1,720,996 research outputs found
Integrating flexibility-based curvature with quasi-static features induced by traffic loads for high-resolution damage localization in bridges
Full text linkCurvature is one of the most popular damage-sensitive features in vibration-based structural health monitoring applications, typically calculated from identified modal features. While the relevant strategic or historical importance of bridges may justify dense sensor networks, a limited budget is generally assigned to monitor “minor” viaducts, thus involving inexpensive devices or extremely sparse sensing solutions. Modal parameters can only be obtained at instrumented locations. Thereby, damage assessment methods based on identified features typically have a low spatial resolution, especially when using low-cost monitoring setups with a modest number of sensing devices. This paper proposes an original identification method for the curvature of bridges based on sparse acceleration measurements that can be collected using standard accelerometers. The raw acceleration signal is processed through a particular filter bank that extracts dynamic and quasi-static signal components. The first components are employed to identify modal parameters, from which sparse yet robust estimates of the structural curvature are retrieved. On the other hand, the quasi-static acceleration generated by the structural deflection induced by traffic load is used to identify the curvature influence lines of the bridge, which are fused with modal estimates using a Kalman filter. The state variable of the analyzed system, representing a dense curvature profile of the structure subjected to concentrated loads, can be used as a damage-sensitive feature for high-resolution damage localization. The method is applied to a steel truss bridge subject to different damage configurations
Damage Localization in a Steel Truss Bridge Using Influence Lines Identified from Vehicle-Induced Acceleration
Full text linkIn the last few decades, structural health monitoring (SHM) has proven a helpful tool to support the maintenance and management of civil infrastructure. However, typical measurement networks are expensive and require considerable initial efforts. The user-friendliness and interpretability of the outcome of SHM systems are crucial factors in motivating infrastructure owners and decision-makers to sustain their costs. For this reason, simple algorithms that provide structural parameters with direct physical interpretability for professionals familiar with the typical quantities involved in structural engineering are still the most used in field applications. This paper proposes an original method to identify curvature influence lines of bridges and viaducts only using the structural acceleration response induced by vehicular loads. Acceleration time histories collected at sparse locations through standard accelerometers are employed. In contrast to SHM approaches based on modal parameters, the proposed method does not need strict synchronization, thus being suitable for wireless and low-cost monitoring solutions. Identified influence lines are used to define a spatially dense damage indicator for accurate localization of structural anomalies with a clear physical meaning. Experimental results obtained for a steel truss bridge analyzed in different damage conditions prove the efficacy of the proposed method for situations where modal-based approaches may fail
Instantaneous modal identification under varying structural characteristics: A decentralized algorithm
Full text linkOne of the latest trends in structural health monitoring involves the use of wireless decentralized sensing systems, developed to reduce costs and speed up the whole monitoring process. The main purpose of this paper is to present a novel decentralized procedure for the instantaneous modal identification of time-varying structures, also suitable in the presence of environmental variations and non-stationary ambient excitation. In particular, a modal assurance criterion (MAC)-based clustered filter bank (CFB) is obtained, capable of decomposing structural responses into modal components for the evaluation of time-varying natural frequencies and modal shapes through a nonlinear energy operator. The proposed algorithm is relatively simple and usable with low-cost smart sensing systems, as it requires low computational effort and works with few data at a time. To prove the effectiveness of the presented method, a simulated near-real-time modal identification procedure has been performed on a full-scale bridge under progressive damage scenarios. The estimated modal parameters have then been used for damage diagnosis. The results reveal a good correspondence between identified modal parameters and reference values, showing also promising outcomes for both damage detection and localization
Seismic structural health monitoring using the modal assurance distribution
Full text linkThanks 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
Damage Detection in Nonlinear Elastic Structures Using Individual Sensors
Full text linkNatural frequencies have always been one of the most intuitive and widely used features for damage identification in civil structures. Even with the recent rapid technological and theoretical developments, frequency-based identification methods are of great interest for applications through low-cost sensing systems. Although most techniques for frequency identification assume a linear structural behavior, in real applications, variations in the amplitude of input excitation can lead to short-term frequency fluctuations due to the inherent nonlinearities of civil structures. This paper proposes a procedure for damage detection in nonlinear systems based on instantaneous resonant frequency and amplitude estimates. A statistical model was fitted to identified data, and a synthetic indicator was proposed to obtain robust damage detection, even when frequency shifts due to variations in the input excitation are comparable to those due to actual damage. The proposed method was applied to a dataset recorded from a reinforced concrete building with strongly nonlinear behavior
An approach to define the minimum detectable damage and the alarm thresholds in vibration-based SHM systems
