Seismic Structural Health Monitoring Research Articles

Fast Bayesian modal identification with known seismic excitations

AbstractFast and accurate identification of structural modal parameters after an earthquake is crucial for assessing structural conditions and facilitating repair. With the development of modern earthquake observation techniques, the recorded ground motion can be leveraged as extra input information for modal identification, enabling the experimental modal analysis applicable. This study develops a Bayesian modal identification algorithm that aims at estimating the most probable value (MPV) of modal parameters and their identification uncertainty. Incorporating the recorded seismic input, the algorithm utilizes with the structural equation of motion in the frequency domain to formulate the likelihood function and adopts a constrained Laplace method for Bayesian posterior approximation of modal parameters. With the aid of complex matrix calculus, an iterative scheme is developed, allowing a fast search of the MPV of modal parameters and an analytical evaluation of the posterior covariance matrix. The performance of the proposed algorithm is validated by examples with synthetic, laboratory and field data, respectively. In addition, its effectiveness on predicting structural responses under a future earthquake is illustrated, showing its potential for various downstream applications in seismic structural health monitoring.

Improved Blob-Based Feature Detection and Refined Matching Algorithms for Seismic Structural Health Monitoring of Bridges Using a Vision-Based Sensor System

Improved Blob-Based Feature Detection and Refined Matching Algorithms for Seismic Structural Health Monitoring of Bridges Using a Vision-Based Sensor System

Seismic structural health monitoring of RC framed building using artificial neural network model: a study

Seismic structural health monitoring of RC framed building using artificial neural network model: a study

Lower bound of structural damage to achieve practical identifiability of nonlinear models in seismic structural health monitoring

AbstractThis paper examines the effect of structural damage on the practical/computational identifiability of the parameters that define nonlinear models of building structures subjected to earthquake‐induced base motions. The objective is to determine the level of physical damage expected to successfully estimate the nonlinear parameters of restoring force models. For this purpose, the analyses aim to determine if the parameters that define a hysteretic (Bouc‐Wen type) model can be identified within a predefined level of accuracy from accelerations measured during seismic events that cause minor damage. The identified model is then interrogated to determine if it can provide accurate predictions of the response and damage level experienced during strong ground motions that cause moderate‐to‐severe damage. The damage model adopted is a Park–Ang type model and the unscented Kalman filter is used for parameter estimation. The results are verified using simulated two‐dimensional building models and validated using experimental data from a large‐scale shake table test.

The Role of Seismic Structural Health Monitoring (S2HM) in the Assessment of the Delay Time after Earthquakes

The concept of seismic resilience has been introduced in the design of buildings in the last decade. In this regard, the delay time may be defined as the time that occurs between the event and the moment the repair process begins. In the literature, only a few contributions have considered delay time, and even its definition is still under discussion. However, it is a key parameter in the assessment of resilience after earthquakes since it may significantly increase the total time after which a structure may be considered recovered. The principle at the base of the paper is that seismic structural health monitoring (S2HM) may play a significant role in reducing the delay time. Therefore, delay time needs to be considered since it may significantly reduce the seismic resilience of structural systems. The paper aims to consider this important issue demonstrating the relationship between S2HM and the assessment of the seismic resilience of buildings. In particular, the assumption herein is that the accuracy of the S2HM may be described with different levels, and in correspondence with these levels, certain values of the delay time may be considered. In addition, the delay time is considered as a percentage of the total repair time. A multidimensional definition that includes the accuracy of S2HM in the description of the delay time is herein proposed to be included in methodologies that aim to assess seismic resilience.

A Comparative Study of Damage-Sensitive Features for Rapid Data-Driven Seismic Structural Health Monitoring

Rapid post-earthquake damage assessment forms a critical element of resilience, ensuring a prompt and functional recovery of the built environment. Monitoring-based approaches have the potential to significantly improve upon current visual inspection-based condition assessment that is slow and potentially subjective. The large variety of sensing solutions that has become available at affordable cost in recent years allows the engineering community to envision permanent-monitoring applications even in conventional low-to-mid-rise buildings. When combined with adequate structural health monitoring (SHM) techniques, sensor data recorded during earthquakes have the potential to provide automated near-real-time identification of earthquake damage. Near-real time building assessment relies on the tracking of damage-sensitive features (DSFs) that can be directly and rapidly derived from dynamic monitoring data and scaled with damage. We here offer a comprehensive review of such damage-sensitive features in an effort to formally assess the capacity of such data-driven indicators to detect, localize and quantify the presence of nonlinearity in seismic-induced structural response. We employ both a parametric analysis on a simulated model and real data from shake-table tests to investigate the strengths and limitations of purely data-driven approaches, which typically involve a comparison against a healthy reference state. We present an array of damage-sensitive features which are found to be robust with respect to noise, to reliably detect and scale with nonlinearity, and to carry potential to localize the occurrence of nonlinear behavior in conventional structures undergoing earthquakes.

