Damage State Research Articles

Predictive Probability of Localization Curves (P-POL) for Structures with Changing Environmental Conditions

One of the fundamental challenges in structural health monitoring (SHM) is the lack of data from the damaged state, which is required to verify the automated damage detection algorithms. In this paper, a recently developed approach is presented that allows one to assess the detectability of damages before they occur. The approach is based on so-called probability of detection curves that can be evaluated based on data and a model from the undamaged structure, in a “predictive” way. The method is based on four fundamental assumptions: the damage-sensitive features can be approximated through a normal distribution, the variance in the measurement remains constant for different damage scenarios, an analytical model of the examined structure is available for the sensitivity computation, and the relationship between measurements and structural changes can be linearized for small structural changes. In previous publications, this approach has already been applied to natural frequency and mode shape monitoring as well as ultrasonic testing. In this paper, it is applied to strain and inclination measurements from numerical case studies for the first time, demonstrating the universality of the method. Moreover, it is analyzed how changes in the environmental and operational variables (EOVs) affect the predicted POD. The results demonstrate that the predicted POD are valid even in the presence of environmental changes, but they depend on the user-defined hyperparameters of the algorithms that remove the environmental effects from the measurements. Therefore, the hyperparameter selection is critically discussed and an optimal monitoring strategy is outlined.

Numerical benchmark for road bridge damage detection from passing vehicles responses applied to four data-driven methods

Drive-by bridge monitoring utilizes measured responses from passing vehicles to perform damage detection of bridge, a methodology challenged by multiple factors and operational conditions. Recently, data-driven methods have been used to improve the accuracy of drive-by monitoring. This thriving research field requires (but lacks) publicly available datasets to improve and validate its monitoring and damage detection capabilities. To foster data-driven drive-by bridge damage assessment methods, this document presents an openly available dataset consisting of numerically simulated vehicle responses crossing a range of bridge spans with various damage conditions. The dataset includes results for different monitoring scenarios, road profile conditions, vehicle models, vehicle mechanical properties and speeds. The intention is to provide a useful resource to the research community that serves as a reference set of results for testing and benchmarking new developments in the field. In addition, four recently published data-driven drive-by methods have been tested using the same dataset.

Constraints of local stiffness adaption for reduced-order models of large-scale steel bridges

Large and complex structures, such as bridges, are subjected to a variety of environmental factors and loads over time, leading to evolving structural states. To assess their current condition and plan predictive maintenance, Digital Twins enriched with Structural Health Monitoring (SHM) data are essential. However, for large-scale structures, the creation of physics-based Digital Twins necessitates reduced-order modeling to ensure efficient adaptation to SHM data. Many of these methods for structural damage assessment rely on localized stiffness reduction to characterize discrete damage states. The Static Condensation Reduced Basis Element (SCRBE) method is a promising approach for model order reduction by decomposing the structure into parametric components. However, SCRBE's effectiveness and the accuracy of the Digital Twin depend on an appropriate partitioning of the structure into to components with reduced stiffness. This paper systematically investigates the process and the constraints of component selection for steel bridges with discrete damages, considering factors like numerics, sensor placement and complex geometries. A comprehensive analysis is conducted using a real bridge case study augmented with synthetic measurement data representing damage scenarios. The results reveal both challenges and the potential of the SCRBE method for Digital Twins. A systematic approach for component selection within the SCRBE framework is proposed, specifically tailored for real-world application scenarios. This structured methodology facilitates the efficient and adaptive development of Digital Twins for large, intricate structures, such as steel bridges.

Performance-Based Seismic Design for Retrofitting Deficient Bridge Bents: Developing Performance-Based Damage States

Performance-Based Seismic Design for Retrofitting Deficient Bridge Bents: Developing Performance-Based Damage States

Experimental Study on the Damage of Bridge Concrete in a Fire Environment Based on the Impact Echo Method

