•Novel Structural Health Monitoring (SHM) method.•Deep learning methods trained by numerically generated responses.•Computational FE model updating.•Binary and multiclass Damage Identification ...problems.•Potential tool for Damage Identification tasks.
Identifying damage through Structural Health Monitoring (SHM) methods is increasingly attracting attention due to multiple maintenance and failure prevention applications. In order to create reliable SHM systems for structural damage identification (DI) tasks, access to large amounts of data containing measured structural responses is usually necessary. The data acquisition is mostly based on direct experimental responses up to now and requires time consuming measurements in various working and ambient conditions of the structure. In the present work, a novel SHM method is tested where all data is solely derived from FE calculated responses, after an initial experimental cost for FE model updating on the healthy structure state. The proposed method can be especially applied in cases where specific damage types are expected or anomalies are adequately defined so they can be effectively simulated by FE models. Origin of such models may then be the healthy experimental status. To test the proposed SHM system, the optimal FE model of an experimental benchmark linear beam structure is constructed, simulating an undamaged condition. In order to check the robustness of the proposed method the damage magnitudes imposed on the benchmark are kept small and combined with random excitations. Next, the optimal FE model is used for generating labeled SHM vibration data through a repetitive load case scheme which also includes uncertainties simulation. The data derived from the optimal FE model is finally used to train a Deep Learning (DL) Convolutional Neural Network (CNN) classifier which is after experimentally validated on the benchmark structure. The optimal FE generated data proves to be able to train an accurate CNN that can predict adequately the experimental benchmark states. A comparison is also given with a CNN trained by the corresponding nominal FE model data which is found not reliable on the experimental validations. The presented combination of optimal FE and DL is a potential solution for future SHM tools and further investigation is encouraged.
This paper investigates the dynamic characteristics and Finite element model updating of two confined masonry buildings in Messina, constructed in the aftermath of the devastating 1908 earthquake. ...The study addresses the need for advanced research in this field to enhance the understanding of the dynamic behaviour of confined masonry structures. The authors identified the modal parameters of the buildings from ambient vibration tests. Finite element models have been developed and fine-tuned in a second step to optimize the agreement between the simulated and observed modal parameters. The optimized parameters are then compared with the outcomes of nondestructive tests on masonry and reinforced concrete. This research addresses the modelling issues when dealing with confined masonry structures, offering guidance to engineers to select the modelling parameters. The paper emphasizes the substantial stiffening effect introduced by confined masonry, as evidenced by the optimized Young’s modulus of masonry, which is almost two and a half times higher than values obtained from flat jack tests. To accurately represent the interaction between reinforced concrete ties and masonry panels within equivalent frame models, it becomes crucial to adequately overstate the masonry stiffness to capture the mutual coupling between structural components.
•Dynamic characterization of confined masonry buildings.•Intelligent Automated Operational Modal Analysis approach.•Predictive capabilities of simplified FE modelling for confined masonry.•Genetic optimization algorithm to fine-tune modelling parameters.•Insight to select model parameters for confined masonry.
Model updating improves the correlation between the response of the real structure and the response of the finite-element (FE) model; however, the selection of the updating parameters ...(parametrization) is crucial for its success. Using full-field modal shapes, a large number of parameters can be updated, e.g., the Young’s moduli of all the finite elements; however, the structural response is not necessarily sensitive to an arbitrary parameter, making the optimization problem ill-conditioned. Additionally, the computation of the full sensitivity matrix is not feasible for relatively large FE models. Not all locations are equally important for model updating; at locations of the highest mechanical loads, more focus is required. In this research, the updating parameters are based on the curvature of the 3D full-field experimental shape, where locations with high curvature are associated with high sensitivity. The assumption is initially researched with the Euler–Bernoulli beam elements and second-order tetrahedrons. The proposed method is investigated on numerical and real experiments, where successful updating was confirmed. With the proposed parametrization and updating approach, a geometrically complex structure is parametrized and the parameters updated without significant user input, generalizing the model-updating procedure.
•3D full-field modal shapes are identified using the frequency-domain triangulation.•The numerical model is parametrized based on each measured full-field modal shape curvature.•Interior Point Method (IPM) is used to update the numerical model.•Anomaly on the structure is successfully identified on the numerical and real experiments.
