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Highlights: We used array-based ambient vibration modal analysis and eigenfrequency modeling to characterize the dynamic behavior and structural conditions of a partly collapsed lava tube roof. Multiple resonance modes were identified from spectral and cross-correlation analyses, and used to calibrate a heterogeneous numerical model of the lava tube. Our study shows that the combination of experimental and numerical modal analysis is a valuable tool for mapping and structural characterization of underground cavity roofs. [ABSTRACT FROM AUTHOR]

Scour is the prevailing cause of bridge failure worldwide, leading not only to traffic disruption, but also to social and economic losses and even to casualties. Many vibration-based monitoring techniques have been proposed for identifying the scour location and extent, based on the evaluation of the changes of the bridge modal properties due to scour. This study describes the experimental and numerical research carried out to investigate the effects of scour on the dynamic properties of structures with shallow foundations. Although these are the most vulnerable ones, they have received less attention compared to structures founded on pile foundations. To fill some existing knowledge gaps, field experiments were carried out on EuroProteas, a structural prototype with shallow foundation that was subjected to increasing levels of scour. The changes of the dynamic properties of the system are evaluated by postprocessing the ambient vibration recordings and by developing various models of the soil–foundation–structural system with different descriptions of the soil–structure interaction problem. The study results shed light on the effects of scour on systems with shallow foundations and on the accuracy of alternative modelling approaches. They are presented here to inform the development and validation of vibration-based techniques and modelling strategies for bridge scour identification. [ABSTRACT FROM AUTHOR]

Modal parameter evaluation is widely used for structural health monitoring, damage detection, and numerical model calibration. This study benchmarks two methods for modal identification under controlled laboratory conditions using operational modal analysis to compare dynamic parameters from ambient vibrations and shaker tests. A 2‐storey 3D spatial steel frame equipped with 3‐axial accelerometers is used as a case study. The identified dynamic parameters are benchmarked against a preliminary finite element model by computing Auto‐MAC and MAC matrices. It can be concluded that the preliminary FEM approach performs better in the prediction of modes shapes than natural frequencies. It was found that analysing the structural response to both ambient and artificially induced excitations provide a more complete understanding of a structure's dynamic characteristics. An additional mode shape was identified from the artificially induced excitation that was not previously captured from the ambient excitation. [ABSTRACT FROM AUTHOR]

Operational Modal Analysis (OMA) is becoming a mature and widespread technique for Structural Health Monitoring (SHM) of engineering structures. Nonetheless, while proved effective for global damage assessment, OMA-based techniques can hardly detect local damage with little effect upon the modal signatures of the system. In this context, recent research studies advocate for the use of wave propagation methods as complementary to OMA to achieve local damage identification capabilities. Specifically, promising results have been reported when applied to building-like structures, although the application of Seismic Interferometry to other structural typologies remains unexplored. In this light, this work proposes for the first time in the literature the use of ambient noise deconvolution interferometry (ANDI) to the structural assessment of long bridge structures. The proposed approach is exemplified with an application case study of a multi-span reinforced-concrete (RC) viaduct: the Chiaravalle viaduct in Marche Region, Italy. To this aim, ambient vibration tests were performed on February 4 th and 7 th 2020 to evaluate the lateral and longitudinal dynamic behaviour of the viaduct. The recorded ambient accelerations are exploited to identify the modal features and wave propagation properties of the viaduct by OMA and ANDI, respectively. Additionally, a numerical model of the bridge is constructed to interpret the experimentally identified waveforms, and used to illustrate the potentials of ANDI for the identification of local damage in the piers of the bridge. The presented results evidence that ANDI may offer features that are quite sensitive to damage in the bridge substructure, which are often hardly identifiable by OMA. [ABSTRACT FROM AUTHOR]