Abstract To accurately assess the damage degree of bridge structures in a fire environment, fire damage test studies were conducted on the C30 and C50 concrete specimens at different temperatures (room temperature, 100°C∼700°C) based on the impact echo method. In addition, the variation rules of mass, impact echo signal, dynamic modulus of elasticity Ed, and static and dynamic modulus of elasticity ratios (Ec/Ed) with overfire temperature were obtained. The results show the following: (1) at ablation temperature ≤ 300°C, the main frequency of the impact echo signal is single, the peak value is obvious, the signal attenuation is fast, and the change is uniform; at ablation temperature > 300°C, the main frequency is frequent, there is more than one peak, and the vibration duration of the phenomenon is longer. (2) After fire damage, the specimen’s dynamic modulus of elasticity Ed is in a cubic relationship with the overfire temperature. (3) In engineering practice, the static and dynamic modulus of elasticity (Ec/Ed) ratio is classically based on Lydon and Balendran’s formula, which is usually used for derivation at room temperature. This article found that the formula can be extended to the rough estimation at lower temperatures (≤200°C), and the model proposed in this article can be used for the derivation of the static modulus of elasticity Ec and the dynamic modulus of elasticity Ed at high temperatures. In summary, the impact echo method can be used to quantitatively assess the damage state of bridges after fire. After the signal time-frequency domain conversion, a qualitative assessment of the degree of concrete damage can be made. Furthermore, a quantitative assessment of bridge concrete after fire damage can be made using the model proposed in this article.

Multi-Objective Model Updating of Tower Structures Featuring Closely Spaced Modes

In this contribution, we present a multi-objective model updating approach for the purpose of damage localisation and quantification on a girder mast structure based on closely spaced modes. In structural health monitoring (SHM), modal quantities, such as eigenfrequencies and mode shapes, serve as monitoring parameters. Specifically, model updating strategies adjust the local structural stiffness of a simulated structure to reduce the difference between the simulation results and the experimentally identified modal quantities. The resulting structural stiffness represents the damaged state of the structure, thus providing valuable information about the occurred damage. Symmetrical tower structures are challenging for model updating approaches due to their closely spaced bending modes, which are prone to uncertain alignments between the identified mode shapes and the model mode shapes. As a result, the naive comparison between the mode shapes of the real structure and the model often yields poor results. In order to overcome this problem, we adapt the concept of the subspace of order 2 modal assurance criterion (S2MAC), i.e., finding the best fit to a mode shape vector in a subspace spanned by two bending modes, hence enabling a meaningful comparison of the changes in the dominant mode shapes. The proposed method is validated using an experimental 9 m tall girder mast structure featuring reversible damage mechanisms. Mode shapes and eigenfrequencies, identified using the Bayesian operational modal analysis (BayOMA) method, are employed for multi-objective model updating, allowing for the estimation of several Pareto-optimal solutions. The structure is parameterised with two damage parameters, accounting for the location and severity, which limits the optimisation to only one damaged beam at any given time. The damage localisation is performed for data sets recorded over a time span of several months, including three different damage positions, resulting in a successful validation of the proposed

Drive-by bridge damage detection by vehicle-bridge interaction neural operator

Drive-by bridge damage detection using vibrations of vehicles for damage detection of bridges has been studied since it needs no instrumentation on bridges. However, it is still a challenge to be able to extract features that are sensitive to bridge damage and relevant to the responses of the bridge. To address the challenges of drive-by bridge damage detection, machine learning-based approaches have also been actively investigated in recent research. With supervised learning methods, it is almost impossible to obtain training data on the damage state of a bridge. On the other hand, unsupervised learning approaches have been considered, but they only provide changes in relative features rather than physical properties that are useful for estimating the structural performance. To address the above issues, this study aims to propose and investigate the feasibility of drive-by bridge damage detection using a vehicle-bridge interaction neural operator (VINO). In the proposed method, numerical simulations of vehicle-bridge interactions considering the damage field of the bridge are first carried out to generate physically informed training data. The VINO is then constructed using the physically informed training data. However, VINOs are still insufficient to model real bridges. Therefore, in order to improve the practicality of the physically-informed VINO, data-informed fine-tuning is carried out using available data from real bridges. It is noted that no damage data is required in the fine-tuning stage. The feasibility of the drive-by bridge damage detection using VINO is discussed using an in-house experiment on a model bridge under a moving vehicle.