Over the last few decades, model updating has become popular in structural dynamics, as it can be used to calibrate (finite element) models, with applications in assessing whether damage has occurred ...in a structural health monitoring context. Early approaches focused on determining the “best” fitting model in a deterministic manner. For example, mathematical optimisation was employed to minimise the discrepancy between measured and simulated modal parameters. More up-to-date approaches take uncertainties, e.g., due to measurement errors or model discrepancy, into account. In this context, Bayesian model updating has become increasingly popular. Recently, “likelihood-free” approaches have been proposed as an alternative to (exact) Bayesian model updating, with Bayesian history matching (BHM) being a promising “likelihood-free” technique. However, since BHM is based on an approximation of the simulation model using a Gaussian process regression (GPR), it can become inaccurate for highly non-linear and especially for (quasi-)discontinuous problems. Therefore, in this work, a new non-implausibility-motivated optimisation (NIMO) approach is proposed, which overcomes the non-linear space problem. The method is a combination of global optimisation and GPR. Global optimisation is used to accurately determine a non-implausible region in the design space, even for discontinuous problems. Subsequently, a GPR is fitted within the non-implausible region to efficiently approximate a posterior distribution. First, the NIMO approach is verified using test functions. Second, a validation is conducted by localising damage on a laboratory beam structure. It is demonstrated that the NIMO approach yields more robust results compared to BHM, while its computing times are manageable and – depending on the objective function – even smaller compared to BHM.
•A model based decision tree algorithm was developed to predict damage.•The FE model was calibrated based on ambient vibration test data.•Surrogate models were employed to simulate environmental ...conditions.•The implementation in a case study has been optimized using a genetic algorithm.•The methodology has shown robustness to be extrapolated to other case studies.
This paper develops a methodology for damage identification in steel truss bridges that uses vibration-based monitoring data and a model-based decision tree algorithm. The methodology resorts to a calibrated FE model with an optimization-based parameter identification procedure to simulate and analyze all the potential damages that might affect the structure. The effect of environmental conditions on the modal parameters is also accounted for, which is modeled as structure stiffness variations using the Young’s modulus and forecasted using a surrogate modeling strategy. The feasibility of the methodology is demonstrated on a full-scale bridge in Vilagarcía de Arousa, Spain. The underlying hypotheses used in the algorithm implementation were validated, and the error ponderation and selection bound employed to detect and identify damage were optimized. The results show an average success rate of 95.0% and an average false positive rate of 1.0% in identifying damage indicating its robustness to be extrapolated to other case studies.
•A damage assessment strategy suitable to historic towers instrumented with few accelerometers.•Integration between automated OMA and continuous updating of baseline FE model.•Damage detection based ...on novelty analysis of the frequency residual errors.•Damage localisation based on updating the parameters of FE model.
The paper presents a damage assessment strategy suitable to historic masonry towers. The methodology is exemplified using the data collected in the continuous dynamic monitoring of the San Vittore bell-tower (Arcisate, Northern Italy). The proposed damage assessment procedure aims not only at detecting the occurrence of structural anomalies, but also at localising the damage in the investigated structure. After a brief description of the tower and past diagnostic survey (including ambient vibration tests and Finite Element modelling), the results of the continuous dynamic monitoring are highlighted and the effect of temperature on automatically identified resonant frequencies is discussed. Subsequently, regression models based on Principal Component Analysis are applied in order to filter out the fluctuations caused by the environmental effects on the identified resonant frequencies. The damage detection and damage localisation issues are then addressed by using novelty analysis tools. The effectiveness of the proposed strategy is demonstrated through the detection and localisation of realistic damage scenarios simulated with the baseline Finite Element model. Specifically, the damage localisation has been tackled by using the “cleaned” modal properties within a continuous Finite Element model updating scheme.
•Proposed a corrosion-related parameter estimation methodology for RC structures using nonlinear FEMU.•Used case studies to investigate the efficacy of the proposed methodology in different ...scenarios.•Demonstrated the promise in estimating corroded properties, particularly for bonding.
Model updating based on field measurements (e.g., from ambient vibration) has proven to be an efficient approach for assessing the condition of reinforced concrete (RC) structures. Despite existing studies in the literature, no effort has been devoted to corrosion-related parameter estimation for RC structures based on Bayesian nonlinear finite element model updating (FEMU) with seismic data. To bridge this gap, this research work extends the application of nonlinear FEMU to corroded RC structures and provides a methodology to estimate the corrosion-related parameters from seismic measurement of RC structures with high nonlinearity. The essential idea of this approach is to integrate recursive Bayesian inference (i.e., unscented Kalman filter) with advanced finite element (FE) modeling of corroded RC structures to estimate corrosion-affected properties. The advanced FE modeling approach used in this study is an efficient modeling strategy that accounts for different aspects of the corrosion, particularly corroded bond-slip in RC structures. The capability of the approach in evaluating the corrosion-affected model parameters is examined by 13 case studies using an example RC column with simulated seismic data due to lack of real-word measurements. These study cases consider different corrosion levels, measurement noise levels, corroded features, seismic intensity levels, and types of measurement. The successful estimation of the corrosion-affected properties for the corroded column under various scenarios indicates that the proposed methodology can be employed to identify the unknown corroded properties of RC structures using recorded seismic response data. The updated FE model of a corroded RC structure can potentially be utilized for reliable seismic performance assessment, which assists in future decision-making for repair or retrofit of existing structures.
