Damage Scenarios Research Articles

Influence of damage on large displacement buckling analysis of beams

Abstract In this study, the influence of concentrated damage on the stability of beams under static loads is investigated according to a co-rotational-based large displacement approach. Well-known benchmark beam-like structures have been considered under different loading conditions and damage scenarios. The concentrated damage has been modelled by means of a cross section reduction of a beam element, following a strategy already adopted in the literature. The results have been expressed in terms of buckling loads and post-buckling responses for different damage scenarios associated with a concentrated damage characterized by different positions, intensities, and extensions.

Deep neural network for damage detection in Infante Dom Henrique bridge using multi-sensor data

This paper proposes a data-driven approach to detect damage using monitoring data from the Infante Dom Henrique bridge in Porto. The main contribution of this work lies in exploiting the combination of raw measurements from local (inclinations and stresses) and global (eigenfrequencies) variables in a full-scale structural health monitoring application. We exhaustively analyze and compare the advantages and drawbacks of employing each variable type and explore the potential of combining them. An autoencoder-based deep neural network is employed to properly reconstruct measurements under healthy conditions of the structure, which are influenced by environmental and operational variability. The damage-sensitive feature for outlier detection is the reconstruction error that measures the discrepancy between current and estimated measurements. Three autoencoder architectures are designed according to the input: local variables, global variables, and their combination. To test the performance of the methodology in detecting the presence of damage, we employ a finite element model to calculate the relative change in the structural response induced by damage at four locations. These relative variations between the healthy and damaged responses are employed to affect the experimental testing data, thus producing realistic time-domain damaged measurements. We analyze the receiver operating characteristic curves and investigate the latent feature representation of the data provided by the autoencoder in the presence of damage. Results reveal the existence of synergies between the different variable types, producing almost perfect classifiers throughout the performed tests when combining the two available data sources. When damage occurs far from the instrumented sections, the area under the curve in the combined approach increases [Formula: see text] compared to using local variables only. The classificatoin metrics also demonstrate the enhancement of combining both sources of data in the damage detection task, reaching close to [Formula: see text] precision values for the four considered test damage scenarios. Finally, we also investigate the capability of local variables to localize the damage, demonstrating the potential of including these variables in the damage detection task.

Structural health monitoring on offshore jacket platforms using a novel ensemble deep learning model

Monitoring health condition of offshore jacket platforms is crucial to prevent unexpected structural damages, where a prevailing challenge involves translating available feature information into structural damage patterns. Although the artificial neural network (ANN) models are popular in addressing this challenge, they often fail to capture the temporal correlations between the feature information and the damage patterns, which reduce their capability for discovering the laws governing the structural damage detection. To bridge this research gap, this study proposes a novel ensemble deep learning model to enhance the temporal feature extraction to improve the damage pattern identification. In this approach, a one-dimensional Convolutional Neural Network (CNN) extracts the spatiotemporal features from the structural vibration measurements. Simultaneously, a SENet attention mechanism is introduced to select the most informatic features. Subsequently, a bidirectional long short-term memory network (BiLSTM) is employed to learn the mapping between the extracted features and the structural damage patterns. Furthermore, the particle swarm optimization (PSO) algorithm is used to optimize the BiLSTM hyperparameters to enhance its stability and reliability. Both simulations and experiments are carried out to collect the vibration responses of the offshore jacket structure in different damage scenarios. The analysis results demonstrate that the proposed method produces remarkable improvement with respect to the accuracy and robustness in identifying the structural damages when compared with the ANNs. The overall detection accuracy of the proposed CNN-BiLSTM-Attention ensemble model is beyond 95%, which provides strong applicability to practical structural health monitoring of offshore platforms.