Damage-informed cost functions for improving data-driven fatigue prediction

Fatigue is among the most common causes for the failure of engineering structures. As a result, there is widespread interest in the prediction of fatigue damage using data-driven methods. However, previous work by the authors has shown that when using conventional cost functions for optimising models for virtual strain sensing, these will frequently fail to reliably predict the fatigue damage state of a structure. This is particularly true when viewing the problem from a probabilistic standpoint, as many cost functions consider only the posterior mean prediction. This work will look at how knowledge of the S-N fatigue damage methodology can be integrated into the training stage of data-driven strain prediction models. This is achieved by developing cost functions that prioritise predictive capability with respect to fatigue damage. Multiple criteria are considered for the cost functions including: the total damage, the distribution of stress ranges after performing cycle counting, the frequency content of predictions and the number of turning points in the data. As this work will consider the problem within a probabilistic framework, the additional challenges this poses shall also be considered. The proposed methodology will be demonstrated in this paper using Gaussian process regression. However, it is also appropriate for use within other data-driven methods.

CD66b+/CD68+ circulating extracellular vesicles, lactate dehydrogenase and neutrophil-to-lymphocyte ratio can differentiate coronavirus disease 2019 severity during and after infection.

Coronavirus disease 2019 (COVID-19) has been a major public health burden. We hypothesised that circulating extracellular vesicles (cEVs), key players in health and disease, could trace the cell changes during COVID-19 infection and recovery. Therefore, we studied the temporal trend of cEV and inflammatory marker levels in plasma samples of COVID-19 patients that were collected within 24h of patient admission (baseline, n=80) and after hospital discharge at day-90 post-admission (n=59). Inflammatory markers were measured by standard biochemical methods. cEVs were quantitatively and phenotypically characterized by high-sensitivity nano flow cytometry. In patients recovered from COVID-19 lower levels of inflammatory markers were detected. cEVs from vascular (endothelial cells) and blood (platelets, distinct immune subsets) cells were significantly reduced at day-90 compared to admission levels, a pattern also observed for cEVs from progenitor, perivascular and epithelial cells. The best discriminatory power for COVID-19 severity was found for inflammatory markers lactate dehydrogenase and neutrophil-to-lymphocyte ratio and for granulocyte/macrophage-released CD66b+/CD68+-cEVs. Albeit inflammatory markers were good indicators of systemic inflammatory response and discriminators of COVID-19 remission, they do not completely reveal cell stress and organ damage states. cEVs reaching baseline pre-infection levels at 90 days post-infection in recovered patients discriminate parental cells affected by disease.

A new damage identification approach of HFSGCW based on LDV and wavelet packet band energy method

This study introduces a damage assessment system and a pivotal detection technique for hidden frame-supported glass curtain wall (HFSGCW) structural sealant. The vibration performance of HFSGCW is evaluated using a laser Doppler vibrometer (LDV). The acquired signals undergo wavelet packet transformation for decomposition. The damage conditions of the structural sealant are analyzed by calculating the sum of squared differences (SSD) of the wavelet packet bands' energy. This SSD indicator serves as the damage indicator. The study investigates the effect of structural sealant damage on the HFSGCW's SSD indicator. Meanwhile, the LDV test results are corroborated with those from numerical simulation analysis to evaluate LDV's viability for detecting the SSD indicator in HFSGCW. The results indicated a strong correlation between the SSD indicator and the numerical modeling analysis. When the structural sealant was damaged, the SSD indicator showed sudden fluctuations, coupled with specific positional indications corresponding to the degree of damage, the type of damage (penetration or non-penetration), and the damage location. This indicator provides a basis for evaluating structural integrity. The research's effectiveness in guiding the construction and assessment of facades is demonstrated by its practical application in the project context.