A Bayesian-based data fusion methodology and its application for seismic structural health monitoring of the Consoli Palace in Gubbio, Italy

Recent earthquakes have demonstrated that monumental structures located in regions characterized by high seismic hazard are particularly sensitive to damage, stimulating a growing attention to the formulation of cost-effective and long-lasting methods for damage assessment. Generally, the evaluation of a healthy or damaged state is data-driven and it can be subjected to a large amount of uncertainty. In order to associate a damage symptom to an actual structural damage, including all the uncertainties involved in the process, a Bayesian-based data fusion methodology is proposed. To this purpose, different sources of information are combined, such as dynamic structural properties extracted from monitoring data (natural frequencies and mode shapes), static response data (crack amplitudes) and visual inspections. More in depth, the proposed procedure comprises three fundamental steps: i) calibration of a finite element (FE) model, partitioned in well-thought-out macro-elements on the basis of engineering judgments and/or numerical simulations and, subsequently, construction of a tuned surrogate model (SM) considering pre-selected uncertain parameters as inputs, such as the Young's modulus, shear modulus, Poisson's ratio and mass density associated to each macro-element; ii) solve the Bayesian-based inverse problem aimed at deriving the posterior statistics of the uncertain parameters over the space of the surrogate model's classes in a computational effective manner by using dynamic data; iii) adjust the posterior distribution on the basis of the information obtained from static data and visual inspections, i.e., data fusion. The suitability of the proposed approach is demonstrated by using the monitoring data pertaining to a monumental palace, located in Gubbio (Italy) and named Consoli Palace, which has been monitored by the Authors since 2017.

Monitoreo de la salud estructural de un edificio residencial de muros de corte de hormigón armado en Lima, Perú, utilizando un sensor 4d raspberry shake

Lima, the capital of Peru, has not suffered from a great earthquake since 1746, making this a prone Mw 8.8 earthquake area according to seismological studies. In this context, seismic structural health monitoring presents an opportunity to assess rapidly buildings after a great earthquake and protect inhabitant from those buildings with high risk of collapse due to aftershocks. A basic seismic structural health monitoring arrangement of a fifteenth-floor residential building is presented using a low-cost sensor called Raspberry Shake 4D. The implemented sensor incorporates both a vertical velocity geophone and three orthogonally positioned microelectromechanical systems accelerometers. The building is a reinforced concrete shear walls structure located in Comas, north of Lima. More than thirty seismic events have been recorded, including the Mw 8.0 Lagunas earthquake (05/26/2019) and the Mw 5.8 (22/06/2021) with epicentral distances of 709 km and 94 km, respectively. Acceleration as high as 150 cm/s2 and as low as 2 cm/s2 have been recorded. It was possible to obtain fundamental frequencies of vibration in longitudinal and transversal directions of the building from the response calculated using the wavelet transform that have a good agreement with results of microtremor measurements performed at different floors. The computed scalograms for seismic records showed important values of energy amplitudes for the fundamental frequencies of the building. Finally, theoretical fundamental frequencies were obtained from a 3D finite element model and an elastic analysis.

Preface for the special issue on Seismic Structural Health Monitoring for the Bulletin of Earthquake Engineering

Preface for the special issue on Seismic Structural Health Monitoring for the Bulletin of Earthquake Engineering

Seismic structural health monitoring to prevent unnecessary economic loss from non-damaging earthquakes in European and Middle Eastern cities

Seismic structural health monitoring to prevent unnecessary economic loss from non-damaging earthquakes in European and Middle Eastern cities

The value of seismic structural health monitoring for post-earthquake building evacuation

In 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.

A methodological framework to relate the earthquake-induced frequency reduction to structural damage in masonry buildings

The diffusion of seismic structural health monitoring systems, evaluating the dynamic response of engineering structures to earthquakes, is growing significantly among strategic buildings. The increasing availability of valuable vibration data is being backed by continuously evolving techniques for analysing and assessing structural health and damage. Within this framework, the paper proposes a novel model-driven vibration-based methodology to support the assessment of the damage level in masonry buildings hit by earthquakes. The leading idea is to exploit, in the pre-event phase, synthetic equivalent-frame modelling and nonlinear dynamic analyses to systematically relate the gradual reduction of natural frequencies to increasing levels of structural damage. The resulting behavioural chart (seismic chart) of the building, constructed by employing computational tools and robustly defined on a statistical base, may provide the theoretical expectation to ascertain a certain level of seismic damage, based on the decrease in vibration frequency experimentally identified in the post-event phase. The methodology is firstly formalized, integrating common identification techniques with a novel damage grade estimation procedure, and finally exemplified for a monitored strategic masonry building damaged by the 2016–2017 Central Italy earthquake sequence. The outcomes of this application confirm the operational validity of the methodology, which can be intended as effective support for the decision-making process regarding structural usability and safety in the post-earthquake scenario.