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•Natural frequency tracking is newly applied for SHM of a stiff masonry Palace.•An effective tracking of natural frequencies and crack amplitudes is achieved.•Frequency-temperature ...correlations are negative in sign due to a major crack pattern.•Crack amplitudes as predictors enhance statistical modeling of natural frequencies.•Remarkable freezing effects on natural frequencies are highlighted.
In recent years, the development of long-term structural health monitoring systems for preventive conservation of historic monumental buildings is receiving a growing trend of scientific interest. Nevertheless, the damage detection effectiveness of these systems is still debated, especially in respect to complex masonry palaces where both local and global failure mechanisms can be activated, whereby the majority of the documented successful applications are limited to masonry towers. In particular, one major issue that needs to be solved in order to derive damage sensitive features is associated to the removal of the effects of changes in environmental conditions and, primarily, of ambient temperature, from static and dynamic signatures. This paper aims to contribute to improving knowledge in this field, by investigating temperature effects on static and dynamic response of an iconic Italian monumental palace: the Consoli Palace in Gubbio. With the purpose of early detecting earthquake-induced damages, as well as damages caused by material degradation associated to awkward environmental conditions, a simple low-cost mixed static and dynamic long-term structural health monitoring system has been installed on the Palace by the authors in July 2017. After discussing surveys, ambient vibration tests, diagnostic investigations, numerical modeling and model calibration of the Palace, the analysis of the first year of monitoring data is presented. This analysis shows that, differently from what observed in other literature works on historic masonry towers, the natural frequencies of the Palace show a marked and sometimes non-linear decreasing trend with increasing ambient temperature, that can be effectively removed through linear statistical filtering provided that dynamic regression models, using past values of predictors, are used. On the other side, the evolution of the amplitudes of two major cracks monitored within the building also shows a marked linear decreasing trend with increasing ambient temperature. These results are meaningful towards the use of monitoring data for assessing the initial health conditions of a structure, as well as in a damage detection perspective.
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•Hierarchical deep learning.•Deep Learning SHM classifiers exclusively trained by FE generated data.•Multiclass damage identification problems.•CFRP pin-joined truss ...structure.•Weakened bolted connections used as damage scenarios.
Structural Health Monitoring (SHM) is an emerging field of engineering with a wide range of applications. The most common SHM strategies operate on structural responses through vibration measurements and focus on training mathematical classifiers which are used after to identify damage in unknown responses. Classifiers may additionally locate damage when adequate labeled damaged data is available. In the present work, a novel SHM method is presented where labeled damaged data is generated through FE models for a pin-joint composite truss structure employing a model-based approach for the problem of data acquisition. The truss is made of carbon fiber reinforced polymer (CFRP) members joint on aluminum connections forming a complex and large FE problem. A Deep Learning (DL) Convolutional Neural Network (CNN) classifier is trained on the FE generated vibration data combined with a hierarchical multiple damage identification and location scheme. The numerically trained CNN is after validated on experimental statuses of the truss in both damage detection and location, proving to be robust and accurate for the considered test case. The potential of hierarchical CNNs with FE based SHM data for multiple damages is investigated in this work and a comparison is given between hierarchical and direct multiclass CNNs. The large performance gains of the former are proven for the studied experimental case highlighting also the importance of SHM system architectures with CNNs.
•Recursive Bayesian updating of mechanics-based nonlinear FE models is performed.•A novel approach to account for modeling uncertainty is proposed.•A realistic 2D building subjected to seismic ...excitation is studied.•Different types and levels of modeling uncertainty are investigated.•The robustness of the proposed approach is demonstrated.
This paper proposes a novel approach to deal with modeling uncertainty when updating mechanics-based nonlinear finite element (FE) models. In this framework, a dual adaptive filtering approach is adopted, where the Unscented Kalman filter (UKF) is used to estimate the unknown parameters of the nonlinear FE model and a linear Kalman filter (KF) is employed to estimate the diagonal terms of the covariance matrix of the simulation error vector based on a covariance-matching technique. Numerically simulated response data of a two-dimensional three-story three-bay steel frame structure with eight unknown material model parameters subjected to unidirectional horizontal seismic excitation is used to illustrate and validate the proposed methodology. Geometry, inertia properties, gravity loads, and damping properties are considered as sources of modeling uncertainty and different levels and combinations of them are analyzed. The results of the validation studies show that the proposed approach significantly outperforms the parameter-only estimation approach widely investigated and used in the literature. Thus, a more robust and comprehensive identification of structural damage is achieved when using the proposed approach. A different input motion is then considered to verify the prediction capabilities of the proposed methodology by using the FE model updated by the parameter estimation results obtained.