Basic Experimental Study on the Application of Natural Frequency to Transfer Learning

Integrating artificial intelligence (AI) into structural health monitoring systems significantly improves the structural integrity and safety of buildings. This integration necessitates extensive data on various structural damage scenarios; however, acquiring comprehensive damage-state data for real-world buildings is difficult, resulting in data scarcity and imbalances between intact- and damage-state datasets. Transfer learning offers a compelling alternative that enables the utilization of damage-state data acquired from scaled experimental models for real-world building applications. This study explores the use of natural frequency ratios before and after damage to facilitate transfer learning. This study established an equation that describes the relationship between the natural frequency ratio before and after damage, accounting for scaling impacts. The natural frequency ratios for both the prototype and scaled models were experimentally determined and compared, focusing on bolted joints in the steel structure. The results of the experiment were consistent with those of the finite-element analysis. The experimentally obtained natural frequency ratios of the prototype and scaled models under identical damage conditions exhibited high congruence. The experimental and FEA results demonstrated analogous patterns of decreasing natural frequency ratio with increasing damage severity. These results indicate that natural frequency ratio data from various damage conditions in scaled models could mitigate data scarcity issues and train AI models for real-world building applications.

Penghijauan Lingkungan melalui Program Penanaman Pohon: Gerakan Bersama Masyarakat Desa Cirumpak

Environmental problems do not only occur in urban areas, but in rural areas environmental problems also often occur. Greening is an alternative effort to prevent environmental problems. The aim of reforestation activities in service is a form of land restoration to improve damaged environmental conditions and support the local ecosystem of Cirumpak Village. The methods used in this activity include (1) socializing the importance of reforestation, (2) Handing over tree seedlings to the people of Cirumpak Village who are assisted by the Village Secretary and chairman of KKM group 75 at Bina Bangsa University. The handover of tree seedlings was carried out at 03.00 WIB, (3) technical assistance for planting. Planting tree seedlings is divided into several places, namely, the area around the Village office, the river bank area and the area of ​​residents' homes. Based on observations, when greening was carried out, this activity received positive appreciation from the sub-district, RT, RW, and the Cirumpak Village community because this greening was an effort to prevent environmental problems in Cirumpak Village.

Stiffness-Based Evaluation of Hinge Joints in Prefabricated Assembled Multi-Girder Bridges under Operational Conditions.

Presently, the prevailing approaches to assessing hinge joint damage predominantly rely on predefined damage indicators or updating finite element models (FEMs). However, these methods possess certain limitations. The damage indicator method requires high-quality monitoring data and demonstrates variable sensitivities of distinct indicators to damage. On the other hand, the FEM approach mandates a convoluted FEM update procedure. Hinge joint damage represents a major kind of defect in prefabricated assembled multi-girder bridges (AMGBs). Therefore, effective damage detection methods are imperative to identify the damage state of hinge joints. To this end, a stiffness-based method for the performance evaluation of hinge joints of AMGBs is proposed in this paper. The proposed method estimates hinge joint stiffness by solving the characteristic equations of the multi-beam system. In addition, this study introduces a method for determining baseline joint stiffness using design data and FEM. Subsequently, a comprehensive evaluation framework for hinge joints is formulated, coupling a finite element model with the baseline stiffness, thereby introducing a damage indicator rooted in stiffness ratios. To verify the effectiveness of the proposed method, strain and displacement correlations are analyzed using actual bridge monitoring data, and articulation joint stiffness is identified. The results underscore the capability of the proposed method to accurately pinpoint the location and extent of hinge joint damage.

A Study on the Thermodynamic Response of Double-Armed Thin-Walled Piers under an FRP Anti-Collision Floating Pontoon Fire

Abstract: As a potential fire scenario for bridge structures, the safety impact of an FRP anti-collision floating pontoon fire on bridge structures cannot be ignored. Taking the FRP anti-collision floating pontoon fire that occurred in a continuous rigid-frame bridge as the engineering background, the damage condition of the actual bridge fire scene was first investigated. In addition, FDS 5.3 software was used to simulate the FRP anti-collision floating pontoon fire scenario. Furthermore, the thermal–structural coupling method was used to investigate the thermodynamic response of double-armed thin-walled piers under fire. The results show that the FRP anti-collision floating pontoon fire causes localized concrete carbonization and spalling on the surface of the P2 pier, and the FRP anti-collision floating pontoons are largely destroyed. The fire has the greatest impact on the P2-1 pier, with the highest temperature of 667 °C on the windward side and the highest temperature of 326 °C on the leeward side. The temperature impact range is 6 m above the bearing platform, and the maximum damage depth of pier body concrete is 84.58 mm. The deformation and stress of the P2 pier under fire do not show significant changes and do not exceed the allowable limits for structural deformation and material stress. Therefore, the impact of this fire accident on the structural safety of the continuous rigid-frame bridge is minor. This study's results provide reliable guidance for the fire safety assessment and post-fire structural repair of the continuous rigid-frame bridge.

