Structural Damage Assessment Research Articles

Multitask vibration transformer for abnormal vibration identification and structural damage assessment in subway tunnels

As urban infrastructure becomes denser, subway operations face numerous risks, including adjacent construction and structural damage deterioration. Integrated monitoring is crucial to ensure safety. This study presents a multitask vibration transformer (M-TVT) model, which utilizes an Expert-Gate network to manage task-related information sharing. The model achieves integrated identification for the type and spatial distribution of internal and external tunnel abnormal vibration responses, along with assessing structural damage degree. Meanwhile, a tunnel-train-soil coupling experiment is conducted to validate the efficacy of the M-TVT model. The results demonstrate that the M-TVT model with the Expert-Gate network maintains high accuracy and stability, efficiently shares parameter information among tasks and achieves improvement in accuracy compared to single-task models.

Structural response and damage assessment method for subsea pipe-in-pipe subjected to anchor impact

The pipe-in-pipe system is widely used in the development and transportation of offshore oil and gas resources. The damage caused by anchor impacts on the pipe-in-pipe system has long been a focal point and challenge in both engineering and academic fields. Numerical simulation method is employed to investigate the impact process of anchors on pipe-in-pipe systems, revealing the variation patterns of pipeline deformation response over time. The structural response of the pipe-in-pipe system to dropped anchor impacts is studied from multiple perspectives, including deformation, energy, and load transfer. The influence of pipe-in-pipe characteristic parameters, anchor characteristic parameters, centralizers, and bulkheads on the extent of impact damage is analyzed, establishing a relationship between the absorbed energy and the damage to the pipe-in-pipe. The results indicate that the impact energy primarily depends on the mass and velocity of the anchor. Energy is conserved during the impact process. It is found that even with different anchor masses and velocities, the extent of damage to the pipe-in-pipe system remains the same if the impact energy is identical. High-strength steel, large diameter, and thick-walled pipelines exhibit better resistance to impact loads. Additionally, centralizers and bulkheads mitigate the impact damage to the pipe-in-pipe system. Finally, an evaluation method for pipe-in-pipe impact damage due to dropped anchors is proposed. It is discovered that the assessment method for pipelines impacted by dropped anchors in the DNV-RP-F107 standard tends to be conservative when the impact energy is high. The findings of this study provide technical support for the integrity management of pipe-in-pipe systems.

Acoustic Emission characteristics and damage evolution of Concrete-Encased CFST columns under compressive load

Concrete-Encased Concrete Filled Steel Tubular (CE-CFST) is usually served as a compression member in engineering structures due to its high performance. It is crucial to reveal its compressive failure mechanism and the damage evolution law. This study used Acoustic Emission (AE) technology to monitor the compressive failure behavior of seven groups of CE-CFST columns with different diameter-width ratio, slenderness ratio and eccentricity, and then the AE signal characteristics and structural damage evolution law were discussed. Results show that the curve of AE characteristics can be used for effectively identifying the damage stages of CE-CFST structures. The failure process can be divided into six stages: initial compaction, stable growth of micro cracks, unstable propagation of macro cracks, collapsing of the outer RC structure, bulging of the core CFST structure and overall failure. The AE characteristic parameters, RA-AF value and b value are closely related to stress and crack of structure, and can be used for early warning of structural failure during the stage of unstable crack propagation. Particularly, the failure of outer concrete can be judged as b value drops to the minimum. The current study is of great significance for understanding the damage evolution process and achieving damage assessment of CE-CFST structures.

Electromechanical admittance-based automatic damage assessment in plate structures via one-dimensional CNN-based deep learning models

Electromechanical admittance-based automatic damage assessment in plate structures via one-dimensional CNN-based deep learning models

Automatic damage detection and segmentation using deep learning algorithms in reinforced concrete structure inspections

