamic behavior. For the diagnosis an inverse problem has to be solved. We discuss the use of modal information as well as the direct use of forced and ambient vibrations in the time and frequency domain. Examples from civil and aerospace engineering as well as off-shore wind energy plants show the applicability of these methods. [Copyright &y& Elsevier]

• The Modal Assurance Criterion (MAC) indicates the similarity between two mode shapes. • When estimated from data, its uncertainty helps to analyze if mode shapes are equal or not. • The distribution of the MAC is Gaussian when the theoretical mode shapes are different. • For estimates of equal mode shapes, it is related to a shifted chi2 distribution. • The resulting schemes are applied to monitor mode shapes changes of the S101 Bridge. The Modal Assurance Criterion (MAC) is a modal indicator designed to decide whether the mode shapes used in its computation are corresponding to the same mode. During structural monitoring, it can be applied to evaluate changes in the mode shapes. When the mode shapes are estimated from measurement data, the MAC inherits their statistical properties, thus is afflicted with statistical uncertainty. The evaluation of this uncertainty is particularly relevant when the MAC estimate is close to 1, where 1 indicates equal mode shapes. In structural monitoring, it can be used to assess changes in mode shapes after early damage. While the framework for uncertainty quantification of modal parameters is well-known and developed in the context of subspace-based system identification methods, uncertainty quantification for the MAC has not been developed yet. A particular challenge for its statistical characterization is its boundedness in the interval between 0 and 1. In this paper it is shown that this boundedness yields two different distributions of the MAC estimates. The MAC computed between estimates of different mode shapes is inside the interval (0 , 1) , and a Gaussian approximation of its distribution is obtained. When the MAC is computed between estimates of equal mode shapes, the resultant MAC estimate is close to 1, and the classical Gaussian approximation is inadequate. In this case it is shown that the MAC estimate is linked to a quadratic form of the mode shapes, whose distribution can be approximated by a scaled and shifted χ 2 distribution. For both cases, uncertainty bounds related to the MAC estimates are established. The proposed frameworks are validated by extensive Monte Carlo simulations and then applied to evaluate mode shape changes due to damage during monitoring of the S101 Bridge. [ABSTRACT FROM AUTHOR]

• Bayesian framework of applying data driven model to uncertain training data identified from operational modal analysis. • Efficient formulation for Bayesian operational modal analysis data with Gaussian Process model in practice. • Examples with synthetic, laboratory and field data. In structural health monitoring (SHM), 'data driven models' are often applied to investigate the relationship between the dynamic properties of a structure and environmental/operational conditions. Dynamic properties and environmental/operational conditions may not be directly measured but are rather inferred based on measured structural response data. Conventional data driven models assume training data as precise values without uncertainty, but this may not be justified when they are identified by operational modal analysis (OMA) where identification uncertainty can be significant. The associated confidence or precision may also vary depending on their identification uncertainties. This paper develops a Bayesian data driven model for modal properties identified from OMA. Identification uncertainty is incorporated fundamentally through the posterior distribution of modal properties of interest given the ambient vibration measurements. A Gaussian Process model is used for describing the potential unknown relationship between the modal properties and environmental/operational condition, which is subjected to OMA identification uncertainty. An efficient framework is developed to facilitate computation. The proposed method is validated by synthetic and laboratory data. Typhoon data from two tall buildings illustrates the field application of the proposed method. [ABSTRACT FROM AUTHOR]