Structural Condition Assessment Research Articles

Structural Material Condition Assessment through Human-in-the-Loop Incremental Semisupervised Learning from Hyperspectral Images

Structural Material Condition Assessment through Human-in-the-Loop Incremental Semisupervised Learning from Hyperspectral Images

Deep learning-based approach for identifying vortex-induced vibrations in stay cables

Due to variations in wind speed profiles along the length of bridge stay cables, vortex-induced vibrations (VIV) exhibit multimodal characteristics, presenting challenges for VIV identification. Currently, the VIV identification is concentrated on the stable stage of VIV, lacking an available early warning system for detecting the initial developing stage of VIV. In this study, a deep learning-based approach that integrates energy distribution ratio features derived from frequency band wavelet packet decomposition to recognize VIV of stay cable was proposed. Firstly, vibration characteristics induced by vortices in cable-stayed bridges were analyzed based on field monitoring data from the bridge health monitoring system, aiming to propose suitable feature indicators for VIV identification. Secondly, using root mean square as label classification, a deep learning model was constructed, incorporating convolutional neural networks, long short-term memory networks, and attention mechanisms. Finally, four different stages in the evolution of stay cables VIV were identified utilizing field monitoring datasets to analyze the optimal parameter. Meanwhile, effective early warning recognition was achieved through the classification and recognition of confusion matrix. This study provides technical support for early warning systems and structural condition assessment concerning bridge stay cable VIV.

Intelligent Assessment of Pavement Structural Conditions: A Novel FeMViT Classification Network for GPR Images

Intelligent Assessment of Pavement Structural Conditions: A Novel FeMViT Classification Network for GPR Images

Noise-robust automated sudden damage detection using blind source separation enhanced by variational mode decomposition and support vector machine based on shapelet transform

Compared to parametric counterparts, non-parametric (aka, model-free) damage detection methods have no requirements of accurate models, with the potential of autonomous monitoring of various complex structures. However, noises, or low signal-to-noise ratio (SNR), are one of the main challenges. This study is aimed at improving blind source separation (BSS)-based damage detection method, one of the most advanced non-parametric methods, in both aspects of noise robustness and autonomous operation. In particular, the measured acceleration responses are processed by variational mode decomposition (VMD) and wavelet transform (WT) in sequential, acting as the input for a BSS model. The BSS is then solved by independent component analysis (ICA), which approves to be more noise-robust compared to the state-of-the-art counterparts. Furthermore, shapelet transform is applied to extract the universal shape-based spike-like feature from the BSS model for training a support vector machine (SVM) model, applicable to different structures; it finally automates the sudden damage detection process and enables online monitoring. The effectiveness of the proposed method is illustrated by a numerical example and an experimental test, and demonstrated by a real-world seismic-excited structure. The results show that both single and multiple sudden damages can be automatically detected with high accuracy. Compared with the existing BSS methods, the proposed BSS method is more capable to detect small damages at relatively low SNR. In addition, the classification accuracy of SVM is also improved when shapelet-based feature is used for training, which reduces the malfunction of automated damage detection as shown by the numerical example. Therefore, the proposed strategy has the potential for rapid condition assessment of structures during rare/extreme events, before engineers are sent for further post-event inspection.

System identification for structural condition assessment: Application to critical neoclassical monuments in Nepal

This paper presents structural condition assessment of some critical neoclassical monuments in Nepal using nonparametric and parametric system identification techniques. Neoclassical buildings in Nepal house critical administrative units. Structural condition assessment of such buildings is therefore important for the safety of their occupants and interrupted function during and after major earthquakes. The historical and cultural values of such monuments necessitate regular structural assessment for maintenance, repair, and preservation. Dynamic characteristics such as natural vibration frequencies of structures provide valuable insights on their structural condition. While numerical models are commonly used to estimate eigen frequencies of structures, they are associated with large uncertainties in massive monuments with complex structural and geometrical configurations. Noninvasive dynamic identification techniques provide an alternative means in such structures. Ambient vibration records from six neoclassical monuments in the Kathmandu Valley, Nepal are used in this study to estimate vibration frequencies of the structures in different states. The structures were damaged during the 2015 Gorkha earthquake, and subsequently retrofitted. Changes in vibration frequencies before and after retrofitting provide useful insights on structural improvement through retrofitting. Input-output and output-only system identification techniques are tested to simplify the structural condition assessment approach. We conclude that the state space system identification can stably quantify dynamic properties so stiffness variation can be confidently extracted, which is the key application for noninvasive structural system identification. Also, using minimum number of sensors, we captured damage aggravation deploying the modal assurance criterion. The outcomes indicate that structural condition assessment of complex structures is possible using a limited number of sensors for circumstances such as damage aggravation to temporal variation (evolution/reduction) of dynamic characteristics.