AbstractTraditional methods of detecting concrete damage, such as manual inspection, are typically slow, labor‐intensive, and subjective. Integrating deep learning algorithms has automated this process, representing a significant advance in building damage inspection. Semantic segmentation, a technique within deep learning, is increasingly recognized for its capability to identify the location and shape of concrete damage accurately. Unlike basic deep learning approaches like classification and object detection, semantic segmentation not only recognizes damage but also delineates its boundaries, and has great potential in facilitating dimension measurement. However, multi‐level, high‐precision detection for various structural damage assessments remains an area requiring further research. This study created a database of reinforced concrete surfaces with pixel‐level, multi‐category semantic segmentation annotations for various levels of component damage, including cracks and spalling of concrete, exposure, buckling, fracture of reinforcing bars, etc. The performance of advanced deep learning segmentation algorithms, including U‐Net, DeepLab, K‐Net, and FastSCNN, was consequently trained and evaluated in detecting various types of concrete damage. All models show good accuracy of more than 98%, but lower F1 scores around 70%. U‐Net and K‐Net demonstrate relatively stable performance, indicating a certain degree of consistency and a high‐performance peak. In contrast, DeepLabv3's F1 score fluctuates significantly, suggesting the model may suffer from overfitting or other stability issues during training, resulting in a relatively low average performance. FastSCNN exhibits the highest potential, achieving the highest F1 score. In addition, this study also tested the performance of each model on new damage images and explored the impact of the dataset proportion ratio (training set vs. validation set), contributing to providing insights into each algorithm's suitability for various inspection scenarios. Finally, perspectives on current challenges and future directions in the field have also been given.

Executing realistic earthquake simulations in unreal engine with material calibration

Earthquakes significantly impact societies and economies, underscoring the need for effective search and rescue strategies. As AI and robotics increasingly support these efforts, the demand for high-fidelity, real-time simulation environments for training has become pressing. Earthquake simulation can be considered as a complex system. Traditional simulation methods, which primarily focus on computing intricate factors for single buildings or simplified architectural agglomerations, often fall short in providing realistic visuals and real-time structural damage assessments for urban environments. To address this deficiency, we introduce a real-time, high visual fidelity earthquake simulation platform based on the Chaos Physics System in Unreal Engine, specifically designed to simulate the damage to urban buildings. Initially, we use a genetic algorithm to calibrate material simulation parameters from Ansys into the Unreal Engine’s fracture system, based on real-world test standards. This alignment ensures the similarity of results between the two systems while achieving real-time capabilities. Additionally, by integrating real earthquake waveform data, we improve the simulation’s authenticity, ensuring it accurately reflects historical events. All functionalities are integrated into a visual user interface, enabling zero-code operation, which facilitates testing and further development by cross-disciplinary users. We verify the platform’s effectiveness through three AI-based tasks: similarity detection, path planning, and image segmentation. This paper builds upon the preliminary earthquake simulation study we presented at IMET 2023, with significant enhancements, including improvements to the material calibration workflow and the method for binding building foundations.

Deep Learning Method for Post-earthquake Damage Assessment of Frame Structures Based on Time–Frequency Analysis and CGAN

Deep Learning Method for Post-earthquake Damage Assessment of Frame Structures Based on Time–Frequency Analysis and CGAN

Comparative analysis of saliency map algorithms in capturing visual priorities for building inspections

This study investigates the efficacy of saliency mapping algorithms in capturing the visual priorities of building inspectors for structural damage assessment. Our work established a ground truth dataset by implementing eye-tracking technology to capture the gaze patterns of building inspectors. Further, it enables a detailed evaluation of the saliency models’ ability to reflect experts' visual attention during inspection tasks. Our comparative analysis assesses the performance of three saliency models— EnDec, DeepGaze, and SALICON— against this ground truth data, using conventional saliency metrics such as Area under the Curve, Similarity, Normalized Scanpath Saliency, Correlation Coefficient, and Kullback-Leibler Divergence. Our findings reveal that while the SALICON model demonstrates a marginally better performance and highlights areas where these models fall short, particularly in accurately reflecting the critical visual properties of inspectors, this insight is crucial for advancing the field. By highlighting these limitations, we have drawn attention to the need for developing more specialized saliency models tailored to the unique demands of building inspection tasks. Thus, the study not only fulfills its objectives of comparative analysis but also contributes to the broader discourse on improving automated structural inspection systems. This study highlights the need to develop specialized computer vision models to address specific building inspection challenges. By identifying strengths and improvement areas, this research contributes valuable insights and highlights the potential and current limitations of applying computer vision techniques to real-world building inspection tasks.