Damage Classification of a Three-Story Aluminum Building Model by Convolutional Neural Networks and the Effect of Scarce Accelerometers

Structural health monitoring (SHM) plays a crucial role in extending the service life of engineering structures. Effective monitoring not only provides insights into the health and functionality of a structure but also serves as an early warning system for potential damages and their propagation. Structural damages may arise from various factors, including natural phenomena and human activities. To address this, diverse applications have been developed to enable timely detection of such damages. Among these, vibration-based methods have received considerable attention in recent years. By leveraging advancements in computer processing capabilities, machine learning and deep learning algorithms have emerged as promising tools for enhancing the efficiency and accuracy of vibration-based SHM. This study focuses on the application of convolutional neural networks (CNNs) for the classification and detection of structural damage within a steel-aluminum building model. An experimental platform was devised and constructed to generate data representative of building damage scenarios induced by bolt loosening. Both the typical placement of sensors on each floor and the utilization of only one accelerometer were employed to understand the effect of scarcity of accelerometers. By subjecting the building model to controlled vibrations and environmental conditions, the response data from both sensor configurations were collected and analyzed to evaluate the effectiveness of the CNN approach in detecting structural damage under varying sensor deployment strategies. The findings demonstrate that the CNNs exhibited high accuracy in both damage classification and detection, even under scenarios with limited sensor coverage. Moreover, the proposed method proved effective in identifying structural damage within building structures.

Safety assessment of an existing bridge deck subject to different damage scenarios through the global safety format ECOV

The assessment of existing reinforced concrete structures, especially bridges and viaducts, nowadays is one of the most relevant aspects for engineers. In fact, due to the old age of a large part of the infrastructure assets and the deteriorating conditions affecting the structural elements, their safety with respect to the ultimate limit states may be compromised. In this perspective, the assessment by means of global safety formats employing non-linear finite element analyses can be an extremely useful tool. In this context, the study proposes to relate the safety level to the damage through 3D global non-linear numerical analyses. Specifically, the assessment of an existing prestressed reinforced concrete bridge deck is carried out by means of the global safety format denoted as “Estimation of Coefficient of Variation” (ECOV) assuming different damage scenarios. In fact, the safety of the structure is initially assessed with respect to the ultimate limit state in its undamaged condition. Subsequently, different damage scenarios, derived from the inspections, are numerically studied. For each damage scenario, the safety of the deck is evaluated with respect to the ultimate limit state relating the safety loss to the damage level. Finally, damage thresholds for this type of structure are defined on specific static parameters.

Case Studies about Finite Element Modeling and Wireless Sensing of Three Pennsylvania Bridges.

Three Pennsylvanian bridges were studied using finite element and wireless sensor technology. A detailed finite element model of each bridge was created using a commercial software in order to calculate the strains generated by a load that simulates the presence of a standard truck. Pristine and damage scenarios were simulated, and the computed strains were compared to the experimental strains measured with proprietary wireless sensors during a truck test performed by companies not involved in the study presented in this article. The comparison demonstrated the accuracy of the model and the presence of a few non-critical anomalies in terms of load redistribution. In addition, the comparison proved the reliability of the wireless sensing system installed on the bridges, although some drift was observed. The structural monitoring program for the three bridges was also evaluated by processing more than two years of data streamed to a repository.

Multi-Span Box Girder Bridge Sensitivity Analysis in Response to Damage Scenarios

Due to their distinct features, including structural simplicity and exceptional load-carrying capacity, steel box girder bridges play a critical role in transportation networks. However, they are categorized as fracture-critical structures and face significant challenges. These challenges stem from the overloading and the relentless effects of corrosion and aging on critical structural components. As a result, these bridges require thorough inspections to ensure their safety and integrity. This paper introduces generalized approaches based on vibration-based structural health monitoring in response to this need. This approach assesses the condition of critical members in a steel girder bridge and evaluates their sensitivity to damage. A rigorous analytical evaluation demonstrated the effectiveness of the proposed approach in evaluating the Szapáry multi-span continuous highway bridge under various damage scenarios. This evaluation necessitates extensive vibration measurements, with piezoelectric sensors capturing ambient vibrations and developing detailed finite element models of the bridge to simulate the structural behavior accurately. The results obtained from this study showed that bridge frequencies are sufficiently sensitive for identifying significant fractures in long bridges. However, the mode shape results show a better resolution when compared to the frequency changes. The findings are usually sensitive enough to identify damage at the affected locations. Amplitude changes in the mode shape help determine the location of damage. The modal assurance criterion (MAC) served to identify damage as well. Finally, the results show a distinct pattern of frequency and mode shape variations for every damage scenario, which helps to identify the damage type, severity, and location along the bridge. The analysis results reported in this study serve as a reference benchmark for the Szapáry Bridge health monitoring.

