This study investigates vibration-based structural health monitoring (SHM) of a small-scale wind turbine blade using multiple sensor types to evaluate the performance of an output-only, data-driven and semisupervised damage detection framework. The primary objective is to assess how different sensing modalities influence damage detection and localisation and to demonstrate the robustness of the proposed SHM approach across diverse sensor configurations in a wind turbine composite material blade. To this end, experimental accelerometer and strain-gauge time series are considered in this study. The method integrates multivariate autoregressive (MAR) modelling, principal component analysis (PCA), Mahalanobis distance-based dissimilarity metrics, and K-means clustering. This hybrid framework requires no labelled damage data and minimal user input, offering a transparent, interpretable and computationally efficient solution suitable for real-world applications. The blade was experimentally tested under progressive damage, which was introduced as sequential cracks at three locations, and under varying temperature conditions. Results show that the method reliably detects damage across sensor configurations, highlighting its potential as a practical and cost-effective tool for continuous SHM. Notably, both sensor types were able to detect damage using only one sensor as input in the model; however, strain gauges proved more effective than accelerometers for damage localisation.

Fatigue estimation for critical structures necessitates comprehensive monitoring, which, in turn, requires dense instrumentation and models that heavily rely on computational resource. However, the fatigue vulnerability of different segments within the infrastructure can be considered to adopt a cost-effective substructure-based monitoring approach, minimizing the need for extensive instrumentation or complex models. However, estimating or instrumenting the substructure boundary adds to the complexity. In addition, conventional fatigue estimation approaches often assume constant structural health, disregarding the unknown current health status of aging structures. To overcome this limitation, a novel substructure-based fatigue life estimation approach is developed, incorporating an interacting ensemble-particle filter that considers the current health status while remaining robust against boundary forces. Numerical experiments validate the proposed approach on a simulated reinforced concrete box girder bridge, utilizing a 3D beam model that accounts for dynamic interaction with vehicles. A parametric analysis investigates the relationships between fatigue damage and factors such as surface roughness, vehicle speed, vehicle weight, and vehicle category, aiming to identify dominant stimuli. The results demonstrate an accurate estimation of health parameters and remaining useful life (RUL). Furthermore, a novel decomposed approach for RUL estimation is developed, enabling the mapping of traffic information to fatigue damage without requiring costly simulations. A case study highlights the practical applicability of the approach, focusing on a reinforced concrete box girder bridge in Himachal Pradesh, India.

Plate-like structures form the essential components of diverse engineering systems, such as building floors, aircraft wings, wind turbine blades, etc. Detection of early onset of damage/s and prevention of sudden catastrophic failures during operation are necessary for maintaining and enhancing the safety standards of such high-dimensional and intricate systems. To detect anomalies in high resolution within the structure, the employment of a high-dimensional support model parameterized with damage attributes becomes imperative within a model-based assessment framework. Further, to support the estimation of consequent high-dimensional parameter sets, the traditional structural health monitoring methods require dense instrumentation over the entire structure for accurate damage location and severity assessment. Targeting an overall cost reduction in the implementation and subsequently circumventing the augmented cost of computation, this article employs a robust Interacting Particle Kalman filtering (IPEnKF)-based approach for health estimation of plate-like structures that is capable of monitoring only a manageable subdomain of interest while being independent of the rest of the domain. Eventually, this approach allows stage-wise health estimation of complex plate-like structures under the limitation of instrumentation. The proposed approach has been validated through both numerical simulations and real experiments on a scaled model of the plate roughly representing the NASA CRM wing, idealized as a trapezoidal Mindlin plate divided into independent substructures. The proposed method employs the IPEnKF algorithm to estimate each substructure independently by modifying the process model by injecting output towards achieving robustness against unknown substructure boundary forces. By using this subdomain estimation approach, the sensor requirement has been reduced by 60% while maintaining 95% accuracy in damage detection and assessment. Additionally, computational costs have been significantly reduced due to the algorithm's ability to independently monitor the system at its substructure level without depending on the rest of the structure. The method also exhibits excellent reliability, with minimal false alarms. The results emphasize the algorithm's robustness and the essential role of sensor placement in improving the accuracy of damage identification.

Beam definitions are used to simplify complex structures like bridges or skyscrapers. This study highlights the importance of monitoring beams as they are often the critical components and damage in them can result in changes in their dynamic characteristics. This study proposes a substructure-based approach for damage estimation in structural beams, that can later be extended for monitoring high-dimensional structures. The traditional approach of full structure monitoring mandates heavy instrumentation and computational costs. To avoid that, the existing subdomain estimation approaches need to deal with the quasi-static displacement at subdomain boundaries making the estimation a coupled problem. Instead, the proposed approach focuses on only monitoring a subdomain of interest, independently, making it computationally cheaper. The study employs an interacting filtering algorithm with Particle and Ensemble Kalman filters, circumventing the complexities of quasi-static displacement. The proposed approach has been tested numerically and the results are promising for further investigation and improvement.