Research and Modification on the Park-Ang Damage Index for Railway Rectangular Piers

ABSTRACT This study addressed the issue of distortion in evaluating the seismic damage state of railway bridge piers using the original Park-Ang damage index. By constructing a parameterized co-simulation program with two finite element models, OpenSees and Abaqus, five sets of flexural failure and five sets of flexural-shear failure rectangular pier quasi-static test results were selected to validate the rationality of the calculation program. Four hundred and eighty parameterized calculations were conducted using this program. The combination coefficient β and critical damage index DI within the Park-Ang damage index were modified using nonlinear regression algorithm and convolutional neural network algorithm. The adaptability of the damage index calculation was ultimately verified through an engineering case study of a simply supported beam bridge. The research findings are as follows: 1) The main factor influencing β is the longitudinal reinforcement ratio, while the axial compression ratio, shear span ratio, volumetric stirrup ratio, aspect ratio, concrete strength, and steel strength are secondary parameters influencing β. 2) Compared to the nonlinear regression algorithm, the convolutional neural network algorithm can better establish a calculation model between pier structural parameters and β, clarifying their relationship. 3) It is recommended to use critical values of 0.11, 0.29, 0.49, and 1.00 for assessing pier damage as no, slight, moderate, severe, and collapse damage when using the damage index calculation model. 4) The Park-Ang index corrected by convolutional neural networks demonstrates improved accuracy and computational stability, making it suitable for practical assessment of seismic damage in bridge structures.

Modelling the maximum ceiling temperature with bifurcated plume in tunnel fires

Evaluation of the temperature profile induced by tunnel fire is of great significance for fire detection and structural damage assessment. In this work, a special plume model, namely, the “plume bifurcation” is introduced, and its impact on the temperature distribution is analysed theoretically. Full-scale tunnel fire experiments and numerical simulations are conducted to investigate the plume evolution under various heat release rates and ventilation conditions. Results show that the plume will sperate into two sub-streams under strong ventilation condition, resulting in the multi-dimensional plume movement pattern when rising. The sub-plume impingement point location, quantified by the longitudinal vertical deflection angle θv and horizontal bifurcation angle θh, is correlated with the fire heat release rate and ventilation condition. The plume bifurcation angle is 0 when the dimensionless ventilation velocity V′≤0.33, marking a single-stream plume; and rapidly increase and then slowly decrease with V′ when V′>0.33, representing the sudden occurrence and decay trend of plume bifurcation with the ventilation velocity. Finally, a novel bifurcated plume-based model to estimate the maximum ceiling temperature is proposed. This study reveals the role of multi-dimensional smoke movement in reshaping the temperature field, providing new perspectives for a deeper understanding of the smoke transport behaviour in longitudinally ventilated tunnel fires.

A novel spike detection model for dynamic stress monitoring of bogie frame

The fatigue evaluation of the bogie frame is an important part of the structural health monitoring of the vehicle. During the dynamic stress monitoring, some signal spikes, which are much larger than the normal fluctuation range due to the interference of the complex electromagnetic environment, affect the accuracy of the structural damage assessment and need to be accurately detected and replaced. Aiming at the drawbacks of traditional detection methods that are overly dependent on engineering experience and not universal, a novel spike detection model is proposed in this paper. By the process of data transformation, spike region features are effectively separated. Based on the isolation forest algorithm, the normalized anomaly score of each point is calculated, and the threshold is determined adaptively. The spike detection rate and damage sensitivity are proposed as the evaluation indices of the detection effect of the method. The results show that the spike detection rate is improved by 7.86% on average, and the damage sensitivity is improved by 15.59% on average. The spike detection model in this paper is significantly improved compared to the existing methods.

A rapid damage assessment framework for regular RC frame structures under external explosions

Local damage of the structural members and even progressive collapse of structures occurred in terrorist or accidental explosions, but there are few unified frameworks of integrating the member damage into the structural damage for rapid assessment against blast loads. To fill this gap, a framework is proposed for engineering practice to rapidly assess damage levels of regular reinforced concrete (RC) frame structures under external explosions, in which the structural damage level is eventually determined with only two inputs: scaled distance and structural redundancy. A regular 5-story RC frame structure subjected to external explosions is adopted as an example to illustrate the proposed framework. Firstly, damage indexes of structural members are formulated under far-field explosions or close-in explosions, and the structural damage index is further derived through the weighted integration of the damage indexes of structural members. Subsequently, the damage modes and collapse processes of the designed RC frame structure are simulated under different explosion scenarios, and the energy-based damage indexes for assessing structures against blast loads are developed and validated. Through numerical results of 23 case studies, the structural damage assessment formula as a function of scaled distance and structural redundancy is derived through regress analysis, and on the other hand, two key threshold values of the energy-based damage indexes are introduced to classify the structural damage levels (minor, moderate and severe levels). Finally, the structural damage index is correlated to the energy-based damage index through curve-fitting to determine the corresponding threshold values of structural damage index as well, and the effectiveness of the damage assessment framework is further validated. The results show that the proposed damage assessment framework can rapidly and accurately assess the damage levels of the regular RC frame structures under external explosions, and the deviation between the predicted damage indexes and the numerical results are 4.11 %–9.52 %.