Technical aspect of the operation of accelerometers as composition of a system for monitoring engineering structures of a cable-stayed bridge over the Petrovsky canal in the construction of the Western Speed Diameter highway in Saint Petersburg

Aim. This study analyzes the current monitoring system for the engineering structures of a cable-stayed bridge across the Petrovsky Canal, part of the Western High-Speed Diameter highway in St. Petersburg, focusing on emergency situations where accelerometers on pylons record values that exceed limits. Methods and Materials. The study utilizes statistical data from existing monitoring system databases. The tasks, set within the framework of the study, are accomplished by applying theoretical scientific methods including analytical techniques, mathematical statistics, and induction. Results. The analysis presents an overview of the current system used for monitoring engineering structures and highlights issues related to emergency situations in which accelerometer readings exceed threshold values. This study proposes a method for assessing the reliability of the monitoring system by classifying the data obtained from the accelerometer into two groups. The research identifies the need for further investigations to understand the root causes of these issues. Conclusion. Тhe results can assist in the design and installation of monitoring systems of the engineering structures for cable-stayed bridges, as well as for modernization and optimization of existing monitoring systems to improve the quality of structural technical condition assessments.

Exploring the effective implementation of population-based SHM in existing buildings. Part I: Structural condition assessment

Despite major critical progress over the past few decades, condition-based decision-making concerning the immediate occupancy or efficient operation of an existing building continues to face unresolved issues. The key issue is to determine the structural condition and its residual capacity using data-driven-based methods without knowledge of its life cycle since design (ageing, extreme events, etc.). Environmental and operational variations, boundary conditions and a lack of damage-state building data lead to further uncertainties in the actual assessment of the structural capacity and affect the effectiveness of implementing structural health monitoring (SHM) solution. Recently, population-based SHM has been developed to address some of these issues to enable transfer from data collected from a group of nominally identical buildings, and to model missing data for the target building. This study presents the Build’Health™ framework for operational data-driven SHM, considering populations of buildings evaluated under operational conditions. Fifty-five period-height empirical models obtained from methods based on ambient vibrations recorded in nominally identical reinforced concrete buildings were first identified in the literature to define the building population models, along with design features (such as boundary conditions, geometry or material design). The shift in structural condition of the target building is inferred from the nominally identical building population, after adding empirical variability in the resonance period due to temperature, assumed herein to be the main environmental cause of structural variation. This work presents the framework implemented and its application to five target buildings under different monitoring and structural conditions. This manuscript is the first of a two-part article on population-based SHM relative to structural condition assessment (part I) and damage-feature classification (part II) presenting the Build’Health™ concept.

Condition assessment of concrete structures using automated crack detection method for different concrete surface types based on image processing

In the inspection and diagnosis of concrete construction, crack detection is highly recommended in the earliest phases to prevent any potential risks later. However, the flaws in concrete surfaces cannot be reliably and effectively identified using traditional crack detection techniques. The suggested algorithm is a supportive tool for agents or authorities to use in crack detection mechanisms to monitor and assess the current condition of buildings or bridges. The researchers aim to establish an intelligent model for automatic crack detection on different concrete surfaces based on image processing technology. Three different concrete surfaces—bridge decks, walls, and concrete cubes—are used to test the model. A subset of the public dataset of bridge decks and walls from SDNET (2018) and 150*150*150 mm of concrete cubes taken from the material laboratory of the faculty of engineering at Ain Shams University are applied to the model. The model F1-score measures are 98.87%, 97.43%, and 74.11% for detecting cracks in bridges, walls, and concrete cubes, respectively. The validation of the applicability of the suggested novel approach is based on a comparison with recent methods for crack recognition. The contribution of this study is that it could be applied to detect cracks efficiently on three different types of concrete surfaces, including uneven concrete surfaces, random noise, voids, dents, colour changes, and stain marks. The proposed method is transparent in its workflow and has a lower computational cost compared with deep learning frameworks. Thus, the outcomes of this model demonstrate its effectiveness in concrete defect field investigation.