This paper presents results from the dynamic monitoring of a reinforced concrete school building located in Camerino (central Italy) during the seismic swarm following the first main shock of the Central Italy earthquake that occurred in August 2016. After the main shock of August 24th, ambient vibration tests were executed on the building to identify its modal dynamic behavior, which was assumed as benchmark to study changes in the structural response because of the subsequent events. A three-day dynamic monitoring was then performed with the aim of investigating changes in the dynamic behavior of the building subjected to ambient and seismic excitations. A procedure to identify the nonlinear response of the structure subjected to seismic events is presented, starting from an optimization methodology that permits the identification of the structure dynamics within time windows in which the building dynamic behavior can be considered linear time-invariant. Data show the variability of the modal properties, resonance frequencies, and damping ratios of the building, with the earthquake intensity. [ABSTRACT FROM AUTHOR]

The rise of human activities and the constant increase of vehicles traffic in the cities, and in particular of heavy vehicles such as buses and trucks, causes continues ambient vibrations that can detrimentally affect the conservation of built artifacts, especially historical constructions. The effects of urban vibrations on buildings and monuments are far from being fully investigated. In general, peak levels of traffic-induced vibrations are rarely high enough to be the main cause of heavy structural damages. On the other hand, they contribute to the process of deterioration by adding extra stresses—through fatigue damage accumulation occurring as a result of long periods of exposure to low levels of vibration—or by contributing to soil densification—which can lead to the settlement of building foundations. As such, the estimation of threshold levels above which traffic-induced vibrations may cause damages to monumental buildings requires specific studies including long-time monitoring campaigns. Data from experimental campaign can, indeed, be used to extract information on the variation of dynamic properties of buildings, thus providing valuable pieces of information for a complete knowledge to plan effective preservation interventions. The present work aims to identify the effects of traffic induced urban vibrations on the two Towers of Bologna, particularly prone to fatigue-related damages and close to a high transit of vehicles, through a preliminary analysis of data from several dynamic monitoring campaigns conducted during the last years.

This article presents the most significant structural response parameters of a railway system analyzed with data gathered during 5 years. The structure is the instrumented curved portion of an elevated railroad that consists of a simply supported beam resting on cantilever columns on soft soil. A monitoring system was implemented to determine, within a few minutes, a preliminary structural state analyzing ambient vibration or seismic events. To assess the state of health of the structure, the response obtained from the seismic events, applying the proposed methodology, is compared with that obtained from controlled field tests conducted at the end of its construction and compared with design values. The monitoring system's results showed minimal frequency variations caused by external works and a transitory variation of the fundamental frequencies during the 19 September 2017 great earthquake that, however, did not affect the structural integrity. The warning system showed acceptable performance, and only suggested checking the support beam-column condition. [ABSTRACT FROM AUTHOR]

Operational modal analysis (OMA) methods are frequently applied for obtaining information on structural dynamic parameters, such as natural frequencies and mode shapes. While synchronization between acceleration response data sets used in OMA is not a prerequisite for estimating natural frequencies, synchronization discrepancies may lead to non-negligible errors in the estimation of mode shapes. Synchronization discrepancies are predominantly associated with wireless structural health monitoring systems, where each wireless sensor node functions as a separate data acquisition unit (DAQ), and clock synchronization is therefore not intrinsic. However, synchronization discrepancies may also occur in cable-based systems, particularly when more than one DAQ is used. In this paper, a synchronization method for detecting and correcting synchronization-induced errors in OMA is proposed. Unlike existing approaches on removing synchronization-induced errors at the output stage, the method proposed herein yields synchronized structural response data at an intermediate stage of OMA. Specifically, time lags between acceleration response data sets are detected based on estimates of the mode shapes obtained either from preliminary structural analysis or from engineering judgment assuming classical damping. The applicability of the proposed method is verified through simulations of a multi-degree-of-freedom oscillator and validated through ambient vibration field tests on a pedestrian overpass bridge. [ABSTRACT FROM AUTHOR]