Structural damage identification based on Bayesian updating

Due to factors such as model accuracy, sensor arrangement, noise influence, eigenvalue selection and the influence of local damage on the overall response, the structural damage identification process inevitably has uncertainties, so it is necessary to introduce probabilistic statistical methods to improve its accuracy and robustness. In this paper, structural damage identification is carried out based on Bayesian updating. Bayesian updating is combined with real-time monitoring data (e.g. displacement, acceleration), and the real-time data reflect the current state of the structure and contain damage information. The experimental results show that the update results of the Bayesian method correlate well with the actual data, which verifies the feasibility of Bayesian updating in structural damage identification. The structural prior probability can be effectively updated by this method, which shows high accuracy and robustness in structural damage identification and is able to maintain stable performance under different working conditions and environments. Based on Bayesian updating, this structural damage identification method provides a new technical means for structural health monitoring and damage assessment, with broad application prospect and development space.

Damage evolution analysis of C/SiC screwed/bonded hybrid joints based on in-situ micro-CT technique

Due to own good high temperature mechanical properties, the C/SiC screwed/bonded hybrid joints are considered as an important development direction of vehicle connection structures. However, the difficulty of directly monitoring the interior of the hybrid joints during the bearing presents a potential challenge to the structural damage assessment. In this study, the damage process of the hybrid joints subjected to tensile loading was monitored and characterized based on the in-situ micro-CT technique. The intrinsic relationship between the mechanical responses of the hybrid joints and the local damage evolution was elucidated by observing the variation of the deposited SiC void volume at the overlap interface. The dual inhomogeneity of SiC bonding layer was quantitatively characterized utilizing the average failure rate and determination coefficient. The coupling influence mechanism of prefabricated bottom hole size and online connection process on the final assembly clearance was revealed.

A new deep learning-based approach for concrete crack identification and damage assessment

Concrete building structures are prone to cracking as they are subjected to environmental temperatures, freeze-thaw cycles, and other operational environmental factors. Failure to detect cracks in the key building structure at the early stage can result in serious accidents and associated economic losses. A new method using the SE-U-Net model based on a conditional generative adversarial network (CGAN) has been developed to identify small cracks in concrete structures in this paper. This proposed method was a pixel-level U-Net model based on a generative network, that was integrated the original convolutional layer with an attention mechanism, and an SE module in the jump connection section was added to improve the identifiability of the model. The discriminative network compared the generated images with real images using the PatchGAN model. Through the adversarial training of generator and discriminator, the performance of generator in crack image segmentation task is improved, and the trained generation network is used to segment cracks. In damage assessments, the crack skeleton was represented by the individual pixel width and recognized using the binary morphological crack skeleton method, in which the final length, area, and average width of the crack could be determined through the geometric correction index. The results showed that compared with other methods, the proposed method could better identify subtle pixel-level cracks, and the identification accuracy is 98.48%. These methods are of great significance for the identification of cracks and the damage assessment of concrete structures in practice.

Diagnosis and assessment of cyclic freeze–thaw damage in tunnel lining concrete using piezoelectric-based electromechanical impedance technique

Cold regional tunnel linings extensively suffer from severe cyclic freeze–thaw damage, which adversely influences their stability, durability and in-service safety. Consequently, the diagnosis and assessment of cyclic freeze–thaw damage in concrete linings have garnered worldwide attention. This study presents a pioneering effort in using the PZT-based electromechanical impedance (EMI) technique to monitor the evolving damage within lining concrete concurrently exposed to bending loads and freeze–thaw cycles. First of all, ultrasonic measurements and four-point bending tests were performed on cyclically freeze-thawed concrete specimens under two different bending loads. Both the degrading flexural strength and the diminishing ultrasonic wave velocity were measured. Particularly, the damage evolution was characterized by the flexural strength degradation ratio, which exponentially increased by 44.81% and 64.28% under two bending scenarios, respectively. Moreover, conductance signatures driven by two vibration modes (d31 and d33 modes) of embedded piezoelectric transducers were recorded, and both localized and overall variations in the signals were analyzed. It is concluded that both the resonant frequency shift and the statistic metric RMSD within the d31-dominated frequency range could accurately indicate the cyclic freeze–thaw damage of lining concrete. Their linear correlation coefficients with the damage variable reached 0.957 and 0.935, respectively. This research establishes a quantitative framework for monitoring the progressive damage of tunnel lining subjected to freeze–thaw cycling using the EMI technique and lays the groundwork for a broader range of future applications in structure damage diagnosis and assessment.