XShake : Intelligent wireless system for cost-effective real-time seismic monitoring of civil infrastructure

Seismic structural health monitoring (SHM) of structures is critical not only to detect earthquakes to send early warning, but also to enable rapid structural condition assessment to ensure safety. Traditional monitoring systems using wired sensors are expensive. Wireless sensors offer tremendous opportunity to reduce costs, which remains elusive for seismic structural monitoring due to two main obstacles. First, there are constraints on power resources. Most wireless sensors are dutycycled to preserve limited battery power; and hence, can miss an earthquake in power-saving sleep mode. Second, there is a lack of support for rapid post-event data collection and processing. Conventional data transmission after sensing can introduce significant delays, and real-time data acquisition that eliminates these delays has limited throughput. In this paper, an intelligent wireless monitoring system, xShake, is developed for cost-effective real-time seismic SHM. It consists of: 1) energy-efficient wireless sensor prototypes utilizing on-demand sensing technique, 2) live-streaming framework that supports high-throughput real-time data acquisition, and 3) a rapid condition assessment application, enabling real-time data visualization and processing for end users. The performance of the xShake is validated through lab tests, demonstrating that it can capture high-fidelity synchronized data under earthquakes and enable real-time structural condition assessment.

Structural change detection applying long-term seismic interferometry by deconvolution method to a modern civil engineering structure (New Zealand)

Pulse-wave propagation velocity and resonance frequency measured in civil engineering structures are both related to structural design. Monitoring their variation following seismic strong shaking provides information about the immediate building capacity. Joint-interpretation of frequency and velocity variation requires a better understanding of the processes controlling seismic structural health. In this study, we analysed 8 years of earthquake data recorded by the vertical array installed in the Te Puni building in Wellington, New Zealand, as part of the GeoNet building instrumentation programme. Co-seismic variations of pulse wave velocity and fundamental frequency are analysed and interpreted through a Timoshenko beam-like building model. This study shows that even though no structural damage was visually reported over the considered time of monitoring, co- and post-seismic variation of both parameters’ values are observed for almost all earthquakes, including a permanent shift following strong ground shaking. Variations of pulse-wave velocity and resonance frequency are cross-interpreted in terms of the building model. They reflect a time variant building response, correlated with the seismic loading. In addition, time delay of the pulse-wave velocity as a function of the building height provides relevant information on the location of the changes and confirms the efficient cross-interpretation of both methods for seismic Structural Health monitoring.

Seismic structural health monitoring using the modal assurance distribution

AbstractThanks 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.

Human-machine collaboration framework for structural health monitoring and resiliency

Post-earthquake damage assessment can be significantly expedited when machine learning (ML) algorithms supported by sensing technologies are used. ML has some limitations when it comes to seismic damage assessment. ML tools require data from each class while training but in the case of seismic structural health monitoring, vibration data are usually available from the undamaged structures. In this paper, a framework called the human-machine collaboration (H-MC) is proposed. The H-MC framework combines the ML tools and human (domain) expertise for damage assessment of real instrumented buildings with only data from undamaged cases. It uses novelty detection as the ML tool and structure-specific analytical model to enable rapid damage detection. Subsequently, the framework is applied to detect damage in real instrumented buildings. The results showed that the H-MC algorithm correctly detected the damaged cases. It also labeled all the undamaged events accurately eliminating false positive detection. Furthermore, it is revealed that the resiliency of a building can be improved when the H-MC method is implemented over traditional field inspection. The proposed framework can be a viable tool for rapid post-earthquake damage assessment which is essential for improving community resiliency.