The preservation of concrete dams is a key issue for researchers and practitioners in dam engineering because of the important role played by these infrastructures in the sustainability of our society. Since most of existing concrete dams were designed without considering their dynamic behaviour, monitoring their structural health is fundamental in achieving proper safety levels. Structural Health Monitoring systems based on ambient vibrations are thus crucial. However, the high computational burden related to numerical models and the numerous uncertainties affecting the results have so far prevented structural health monitoring systems for concrete dams from being developed. This study presents a framework for the dynamic structural health monitoring of concrete gravity dams in the Bayesian setting. The proposed approach has a relatively low computational burden, and detects damage and reduces uncertainties in predicting the structural behaviour of dams, thus improving the reliability of the structural health monitoring system itself. The application of the proposed procedure to an Italian concrete gravity dam demonstrates its feasibility in real cases. [ABSTRACT FROM AUTHOR]

The procedure for detecting the location and severity of damage of complex structural systems using their modal properties is an important tool of Structural Health Monitoring (SHM) of civil infrastructure. The herein presented research proposes procedures for damage detection based on two heuristic optimization methods: Simulated Annealing (SA) and Tabu Search (TS). In order to test the proposed procedures in different frequency ranges, experimental and numerical analyses were conducted on a steel grid bridge model in two configurations, according to the total mass of the structure, as well as for two simulated damage cases. The calibration of model parameters, according to experimentally extracted modal properties, is carried out using the proposed procedures. Numerical computations were conducted using ANSYS package and developed routines under MATLAB environment for model calibration and damage detection procedures. Experimental modal properties were extracted from ambient vibration measurements, as state-of- the art in SHM of complex structures, by the Frequency Domain Decomposition (FDD) technique, using ARTeMIS software. Both of the proposed procedures for model calibration and damage detection, with adopted objective functions including frequency and mode shape differences, exhibit accuracy, efficiency and robustness.

Ambient Vibration-Based Structural Health Monitoring (AVB–SHM) studies on prone-to-fall rock compartments have recently succeeded in detecting both pre-failure damaging processes and reinforcement provided by bolting. The current AVB–SHM instrumentation layout is yet generally an overkill, creating cost and power issues and sometimes requiring advanced signal processing techniques. In this article, we paved the way toward an innovative edge-computing approach tested on ambient vibration records made during the bolting of a ~760 m3 limestone rock column (Vercors, France). First, we established some guidelines for prone-to-fall rock column AVB–SHM by comparing several basic, computing-efficient, seismic parameters (i.e., Fast Fourier Transform, Horizontal to Vertical and Horizontal to Horizontal Spectral Ratios). All three parameters performed well in revealing the unstable compartment’s fundamental resonance frequency. HHSR appeared as the most consistent spectral estimator, succeeding in revealing both the fundamental and higher modes. Only the fundamental mode should be trustfully monitored with HVSR since higher peaks may be artifacts. Then, the first application of a novelty detection algorithm on an unstable rock column AVB–SHM case study showed the following: the feasibility of automatic removing the adverse thermomechanical fluctuations in column’s dynamic parameters based on machine learning, as well as the systematic detection of clear, permanent change in column’s dynamic behavior after grout injection and hardening around the bolts (i1 and i2). This implementation represents a significant workload reduction, compared to physical-based algorithms or numerical twin modeling, and shows better robustness with regard to instrumentation gaps. We believe that edge-computing monitoring systems combining basic seismic signal processing techniques and automatic detection algorithms could help facilitate AVB–SHM of remote natural structures such as prone-to-fall rock compartments.