Full text linkThis paper proposes an approach to defining the alarm thresholds for vibration-based structural health monitoring (SHM). The approach uses natural frequencies identified from the acceleration response of the monitored structure and is based on the concept of Minimum Detectable Damage (MDD), namely the smallest damage size in each structural element associated with a given probability of detection (POD) and probability of false alarm (PFA). The approach is demonstrated using natural frequencies computed from finite element models of the healthy and damaged structure, also accounting for temperature fluctuations and measurement noise. The approach first builds a baseline dataset of modal frequencies for a yearly thermal cycle on the healthy structure. Then, different damage conditions are simulated. For each sample of natural frequencies, a Damage Index (DI) is computed as the Mahalanobis distance between the considered sample and the baseline distribution. The alarm threshold is defined as the DI value for a given PFA. Based on the DIs obtained for the damaged structure, the POD is computed for the considered system threshold. This operation is repeated by increasing the damage entity. The MDD is thus defined as the level of damage associated with a desired value of POD. The proposed idea is tested on a steel truss bridge, where the MDD for each element is estimated by considering PFA=5% and POD=95%
The value of seismic structural health monitoring for post-earthquake building evacuation
Full text linkIn the aftermath of a seismic event, decision-makers have to decide quickly among alternative management actions with limited knowledge on the actual health condition of buildings. Each choice entails different direct and indirect consequences. For example, if a building sustains low damage in the mainshock but people are not evacuated, casualties may occur if aftershocks lead the structure to fail. On the other hand, the evacuation of a structurally sound building could lead to unnecessary financial losses due to business and occupancy interruption. A monitoring system can provide information about the condition of the building after an earthquake that can support the choice between several competing alternatives, targeting the minimization of consequences. This paper proposes a framework for quantifying the benefit of installing a permanent seismic structural health monitoring (S2HM) system to support building evacuation operations after a seismic event. Decision-makers can use this procedure to preventively evaluate the benefit of an SHM system and decide about the worthiness of its installation
Identification of bridge curvature profiles from dynamic responses induced by moving vehicles using autoregressive models
No full textThis paper introduces a novel methodology for structural damage localization in beam-like single- and multi-span bridge structures based on curvature profiles extracted from acceleration measurements. The approach builds on the rationale that autoregressive models, when trained on ambient vibration data, fail to reconstruct the quasi-static response induced by moving loads. This limitation produces a reconstruction residual that, under suitable conditions, corresponds to a shifted and scaled version of the curvature profile of the structure generated by a point load applied at the sensor location. The proposed method enables the calculation of this residual with extremely sparse sensor networks that do not require synchronization. A damage index is then defined from variations in the estimated curvature profile, enabling localization of stiffness reductions. To eliminate the need for manual parameter tuning, a model order selection criterion is proposed, which makes the method fully automated, unlike existing approaches that rely on prior knowledge of the monitored structure. The methodology is validated through numerical simulations that incorporate vehicle-bridge interaction phenomena and road roughness, as well as experimental data from a full-scale truss bridge. The results demonstrate that the proposed method achieves damage localization performance comparable to established filter-based techniques, while offering improved spatial resolution and requiring no tuning parameters
The role of finite element model updating in homogeneous transfer learning for damage classification in structural health monitoring
No full textThis paper investigates the potential of employing transfer learning (TL) for structural damage classification using synthetic data generated from un-updated finite element models (FEMs). While finite element model updating (FEMU) is commonly used to align FEM outputs with experimental data, it does not fully eliminate residual discrepancies between simulated and real measurements. These discrepancies can undermine the reliability of damage classification models trained on FEM-generated data, when used to classify experimental observations. Recent studies have addressed this issue by leveraging TL to align synthetic data from updated FEMs with real measurements. However, FEMU is time-consuming, prone to ill-conditioning, and its necessity in TL-based damage classification remains unclear. Motivated by these factors, this study investigates whether synthetic data from un-updated FEM can still enable effective damage classification through the Joint Domain Adaptation (JDA) method, a form of homogeneous TL where real and simulated domains share consistent labels and features. To this end, an investigation using both numerical and experimental case studies is conducted, along with a detailed discrepancy analysis to evaluate how domain mismatch affects classification performance. The results demonstrate that the TL-based approach achieves high classification accuracy and reliable damage characterization, even without an ad-hoc FEM calibration
Structural Management and Value of Information Analysis Accounting for Sensor Data Quality
No full textStructural health monitoring (SHM) can be used to assess the state of health of civil structures and infrastructures and acquire information that can support maintenance-related activities and post-disaster emergency management. Nevertheless, SHM outcomes may be susceptible to errors due to malfunctioning of the sensing system. The long-term benefit of SHM systems against the initial investment in sensing instrumentation is often quantified without considering the eventuality of faulty sensors. Inaccurate or missing sensor data, not accounted for when information from the SHM system is used to support decisions, can lead to the choice of sub-optimal maintenance actions, and associated economic losses. In the last two decades, Sensor Validation Tools (SVTs) have been proposed, which assess data quality before the SHM information is extracted to isolate and discard abnormal measurements. Nevertheless, automatic SVTs are still rarely implemented in real applications. Recently, a framework based on Bayesian decision theory has been proposed to quantify the benefit of using an SVT before it is implemented. The novel approach extends the traditional VoI to consider multiple モfunctioningヤ states of the SHM system with the final goal of quantifying the additional benefit obtained from SVTs. In this paper, this framework is demonstrated using a general example representative of different real situations. Uncertainties in the SVT results are accounted for to show that the adoption of an SVT enhances the overall benefit provided by an SHM system
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