Life-cycle management cost analysis of transportation bridges equipped with seismic structural health monitoring systems

Life-cycle cost analysis is an approach that has gained popularity for assisting the design of civil infrastructures. The life-cycle cost analysis approach can be leveraged for structures equipped with structural health monitoring systems in order to quantify the benefits of the technology and de facto support its long-term implementation. However, for new structures, the long-term assessment of the expected value of the total investment cost, in terms of the current worth at the design time, is still the focus of ongoing research due to unknowns and uncertainties on the impact of the structural health monitoring system on long-term structural performance. This article proposes a new combined model of life-cycle cost formulation and simulation methodology for the long-term financial assessment of transportation bridges equipped with seismic structural health monitoring systems, in order to evaluate the total costs and benefits offered by such monitoring systems for post-seismic assessments. The formulation characterizes the time evolution of bridge management cost terms, highlighting the most sensitive parameters. The simulation methodology allows to quantitatively weigh each maintenance action on the total cost based on when the action is performed. The model is used to compare structures managed by the traditional approach of post-earthquake inspection versus those managed by a condition-based approach enabled by structural health monitoring systems. The originality of the model empowers the comparison by payback time, defined as the break-even point between costs and benefits of a structural health monitoring system, as well as by economic gain, defined as the difference between the total costs of an unmonitored versus a monitored structure through the end of service life. The proposed model is demonstrated through parametric analyses on a case study consisting of a continuous steel-concrete composite bridge, where the structural health monitoring system is used to monitor the elastic limit state condition of bending forces in piers during the earthquake.

Bayesian Calibration of Hysteretic Parameters with Consideration of the Model Discrepancy for Use in Seismic Structural Health Monitoring

Bayesian model calibration techniques are commonly employed in the characterization of nonlinear dynamic systems, as they provide a conceptual and effective framework to deal with model uncertainties, experimental errors and procedure assumptions. This understanding has resulted in the need to introduce a model discrepancy term to account for the differences between model-based predictions and real observations. Indeed, the goal of this work is to investigate model-driven seismic structural health monitoring procedures based on a Bayesian uncertainty quantification framework, and thus make relevant considerations for its use in the seismic structural health monitoring, focusing on masonry structures. Specifically, the Bayesian inference has been applied to the calibration of nonlinear hysteretic systems to both provide: (i) most probable values (MPV) of the parameters following the calibration; and (ii) estimates of the model discrepancy posterior distribution. The effect of the model discrepancy in the calibration is first illustrated recurring to a single degree of freedom using a Bouc–Wen type oscillator as a numerical benchmark. The model discrepancy is then introduced for calibrating a reference nonlinear Bouc–Wen model derived from real data acquired on a monitored masonry building. The main novelty of this study is the application of the framework of uncertainty quantification on models representing data measured directly on masonry structures during seismic events.

Performance-based post-earthquake decision making for instrumented buildings

This paper develops a decision making framework for post-earthquake assessment of instrumented buildings in a manner consistent with performance-based design criteria. This framework is achieved by simultaneously combining and advancing existing knowledge from seismic structural health monitoring and performance-based earthquake engineering paradigms. The framework consists of (1) measurement, (2) uncertainty modeling, (3) dynamic response reconstruction, (4) damage estimation, and (5) performance-based assessment and decision making. In particular, the main objective is to reconstruct inter-story drifts with a probabilistic measure of exceeding performance-based acceptance limits and determine the post-earthquake re-occupancy classification of the instrumented building of interest. Since the proposed framework is probabilistic, the outcome can be used to obtain the probability of losses based on the defined decision variables and be integrated into a risk-based decision making process by city officials, building owners, and emergency managers. The framework is illustrated using data from the Van Nuys hotel testbed, a seven-story reinforced concrete building instrumented by the California Strong Motion Instrumentation Program (CSMIP Station 24386).

Damage detection of structures using signal processing and artificial neural networks

This article presents a signal-based seismic structural health monitoring technique for damage detection and evaluating damage severity of a multi-story frame subjected to an earthquake event. As a case study, this article is focused on IASC–ASCE benchmark problem to provide the possibility for side-by-side comparison. First, three signal processing techniques including empirical mode decomposition, Hilbert vibration decomposition, and local mean decomposition, categorized as instantaneous time–frequency methods, have been compared to find a method with the best resolution in extracting frequency responses. Time-varying single degree of freedom and multiple degree of freedom models are used since real vibration signals are nonstationary and nonlinear in nature. Based on the results, empirical mode decomposition has proved to outperform than the others. Second, empirical mode decomposition is used to extract the acceleration response of the sensors. Next, a two-stage artificial neural network is used to classify damage patterns. The first artificial neural network identifies location and severity of damage and the second one calculates the severity of damage for the entire structure. IASC–ASCE benchmark problem is used to validate the proposed procedure. By taking advantage of signal processing and artificial intelligence techniques, damage detection of structures was successfully carried out in three levels including damage occurrence, damage severity, and the location of damage.