Damage localization based on ambient vibration data in combination with finite element models can be challenging, in particular due to the large number of parameters in the model and noisy measurement data. Changes in different structural parameters can cause similar changes in data-driven features, and vice versa, it can be challenging to identify which parameter caused the deviation in the data. The problem is ill-conditioned and slight variations in the features, due to inherent statistical uncertainty, can lead to significant errors in the result interpretation. A possible solution is sensitivity-based statistical tests in combination with a parameter clustering approach that considers the uncertainties of data-driven features. In this context, this paper introduces the concept of damage localizability , and provides a framework to evaluate it based on the minimum detectable parameter changes, possible false alarms in unchanged parameters, as well as the achievable damage localization resolution. Since clustering approaches depend on user-defined hyperparameters, such as the number of clusters, the second objective of this paper is to optimize the performance of the damage localization, by adjusting the hyperparameters for clustering. A particular strength of the approach is that the analysis can be conducted based on data and a numerical model from the undamaged structure alone, making it a suitable approach to assess and to optimize the diagnosis performance before damage occurs. For proof of concept, a laboratory case study on a simply-supported steel beam is presented, where the localizability of mass changes is analyzed and optimized. • An approach is developed to assess and optimize the performance of damage localization tests. • Clustering of sensitivity vectors avoids ill-conditioning. • Localizability is defined by the three criteria: resolution, detectability, and false alarms. • All criteria can be quantified based on data from undamaged structures, and a numerical model. • Experimental data confirm the predictive capabilities of the performance assessment. [ABSTRACT FROM AUTHOR]

Dynamic measurements of two steel truss bridges for Structural Health Monitoring (SHM) were performed. The investigated bridges were: a single supported one span truss bridge and a multi-span truss bridge. Two possibilities of measuring setup were considered. A simple arrangement made up of one polygon has been used for data acquisition of the single span truss bridge. A more complex measuring setup made up of two polygons with up to 32 channels has been used for the large bridge with an entire length reaching up to 500 m. A connection of both measuring polygons was achieved by Wi-Fi antennas. The reason why the multi-span truss bridge was chosen for SHM is that the construction is nowadays overloaded. The dynamic measurements were done, ambient and semi-ambient data were analysed. After that, some mode-shapes were identified from the acquired data and then the data were compared with numerical calculations. Finally, experiences and conclusions from the ambient data measurement are summarized. [ABSTRACT FROM AUTHOR]

This paper proposes a relative displacement sensor developed to measure directly the relative slip between slab and girder in composite bridges for assessing the health condition of shear connections. The structure, design principle, features, and calibration of the developed relative displacement sensor are presented. The design of the sensor ensures that there are no voltage outputs for the tension, compression, bending, and torsion effects, but only for the relative displacement between the two connecting pads of the sensor. The accuracy of the developed sensor in measuring the relative displacement response and using it for monitoring the conditions of shear connectors was tested on a composite bridge model in the laboratory. Shear connection condition was monitored under ambient vibrations, then static load tests were conducted to introduce cracks into the composite bridge. Both the vertical deflections and relative displacements were used for the crack detection. Experimental studies demonstrate that the developed sensor is very sensitive to the relative displacement and has a decent performance for the structural health monitoring of composite bridges. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Damage diagnosis based on global structural vibrations critically depends on the sensor layout, in particular when a small number of sensors is used for large structures under unknown excitation. This paper proposes a sensor placement strategy that yields an optimized sensor layout with maximum damage detectability in selected structural components. The optimization criterion is based on the Fisher information, which quantifies the information that the damage-sensitive feature carries on the design parameters of structural components, such as material constants or cross-sectional values. It is evaluated using a finite element model, and considers the statistical uncertainties of the damage-sensitive feature. The methodology is shown for the stochastic subspace-based damage detection method, but can be applied to any damage-sensitive feature whose distribution can be approximated as Gaussian. It is suitable to find the optimal layout for a fixed number of sensors and to choose an appropriate number of sensors. Since the Fisher information is defined component-wise, the sensor layout can be tuned to become more sensitive to damage in local structural components, such as damage hotspots, non-inspectable components, or components that are critical for the safety and serviceability of the structure. For proof of concept, the sensor layout on a laboratory beam is optimized based on numerical simulations, and it is showcased that the optimal sensor layout leads to the highest damage detectability for experimental data. • Gaussian vibration-based features are employed for statistical damage detection. • The sensor layout is optimized to maximize the feature's sensitivity and minimize its uncertainty. • The optimization criterion allows one to tune the sensor layout for high detectability in critical components. • A custom-made genetic algorithm efficiently finds a close-to-optimal solution for large structures. • The optimization is based on a numerical model and validated in a lab experiment. [ABSTRACT FROM AUTHOR]