Monitoring the health of large-scale infrastructure with the required precision necessitates high-dimensional support models and extensive instrumentation. However, in a model-based structural health monitoring (SHM) framework, the ability to localize damage is constrained by the discretization of the model. To enhance the resolution of discretization, dense instrumentation is essential to address the inherent challenges in solving the inverse problem. Yet, increasing dimensionality can introduce detection delays, a critical concern in SHM. Additionally, addressing uncertainties stemming from the model, measurements, or external factors is crucial for reliable real-world SHM. This study focuses on localized damages, such as cracks, where detection precision is paramount. To tackle these challenges, this study proposes an innovative hybrid interacting Particle-Kalman filter (h-IPKF)-based SHM approach that surpasses model discretization limitations for detection resolution. This approach employs two distinct indices for crack localization: global (GDI) and local (LDI) damage indices. GDI identifies the affected element using a coarsely discretized predictor model, while LDI precisely pinpoints the crack location within the element. This paper represents the crack as a massless rotational spring with deteriorated stiffness (γ) and parameterizes it using GDI, LDI, and γ. Subsequently, the h-IPKF framework is employed to estimate the crack parameters, capable of handling both continuous and discrete random variables, which is advantageous for addressing discrete variables like GDI. Extensive numerical and real validation tests on beam structures were conducted to assess the algorithm's sensitivity to noise and damage severity, demonstrating its efficiency, promptness, and precision in detecting cracks within structures.

The development of operational modal analysis (OMA) techniques has enabled the monitoring of large civil engineering structures, such as long-span bridges and high-rise buildings. However, implementing these techniques in real-world testing sites with limited resources remains a challenge. This study introduces a novel approach to obtain the modal characteristics of a box girder bridge in a more cost-effective and simplified manner, with experiments performed in a gap of more than 4 years (Years: 2018 and 2023). The study proposes two output-only frequency domain system identification techniques, namely roving reference normalized power spectrum and roving reference frequency domain decomposition, as part of the OMA-based approach. These techniques involve measuring responses from a pair of roving accelerometers. The collected vibration data records the concrete bridge's response to various external factors, including vehicular traffic on the carriageway, pedestrian movement, river flow, and wind. Despite weak environmental excitations and sensor noise, the findings suggest that accurate modal properties can still be extracted. The two proposed OMA approaches yielded five mode shapes and modal frequencies with closely matching results. When these experimental findings are compared with the numerical results, they exhibit a notable level of consistency. The paper identifies the dynamic characteristics of bridge structures from ambient vibration responses using only two accelerometers with two-point roving technique. This is especially important in real-world testing sites where data are inevitably noisy, unlike in laboratory environments. Additionally, it significantly reduces the cost of the bridge health monitoring.

Typically, for linear parameter varying systems, which can potentially get influenced by spatio-temporal external parameters, possible changes in their eigenstructure are not easy to be attributed conclusively to system faults or spatio-temporal parametric variations. Such spatio-temporal variations can although be estimated alongside, yet at the cost of making the estimable system dimension disproportionately large. Such augmented system dimension can thereby jeopardize tracking of the system evolution, either due to computational constraints or due to insufficient measurement channels (ill-posedness). This paper proposes a localized estimation approach wherein only a subdomain of the entire system is considered which reduces the dimension of the estimated model within manageable limits. To focus on the subdomain properties without knowledge of the rest of the model parameterization, a robust algorithm is developed through output injection using a simpler and sub-optimal version of Kitanidis filtering approach to induce robustness in the system parameter estimation against the boundary measurements. Finally, the subdomain model is estimated employing a marginalized filtering approach wherein a particle filter is employed for estimating both the eigenstructure and the controlling parameter while an ensemble Kalman filter estimates the states. The approach is demonstrated with the help of a mechanical system under spatial variation in temperature for which subdomain isolation necessitates the interface to be measured. In the context of the numerical application, the induced fault is due to damage, and the mechanical model is controlled and parameterized by the internal temperature, whose variations can be significant due to substantial external thermal variations inducing significant variations in the dynamic properties.

Structural Health Monitoring (SHM) enables assessing in-service structures’ performance by localizing structural anomaly instances immediately after their occurrence. Typical SHM approaches monitor the entire structural spatial domain aggravating the required density and cost of instrumentation. Further, with model-based approaches, the entire structural domain is needed to be defined with high dimensional, compute-intensive models rendering the SHM approaches ill-posed and slow especially when the instrumentation is limited and system observability is compromised. Moreover, in absence of high-fidelity models, oversimplification and subsequent model inaccuracies may lead to inaccurate estimation and possibly false alarms even if a subdomain is modeled inaccurately, e.g. support boundaries. To mitigate such issues, stand-alone monitoring focusing only on a subdomain of interest may be a computationally cheaper and prompt approach while being substantially robust to false alarms. Typically, such stand-alone substructure monitoring approaches demand extensive measurement of the interface, which can be a challenge in real-life applications. This paper presents a novel filtering-based online time domain approach for estimating substructure parameters without the need to measure or estimate the substructure interfaces. The proposed component-wise estimation is stand-alone so that the health estimation of the complete structural domain can be undertaken in parallel and later coupled through post-processing. The requirement of the interface measurement has been alleviated by employing an output injection approach. The proposal has been validated on a numerical beam structure subjected to arbitrary forces and subsequently, the sensitivity against noise and damage severity of the proposal has been investigated. Finally, the proposal is validated on a real beam to illustrate its real-life applicability and significance.