Optimal Sensor Placement for Vibration-Based Damage Localization Using the Transmittance Function.

A methodology for optimal sensor placement is presented in the current work. This methodology incorporates a damage detection framework with simulated damage scenarios and can efficiently provide the optimal combination of sensor locations for vibration-based damage localization purposes. A classic approach in vibration-based methods is to decide the sensor locations based, either directly or indirectly, on the modal information of the structure. While these methodologies perform very well, they are designed to predict the optimal locations of single sensors. The presented methodology relies on the Transmittance Function. This metric requires only output information from the testing procedure and is calculated between two acceleration signals from the structure. As such, the outcome of the presented method is a list of optimal combinations of sensor locations. This is achieved by incorporating a damage detection framework that has been developed and tested in the past. On top of this framework, a new layer is added that evaluates the sensitivity and effectiveness of all possible sensor location combinations with simulated damage scenarios. The effectiveness of each sensor combination is evaluated by calling the damage detection framework and feeding as inputs only a specific combination of acceleration signals each time. The final output is a list of sensor combinations sorted by their sensitivity.

Damage detection based on accelerometers and computer vision measurements of moving load-induced structural responses

Damage identification is an important and challenging issue in structural health monitoring to prevent sudden structural failures. This study introduces a novel damage detection method for beams associated with the cross-correlation function of the moving load-induced full-field displacements. Firstly, through theoretical derivation, the structural responses of a single-span beam under arbitrary boundary conditions subjected to a moving concentrated force were investigated, both before and after damage occurred. The changes in structural responses due to local stiffness damage were analyzed from a theoretical perspective. Then, a method is introduced to extract high-resolution mode shapes, which represent the true state of the structure, from the computer vision-based displacements and accelerations measured at limited locations. These mode shapes were then used for full-field displacement reconstruction, and the changes in the cross-correlation function of these reconstructed displacements were employed for damage detection. The proposed method can obtain the higher-order responses compared to the traditional vision-based methods, and the displacements at the acceleration-unmeasured locations can be improved compared to the conventional data fusion method. Finally, the proposed method was validated both in numerical simulations and experimental tests on a beam model with multiple damage scenarios. The results have demonstrated the capability of the proposed method in mode shape extraction, full-field displacement reconstruction, and damage detection.

Enhancing Damage Localization in GFRP Composite Plates: A Novel Approach Using Feedback Optimization and Multi-Label Classification

Damage localization in GFRP (glass-fiber-reinforced polymer) composite plates is a crucial research area in marine engineering. This study introduces a feedback-based damage index (DI) combined with multi-label classification to enhance the accuracy of damage localization and address scenarios involving multiple damages. The research begins with the creation of a modal database for yachts’ GFRP composite plates using finite element modeling (FEM). A method for deriving a feedback-weighted matrix, based on the accuracy of the DI, is then developed. Sensitivity analysis reveals that the feedback DI is 50% more sensitive than the traditional DI, reducing false positives and missed detections. The associated feedback-weighted matrix depends solely on the structural shape, ensuring its transferability. To address the challenge for localizing multiple damages, a multi-label classification approach is proposed. The synergy between the feedback optimization and multi-label classification enables the rapid and precise localization of multiple damages in GFRP composite plates. Modal testing on damaged GFRP plates confirms the enhanced accuracy for combining the feedback DI with multi-label classification for pinpointing damage locations. Compared with traditional methods, this feedback DI method improves sensitivity, while multi-label classification effectively handles multiple damage scenarios, enhancing the overall efficiency of the damage diagnosis. The effectiveness of the proposed methods is validated through experimentation, offering robust theoretical support for composite plate damage diagnostics.