With the increasing application of long-term dynamic monitoring systems to civil engineering structures, automated operational modal analysis (OMA) has become a fascinating research area for structural health monitoring. In recent years, different automated approaches based on the existing parametric or nonparametric OMA algorithms have been developed. However, they usually perform less well in large complex structures with weak excitation because of inherent limitations of those algorithms. In this work, a three-stage automated OMA algorithm based on a combination of the second-order blind identification (SOBI) and the covariance-driven stochastic subspace identification (SSI-COV) is proposed, which takes full advantage of both parametric and nonparametric algorithms while overcoming the limitations. The automated approach avoids the ambiguous work related to interpreting stabilization diagrams without losing accuracy and thus obtains more reliable modal parameters. The proposed method is fully validated by simulated data from an 8-DOF system and data recorded on a concrete arch-gravity dam in view of single and continuous monitoring. The results show that all physical modes can be well-separated and reliably identified by the proposed method with high accuracy, confirming the excellent performance of the method, especially for continuous monitoring. [ABSTRACT FROM AUTHOR]

• The minimum detectable change in structural parameters is determined for damage detection. • The analysis requires vibration data from the reference state and a finite element model. • Detectability is defined through a reliability concept for monitoring using statistical tests. • Detectable damages explicitly depend on damage sensitivity, noise, data length and reliability. • The predicted damage detectability is confirmed on experimental data of a beam with added masses. This paper derives a formula to determine the minimum detectable damage based on ambient vibration data. It is a key element to analyze which damage scenarios can be detected before a monitoring system is installed. For the analysis, vibration data from the reference structure as well as a finite element model are required. Minimum detectability is defined by adopting a code-based reliability concept that considers the probability of detection and the probability of false alarms. The results demonstrate that the minimum detectable damage depends on three elements: the uncertainty of the damage-sensitive feature (which decreases with increasing measurement duration), its sensitivity towards model-based design parameters, and the reliability requirements regarding the damage diagnosis results. The theory is developed for the stochastic subspace-based damage detection method but can be applied to any damage-sensitive feature provided its sensitivities and statistical properties can be characterized. For proof of concept, the minimum detectable change in stiffness and mass of a pin-supported beam are analyzed in a numerical and experimental study, respectively. The predictions with the developed approach appear to be accurate and robust to noise effects for both simulated and real data. [ABSTRACT FROM AUTHOR]

Ambient Vibration-Based Structural Health Monitoring (AVB–SHM) studies on prone-to-fall rock compartments have recently succeeded in detecting both pre-failure damaging processes and reinforcement provided by bolting. The current AVB–SHM instrumentation layout is yet generally an overkill, creating cost and power issues and sometimes requiring advanced signal processing techniques. In this article, we paved the way toward an innovative edge-computing approach tested on ambient vibration records made during the bolting of a ~760 m3 limestone rock column (Vercors, France). First, we established some guidelines for prone-to-fall rock column AVB–SHM by comparing several basic, computing-efficient, seismic parameters (i.e., Fast Fourier Transform, Horizontal to Vertical and Horizontal to Horizontal Spectral Ratios). All three parameters performed well in revealing the unstable compartment's fundamental resonance frequency. HHSR appeared as the most consistent spectral estimator, succeeding in revealing both the fundamental and higher modes. Only the fundamental mode should be trustfully monitored with HVSR since higher peaks may be artifacts. Then, the first application of a novelty detection algorithm on an unstable rock column AVB–SHM case study showed the following: the feasibility of automatic removing the adverse thermomechanical fluctuations in column's dynamic parameters based on machine learning, as well as the systematic detection of clear, permanent change in column's dynamic behavior after grout injection and hardening around the bolts (i1 and i2). This implementation represents a significant workload reduction, compared to physical-based algorithms or numerical twin modeling, and shows better robustness with regard to instrumentation gaps. We believe that edge-computing monitoring systems combining basic seismic signal processing techniques and automatic detection algorithms could help facilitate AVB–SHM of remote natural structures such as prone-to-fall rock compartments. [ABSTRACT FROM AUTHOR]