A standardized experiment for validating Structural Health Monitoring (SHM) methods is taken up. The test structure is a laboratory-scale five-storey steel frame designed with joints that can be easily detached or reattached as needed. The relatively heavier joints mimic the real-life rigid structural joints fabricated with extensive use of gusset plates and fasteners. The frame members are also proportionally chosen to allow sufficient flexibility as in typical real-life structural frames. The material properties, like elasticity and density are experimentally obtained and reported. The real structure has been tested under different levels of forces exerted through an impact hammer. Accordingly, the force and response histories are recorded and reported in this article. Further to complement the requirement of support models for typical model based SHM approaches, two support models are prepared that mimic the test setup. The first one is a high-fidelity Finite Element (FE) model prepared using commercial ABAQUS software and the second one is a simplified FE model prepared with MATLAB scripting language. While the first model emphasizes the details to be replicated with sufficient accuracy through the numerical model, the simplified model aims to reduce the computational burden that is typically induced through recursive simulation calls of such support models. Both the models are calibrated (/updated) using typical optimization protocols minimizing the departure between model and real experimental responses. Both time and frequency domain information has been used in this attempt. All details and data produced by the models and the experiments are disseminated in this article.

Typical civil infrastructures are prone to fatigue-induced failure due to repeated loading during their service life. To effectively manage the consequences of fatigue-induced failure, the remaining useful life (RUL) of a structure must be estimated on the basis of a certain established parameterized fatigue model. Eventually estimation of the pertinent fatigue model parameters becomes imperative which has traditionally been approached offline using a complete available database. This paper proposes an online model-based approach to predict (/estimate) the fatigue life drawing inference from only available structural health monitoring (SHM) data employing an extended Kalman filter (EKF). Keeping the real life uncertainties (loading, model inaccuracy, ambient variability etc.) into account, the study casts the problem in the probabilistic domain. Updated Paris model is employed in this attempt to simulate the fatigue crack growth propagation, and the model parameters are estimated using SHM data while taking the uncertainties into consideration. The proposed method employs two steps: first, to estimate the unknown model parameters using the available crack growth history and the second, to perform the prognosis of the crack based on the estimated model parameters. Numerical studies are conducted on two fracture scenarios: edge and center crack in a finite plate under mechanical and thermal loading conditions. Further, numerical simulations have been carried out to study RUL for a welded joint of a bridge based on its worst operational scenario. To further validate the proposed method, an experimental study is conducted on compact tension (CT) specimens. Estimation of Paris model parameters and fatigue crack prognosis with the proposed approach has been validated on these test sets. This method is observed to be consistently accurate in estimating the fatigue model parameters and subsequently predicting the RUL as well.

Sensor types and their positioning is a major factor in structural health monitoring (SHM) to ensure certainty in estimation. While acceleration has predominantly been employed for damage detection, they are known to be costly and not frame invariant (except for moderately accurate GPS based accelerometers). A thorough monitoring of a real life structure requires dense instrumentation which might become expensive with costly sensor types. Further, damages mostly occur at rare events, like seismic base excitation, for which typical accelerometers are not proper. This study employs strain as a cheaper alternative for damage sensitive measurement that is also frame invariant. An interacting filtering approach with particle and Kalman filters is employed that estimates structural health from measured dynamic strains. Further to account for extreme non-stationary events like seismic excitation, robustness against uncertain inputs is induced in the filtering environment following an output injection approach. The proposed algorithm is tested on a seven story-one bay frame model and a real experimental beam structure.

Majority of construction industries are aiming to go for taller and lighter buildings which may result in flexible and slender structures. Hence serviceability and safety become a critical issue during the occurrence of heavy winds and high magnitude earthquakes. Therefore, considerable techniques are adopted to minimize the vibrations caused by these natural responses of the structures. One of the techniques used prominently for tall structures is Tuned Mass Damper (TMD). TMD’s have been very effective in controlling structural vibrations. This study proposes a detailed analysis of a 2D frame structure with a TMD system placed at different levels of the structure in order to evaluate the behaviour of structure for given earthquake ground motions. The results obtained indicate installation of simple frames can decrease the response of the structure during an earthquake and location of TMD is also discussed in detail.

Two-dimensional reinforced concrete frames with the storey of different heights are simulated to the column removal scenario to understand the progressive collapse behaviour by using General Services Administration (GSA 2003) guidelines. Numerical modelling of 4-, 8- and 12-storey structures with different analyses procedures were evaluated such as Linear Static Analysis (LSA), Linear Dynamic Analysis (LDA) and Nonlinear Static Analysis (NLSA) using ETABS 2015. The liner results demonstrate the corner column removal and thus make the structure more vulnerable to progressive collapse when compared to the middle column removal and nonlinear results indicate that the progressive collapse of the structures decreases with increasing the height of the storey.