A new algorithm and damage index for detection damage in steel girders of bridge decks using time-frequency domain and matching methods

Damage identification, especially in the early stages, is one of the most important factors in preserving and maintaining structures and infrastructures. Structural Damage can lead to adverse and unwanted changes in the system. Unless these damages are identified and fixed on time, they gradually worsen, which may eventually lead to negative effects on the mechanism of the structure. For example, one of the most common types of damage in all types of structures is cracks causing changes in the characteristics and qualifications of the structures including reducing the stiffness and increasing the damping. The structure may experience significant problems if the crack extension increases. Changes and reductions in the cross-sectional area, changes in the characteristics and qualifications of the materials used, damage in the connections, or boundary conditions are considered to be among the other structural damages. Changes in the construction of the structure can cause changes in the dynamic behavior of the structure, too. In this research, a new algorithm to identify damages in the steel girders of the bridge decks has been presented. Based on this algorithm, the response signals of the steel girder are recorded and the reported signals are processed by Bern-Jordan time-frequency distribution and, finally time-frequency matrices are calculated. Then, the damage is identified and its location is determined by using the proposed damage index (C-Index). In addition, the proposed algorithm has been evaluated on the College Bridge. The analytical model of the College Bridge was designed based on the as-built details, field inspections, and material strength tests. To conduct a comprehensive evaluation, 10 different damage scenarios were considered. The results obtained from this research show that the proposed damage index can identify the damage and its location in steel girders with a high level of accuracy.

Reliability assessment of ship hull girders considering pitting corrosion and crack

The current study aims to investigate the combined effect of cracking and pitting damage on the ultimate strength of ships. The well-known Smith’s approach is modified considering the random number and distribution of cracked-pitted plates in the ship cross-section. Using the Monte Carlo approach, the structural reliability index of the cracked-pitted ship is determined. A single-bottom oil tanker’s ultimate strength is computed, and it turns out that the reliability indices for various damage scenarios are nearly identical when the ship is at its early age. When the ship ages, its reliability index rises to its maximum if the damage is concentrated at the bottom under sagging conditions and at the sides and longitudinal bulkheads in hogging conditions. The reliability indices in the hogging conditions are often greater than those in the sagging conditions. Furthermore, it is determined that, while the ship is at its early age, the detrimental effect of pitting, cracking, or a combination of both on the reduction of the ship’s hull girder ultimate strength is equal. The lowest reliability index is seen in aged ships when cracking and pitting are combined, followed by cracking and pitting damage separately. It is shown that pitting corrosion has a lower reliability index than the general type of corrosion.

Damage assessment in plate structures using an enhanced Jaya algorithm

The article proposes a new method called enhanced Jaya (EJaya) to identify and evaluate damage in plate structures. The method uses a nonlinear optimization task to solve the inverse damage identification problem. The damage magnitudes of the plate elements are taken as design variables, and the objective function to be minimized uses the variations in the plate flexibility matrix and includes a penalization term to avoid false alarm detection. EJaya is developed by incorporating three modifications in the traditional Jaya algorithm: generating an initial population of candidate solutions using prior knowledge of the damage scenario, integrating a component that takes into account the individual distribution through the search space, and implementing a new local search technique. The simulation results show that EJaya is efficient in both solution quality and computational effort and can detect and evaluate damage even in large-sized problems and under noisy data conditions.

Fail-Safe Topology Optimization Using Damage Scenario Filtering

Within the framework of isotropic materials, this paper introduces an efficient topology optimization method that incorporates fail-safe design considerations using a penalty function approach. Existing methods are either computationally expensive or overlook fail-safe requirements during optimization. This approach not only achieves optimized structures with fail-safe characteristics, but also significantly enhances the computational efficiency of fail-safe topology optimization. In this method, the minimization of worst-case compliance serves as the optimization objective, employing the Kreisselmeier–stein Hauser function to approximate the non-differentiable maximum operator. A sensitivity analysis, derived through the adjoint method, is utilized, and a universal fail-safe optimization criterion is developed to update the design variables. During the optimization process for fail-safe strategies, a density-based filtering method is applied, effectively reducing damage scenarios. Finally, the effectiveness and computational efficiency of this method are validated through several numerical examples.