Magnetic Resonance Imaging (MRI)-Based Semi-Quantitative Methods for Rheumatoid Arthritis: From Scoring to Measurement.

Rheumatoid arthritis (RA) is a chronic autoimmune disease that primarily affects the small joints of the hands and feet, characterized by pain, inflammation, and joint damage. In this context, magnetic resonance imaging (MRI) is useful to identify and monitor joint/tendon inflammation and the evolution of joint damage, playing a key role in treatment response evaluation, in addition to clinical measurements. Various methods to quantify joint inflammation and damage with MRI in RA have been developed, such as RA-MRI Score (RAMRIS), Early RA-MRI Score (ERAMRS), and Simplified RA-MRI Score (SAMIS). RAMRIS, introduced in 2002, offers an objective means to assess inflammation and damage via MRI in RA trials, encompassing findings such as synovitis, bone erosion, and edema/osteitis. Recently, an updated RAMRIS version was developed, which also includes the evaluation of joint space narrowing and tenosynovitis. The RAMRIS-5, which is a condensed RAMSIS version focusing on five hand joints only, has been proven to be a valuable resource for the semi-quantitative evaluation of RA joint damage, both in early and established disease. This narrative literature review will provide an overview of the MRI scoring systems that have been developed for the assessment of joint inflammation and structural damage in RA patients.

Correlation coefficients of vibration signals and machine learning algorithm for structural damage assessment in beams under moving load

This paper presents a novel method of assessing structural damage in beams exposed to moving loads via acceleration signals through experimental studies. In this study, beams are supported on both ends, and their dynamic response to moving loads is assessed. The raw signal has been improved using a random decrement technique. Take measurements from different locations and calculate correlation coefficients between them, then use these as features to evaluate the structure. In order to create a reliable and potential framework for predicting damage efficiently, these features are used as input variables to the machine learning model. The proposed methodology exhibits promising results in accurately discerning and predicting damage in beam structure. It demonstrates a high level of precision to subtle changes in structural integrity when trained by machine learning on the statistical feature extracted from acceleration signals. As a result of this research, methods for detecting structural damage can be made more reliable and efficient by employing machine learning techniques. Additionally, structures operating in dynamic environments can benefit significantly from the proposed methodology.

Predicting Blast-Induced Damage and Dynamic Response of Drill-and-Blast Tunnel Using Three-Dimensional Finite Element Analysis

The significance of predicting the dynamic response and damage of an existing concrete tunnel during underground blasting has increased owing to the close proximity between the newly built and existing tunnels. Peak particle velocity (PPV) is a commonly used criterion in the assessment of blast-induced structural damage. However, such structural damage is also associated with the frequency content of the blast wave. Nevertheless, the recommended threshold PPVs, which are based on empirical criteria, predict conservative estimations. Using stringent and regulated blasting methods often results in project delays and escalates the total project expenditure. In this paper, a three-dimensional finite element model of an underground tunnel has been developed in LS-DYNA to analyze damage to the concrete tunnels under blast loading. A suite of analyses was performed to examine the potential damage induced in the underground tunnel. A lower frequency load was found to have a greater potential for producing damage compared with a high frequency blast load. The results showed that the location of the cracking within the tunnel, such as the arch foot or tunnel wall, was also influenced by the frequency of the blast wave. The maximum allowable PPV for the concrete tunnel was determined for a range of frequencies based on predicted free field PPV and additional factors of safety of 1.2 and 1.5 were established, depending on the safety needs and importance of the tunnel construction. Thus, our findings provide useful information for improving the evaluation of tunnel damage and guaranteeing the safety of underground tunnels.

Evaluating Human Expert Knowledge in Damage Assessment Using Eye Tracking: A Disaster Case Study

The rising frequency of natural disasters demands efficient and accurate structural damage assessments to ensure public safety and expedite recovery. Human error, inconsistent standards, and safety risks limit traditional visual inspections by engineers. Although UAVs and AI have advanced post-disaster assessments, they still lack the expert knowledge and decision-making judgment of human inspectors. This study explores how expertise shapes human–building interaction during disaster inspections by using eye tracking technology to capture the gaze patterns of expert and novice inspectors. A controlled, screen-based inspection method was employed to safely gather data, which was then used to train a machine learning model for saliency map prediction. The results highlight significant differences in visual attention between experts and novices, providing valuable insights for future inspection strategies and training novice inspectors. By integrating human expertise with automated systems, this research aims to improve the accuracy and reliability of post-disaster structural assessments, fostering more effective human–machine collaboration in disaster response efforts.