A new model for chloride ion diffusion in cracked concrete with consideration of damage and strain fields

In this paper, a new model for chloride diffusion in cracked concrete is presented. The model was developed within a standard continuum damage mechanics framework but newly considers both an irreversible damage variable and a strain field, gaps identified through a literature review, offering a more comprehensive description of the diffusion-like processes in concrete with small and moderate cracks than current models. The model utilizes a realistic dependence for the diffusion coefficient on the damage state and employs a bi-logistic mapping function or an S-curve mapping function to effectively describe the non-linear effects of cracks on diffusion in concrete. The dependence on the strain field introduced by this model not only allows examination of diffusion in opening cracks, it also enables the process of crack closing to be simulated, whatever the irreversible damage variable may be. This paper also proposes a novel calibration procedure for experimental testing involving the use of the Brazilian splitting test on samples enclosed in a steel ring. This innovative approach simplifies the process of creating a portfolio of cracks with varying widths. In the calibration tests described here, samples were subjected to natural diffusion while chloride content was monitored in inlet and outlet chambers attached to the samples, eliminating the need for laborious chloride profiling. Our in-depth examination of chloride diffusion in cracked concrete is applicable to analyses of corrosion in steel-reinforced concrete structures worldwide, with the new calibration procedure offering a straightforward method for populating the model with realistic material parameters, thus providing a valuable tool for accurately predicting chloride penetration in real-world scenarios.

Seismic fragility analysis of girder bridges under mainshock‐aftershock sequences based on input‐output hidden Markov model

AbstractCurrent seismic design codes for bridge structures do not account for the influence of aftershock sequences, which, to some extent, overestimate the seismic performance for bridges subjected to mainshock‐aftershock (MS‐AS) scenarios. To address the great need for ground motion sequences tailored to specific research sites for fragility analysis, this study proposes a method for generating artificial MS‐AS ground motion sequences based on the evolutional bimodal Kanai–Tajimi model and the Epidemic–Type Aftershock Sequence model. We establish a framework for MS‐AS fragility analysis using an input–output Hidden Markov Model (IOHMM), where the damage states (DS) of bridge piers are considered unobservable and are inferred statistically through damage indices in an unsupervised manner. Model parameters are trained using intensity measure (IM) sequences and damage index (DI) sequences. Fragility curves for both the mainshock and state‐dependent aftershocks considering multiple aftershocks are formulated based on the initial state probability and state transition probabilities of the proposed IOHMM. The fragility analysis results reveal that as the initial seismic damage level increases, the probability of aftershocks causing higher damage levels in the structure also increases, highlighting the significant impact of aftershocks on structural damage increments. Furthermore, we extend the proposed model to a bivariate seismic intensity measure and develop fragility surfaces. The proposed framework provides a novel approach and insights for tackling seismic fragility under multiple aftershocks.