Research on guided wave propagation characteristics of quartz ceramic thermal protection structure for ablation monitoring

ABSTRACT Monitoring the thermal protection structure (TPS) of hypersonic vehicles is vital for ensuring safe flight. The guided wave(GW) structure health monitoring（SHM）method is promising for TPS health monitoring due to its high sensitivity, ability to detect small damages, and wide monitoring coverage, both online and offline. However, there is currently a clear research gap in monitoring hypersonic aircraft TPS in real-service environments. This paper addresses this gap by conducting GW monitoring research on hypersonic vehicles TPS under simulated real-service conditions, involving high-temperature and high-speed airflow ablation. This study takes quartz ceramic TPS as the object, undertaking a preliminary investigation into its GW propagation characteristics. Subsequently, the TPS underwent high-temperature and high-speed oxygen-acetylene ablation at 2100°C to induce damages. GW monitoring was then conducted to capture GW signals in both undamaged and various damage scenarios. Through signal analysis, characteristic parameter extraction, and application of the damage index method, rules governing how GW signals respond to damage were established. Results indicate significant impacts on amplitude and phase of GW signals with increasing damage severity, validating the feasibility of the GW monitoring method for detecting high-temperature and high-speed airflow damage to TPS and supporting its expansion for damage monitoring.

Piezoelectric impedance-based high-accuracy damage identification using sparsity conscious multi-objective optimization inverse analysis

Two elements are essential in structural health monitoring utilizing dynamic responses: response measurement with high-frequency contents, i.e., small characteristic wavelengths, that can adequately reflect damage features, and effective inverse identification analysis that is however oftentimes under-determined. The advancement of smart structure integration has led to active interrogation through frequency-sweeping piezoelectric impedance measurement at high frequency range. In this research we develop a multi-objective optimization formulation for the identification of damage location and severity utilizing piezoelectric impedance. While one optimization objective is to match the response measurement with finite element model prediction in the damage parametric space, the other is the number of locations of damage, i.e., the sparsity of damage index as the solution vector, since damage usually occurs within a small number of locations. This multi-objective formulation fits well the under-determined nature of damage identification, as it naturally provides multiple solutions as basis for further elucidation. The challenge remaining is how to find a small solution set that can include the actual damage scenario. Here we develop a novel inverse identification framework utilizing the intelligent swarm optimizer which possesses flexibility for enhancement. We first embed a sparsity enforcement process into the population generation of the optimizer, which yields a solution repository intrinsically possessing sparsity. We then apply reinforcement learning so the agents can adaptively opt for local strategies with the aim of enriching the searching patterns to diversify the solutions. Through the incorporation of a Q-table, searching toward more promising directions will be rewarded. Our case analyses employing experimental data indicate that this sparsity-conscious multi-objective particle swarm optimization technique can lead to a small solution set which generally encompasses the true damage scenario. This effectively solves the structural damage identification problem with piezoelectric impedance measurement.

Towards vibration-based damage detection of civil engineering structures: overview, challenges, and future prospects

AbstractIn this paper, we delve into the evolving landscape of vibration-based structural damage detection (SDD) methodologies, emphasizing the pivotal role civil structures play in society's wellbeing and progress. While the significance of monitoring the resilience, durability, and overall health of these structures remains paramount, the methodology employed is continually evolving. Our focus encompasses not just the transformation brought by the advent of artificial intelligence but also the nuanced challenges and future directions that emerge from this integration. We shed light on the inherent nonlinearities civil engineering structures face, the limitations of current validation metrics, and the conundrums introduced by inverse analysis. Highlighting machine learning's (ML) transformative role, we discuss how techniques such as artificial neural networks and support vector machine's have expanded the SDD's scope. Deep learning's (DL) contributions, especially the innovative capabilities of convolutional neural network in raw data feature extraction, are elaborated upon, juxtaposed with the potential pitfalls, like data overfitting. We propose future avenues for the field, such as blending undamaged real-world data with simulated damage scenarios and a tilt towards unsupervised algorithms. By synthesizing these insights, our review offers an updated perspective on the amalgamation of traditional SDD techniques with ML and DL, underlining their potential in fostering more robust civil infrastructures.

A multi-objective optimization and multi-attribute decision-making analysis for technical-thermodynamic-economic evaluation considering the rock damage on production performance of hot dry rock geothermal resources

In the long-term mining of geothermal resources in hot dry rock (HDR), the change of thermal stress and pore pressure will increase fracture conductivity evolution, further improving production performance. The optimization and decision-making of the development scheme based on the impact of damage from fractures have yet to be reported. The damage to fractures is essential in designing and adjusting geothermal resource development schemes, particularly in selecting optimal schemes. Therefore, the production performances of HDR resources under different parameters are analyzed to establish a database. Then, minimizing flow resistance, maximizing net power, and maximizing economic benefits are set as optimization goals. Various injection-mining parameters and fracture characteristics are treated as decision variables. Multi-objective optimization and multi-attribute decision analysis is conducted to obtain optimal schemes. Finally, optimal schemes are evaluated and compared, considering damage and non-damage scenarios. Results show that the NSGA-II algorithm is more suitable for optimizing geothermal development questions. Net power and economic benefits of the optimal scheme considering damage increase by 45.84 % and 21.35 % compared to the control scheme with damage. For the non-damage scenario, the above values increased by 31.55 % and 5.15 %, respectively. Compared to not considering the damage, higher mass flow and well spacing of optimal scheme can be selected for the case when damaged. Moreover, the parametric design of the optimal scheme becomes more conservative as the production cycle increases.

Fragility Analysis Framework for Bridges Subjected to Successive Natural Hazards

Bridges are key parts of transportation networks and hence, their post-hazard condition is vital for the recovery of the regions affected by natural hazards. However, the fact that they are often subjected to more than one destructive natural hazard during their life span resulting in accumulated damage that may influence their capacity has attracted relatively little research interest, while almost no design standards take it into consideration. The multiple hazard events may be successive single events, separated by considerable time intervals (e.g. an earthquake followed by a flood event), combined or cascading multiple-hazard events (e.g, landslide after an earthquake), or even simultaneous events. The main objective of this study is to propose an effective framework for the development of multiple-hazard fragility curves of bridges considering the effect of subsequent events and taking into account different possible damage scenarios of the bridge critical components, i.e. piers, bearings and the abutment-backfill systems. In this framework, the effect of the damage level of the critical components and their interdependence on the fragility assessment for multiple events is examined and compared with the bridge fragility for single events. A pilot case study is also included in the present work, aiming to highlight the effect of the initial damage state on the final fragility estimate of a typical riverine bridge.

Advanced Fiber Beam Finite Element Model for Neural Network Training in Vibration-Based Bridge Monitoring

Recent advancements in civil infrastructure monitoring have witnessed the increasingly high-performance sensor technologies and data-driven algorithms, opening up new possibilities for assessing structural conditions. In recent years, there has been a growing interest in leveraging the potential of Artificial Intelligence for civil infrastructure monitoring. One promising approach is the use of computational models to train and test data-driven algorithms aiming to tackle damage detection problems. To enhance the effectiveness of such procedures based on simulated data, this study proposes a high-performance beam finite element model for training a neural network model able to predict the dynamic response of the structure and for generating various damage scenarios. Compared to 2D and 3D finite element models, the advanced fiber beam model offers superior computational efficiency while accurately capturing the nonlinear behavior of structural elements. Specifically, a force-based beam finite element based on a damage-plasticity model is implemented to describe damage and degradation of materials in reinforced concrete girders. Through the simulation of the dynamic structural response under withe noise excitation, a neural network model representing the structure in the undamaged conditions is obtained. The prediction error of such network model is investigated as a suitable measure for the definition of a damage indicator able to detect the presence of damage (concrete cracks and reinforcement yielding). The integration of an advanced fiber beam model, accurate constitutive law and neural network models shows promising potential in the monitoring of existing bridges.