Impact response of textile-reinforced 3D printed concrete panels

This study assessed the impact response of 3D-printed textile-reinforced concrete for structural applications of 3D concrete printed structures, where impact is a significant load case. To study the impact response, two layers of AR-glass and two layers of carbon textile-reinforced 3D-printed high-strength concrete panels were investigated experimentally under low-velocity impacts from drop weights, respectively. The effect of textile reinforcement on the impact behaviour was compared with unreinforced printed specimens. The specimens were subjected to increasing levels of impact load until the failure was observed. The effect of textile reinforcement on the impact resistance, cumulative energy absorption capacity and failure pattern of printed specimens were investigated and compared with their mould-cast counterparts. To understand the effect of textile reinforcement on the printed and mould-cast panel specimens, a quasi-static flexural test was performed to evaluate the load vs deformation behaviour. The test results from the quasi-static flexural test showed that the incorporation of textile reinforcement improved the first crack strength by about 40 % and enhanced post-peak behaviour for both printed and mould-cast specimens. Further, providing carbon textile reinforcement significantly improved the impact resistance by 75 % when compared to AR glass textile-reinforced specimens due to the higher stiffness and better strain-hardening behaviour. Moreover, the effect of textile reinforcement on enhancing the energy absorption capacity of 3D-printed specimens was more evident at higher impact velocities. The cumulative energy absorption capacity of carbon textile-reinforced specimens was observed to be 60 % higher compared to AR glass textile-reinforced specimens. During high-velocity impacts, the textile reinforcement was observed to improve damage distribution by enhancing the bridging between the interlayers. The damage condition at failure showed that AR-glass textile-reinforced printed and mould-cast specimens showed severe punching failure on the compression face and widened cracks and spalling on the tension face. However, carbon textile reinforcement enhanced the impact resistance, thus showing multiple cracks and reduced spalling on the tension face even after multiple impacts. Overall, the impact performance of 3D-printed textile-reinforced concrete panels showed high-level impact resistance.

Rapid Emergency Response Resilience Assessment of Highway Bridge Networks under Moderate Earthquakes

Quick post-disaster emergency response of highway bridge networks (HBNs) is vital to alleviating the impact of disasters in affected areas. Nevertheless, achieving their emergency response resilience remains challenging due to the difficulty in accurately capturing the response capacity of HBNs and rapidly evaluating the damage states of regional bridges. This study delves into the emergency response, seismic resilience, and recovery scheduling of HBNs subjected to frequent yet mostly ignored moderate earthquakes. Firstly, the feasibility of intelligent methods is explored as a substitute for nonlinear time-history analysis of regional bridges. Subsequently, for realistic modeling of post-disaster HBNs, a decision tree model is developed to determine potential traffic restrictions imposed on damaged bridges. Moreover, their emergency response functionalities are thoroughly investigated, upon which a comprehensive multi-dimensional resilience metric vector is proposed. Finally, the proposed methodologies are applied to the Sioux Falls HBN as a case study, revealing a decreasing mean value and increasing deviation values in the long term. The results are expected to provide important theoretical and practical emergency response guidance.

Probabilistic-based seismic fragility analysis of a ground-bridge structure system considering site liquefaction

Bridge structures are vulnerable to severe damage in previous earthquake events. Meanwhile, the earthquake-induced site liquefaction can significantly affect the seismic performance of bridge structures. Seismic fragility analysis is considered as an effective approach for evaluating seismic performance of bridge structural systems exposed to seismic hazards. This study aims to assess seismic fragility of a ground-bridge structure system considering site liquefaction. Based on the probabilistic seismic demand model (PSDM) and joint probabilistic seismic demand model (JPSDM), a holistic framework for fragility analysis considering site liquefaction is proposed to derive the liquefaction fragility as well as the structural system fragility. The system fragility surfaces obtained by the proposed method can reflect the influence of site properties on the damage exceeding probability of bridge structures. The results demonstrate an increasing probability of undergoing high-level damage states for the whole system containing highly loose sand under strong earthquakes, which may provide significant basis for probabilistic-based aseismic design.

Finite element analysis and simplified envelope curve model for RC shear key under earthquake-tsunami actions

Coastal bridges are vulnerable to the compounded effects of earthquakes and subsequent tsunamis. Their limited lateral restraints can result in critical risks such as the deck unseating or overturning. To this end, reinforced concrete (RC) shear keys are often integrated to enhance the superstructure resilience. While previous studies have investigated their seismic performance, their effects under seismic-tsunami actions remains relatively unexplored yet. This study employed finite element (FE) analysis to examine shear key properties under such conditions, with emphasis on hysteretic curves, concrete damage, and strain evolution. The FE model was developed in accordance with precedent tests, and the modeling results were also compared with tests. A simplified envelope curve model was developed using both geometry and rebar configuration, with various damage states included to reckon their seismic damages after initial earthquakes. The proposed model was validated and aligned well with test results. Further, a framework was proposed to determine the envelope curve for bridges at different damage stages. This study aims to provide insights into the seismic-tsunami analysis on coastal bridges by considering their varying properties under seismic and tsunami actions.