Optimizing self-sensing performance of conductive mortar via gradation of graphene coated aggregate

With the increasing focus on structural health monitoring of concrete during its service life, self-sensing functional concrete has emerged as an effective method for real-time detection and assessment of structural conditions. We recently proposed a new methodology of preparing conductive cementitious mortar with graphene-coated aggregates (Gr@Ag). Instead of the traditional approach, where conductive fillers are directly intermixed and uniformly distributed in the binding matrix, the development of Gr@Ag has created a more efficient path for electron transport through the connections of aggregates. The main objective of this study is to understand the effect of aggregate gradation on the electrical performances of conductive mortars, and the piezoresistivity is optimized by adjusting the aggregate packing. Electrochemical impedance spectroscopy was employed to investigate the underlying mechanisms of how aggregate gradation affects the mortar's electrical conductivity. It was found that both the internal resistance and resistivity of the conductive mortar considerably decreased with the reduction in Gr@Ag particle size. The contribution of conductive sand to the conductivity of mortar can reach 80.89–92.36 %, effectively transforming the conductive path from a disconnected state to a connected state. Reducing the particle size of the conductive aggregates contributes to an increase in particle contact rate, with the particle contact rate reaching up to 59 %. The presence of completely isolated conductive aggregates is noticeably reduced. As the particle size decreases, there is a significant reduction in mechanical performance. The conductive aggregates become relatively isolated from the matrix, and both the size of the conductive aggregates and the interfacial transition zone play vital roles to affect the mechanical properties.

Dmg2Former-AR: Vision Transformers with Adaptive Rescaling for High-Resolution Structural Visual Inspection.

Developments in drones and imaging hardware technology have opened up countless possibilities for enhancing structural condition assessments and visual inspections. However, processing the inspection images requires considerable work hours, leading to delays in the assessment process. This study presents a semantic segmentation architecture that integrates vision transformers with Laplacian pyramid scaling networks, enabling rapid and accurate pixel-level damage detection. Unlike conventional methods that often lose critical details through resampling or cropping high-resolution images, our approach preserves essential inspection-related information such as microcracks and edges using non-uniform image rescaling networks. This innovation allows for detailed damage identification of high-resolution images while significantly reducing the computational demands. Our main contributions in this study are: (1) proposing two rescaling networks that together allow for processing high-resolution images while significantly reducing the computational demands; and (2) proposing Dmg2Former, a low-resolution segmentation network with a Swin Transformer backbone that leverages the saved computational resources to produce detailed visual inspection masks. We validate our method through a series of experiments on publicly available visual inspection datasets, addressing various tasks such as crack detection and material identification. Finally, we examine the computational efficiency of the adaptive rescalers in terms of multiply-accumulate operations and GPU-memory requirements.

Technologies and Platforms for Remote and Autonomous Bridge Inspection – Review

Recent scientific and technological advancements have enabled a more efficient structural condition assessment of bridges, mainly through the implementation of intelligent inspection strategies. These intelligent strategies can provide early identification of critically damaged components before failure, and will therefore play a key role in extending the life of infrastructure. The latest inspection technologies can provide inspection plans with damage conditions, create a damage report as well as provide statistics and comparisons to the previous inspection findings. The new and existing inspection technologies are directed to help with the digitalization of the Bridge Management System (BMS). The complexity of maintenance/inspection requires organized, automated, open and transparent digital processes, which should consider both—structure and asset management data. Further, the inspection/monitoring findings serve as a source for decision-making models. The digitalized aspects of autonomous inspection provide better performance prediction models and guarantee safety for the users. This paper presents the latest findings in the field of remote inspection of bridges. In particular, the main technologies for inspection and geometrical assessment are depicted, especially those based on computer vision systems installed in UAVs and robots, LiDAR, radar, satellites and other non-contact systems including on-board monitoring. The accompanying article entitled: “Methodologies for remote bridge inspection” deals with the methodologies used for data processing based on Artificial Intelligence (AI).

Application of computer vision techniques to damage detection in underwater concrete structures

Underwater concrete structure crack detection and structural health condition assessment based on image processing is a challenging task. The complex underwater environment and severe image degradation seriously affect the accuracy of crack detection. To solve these problems, a monocular vision and image-enhanced fractal-based fractal science based on computer vision and image processing techniques are proposed to carry out a non-contact detection study of underwater concrete cracks. In this study, a four-level structural health condition was established to assist in underwater crack measurement and safety assessment. The box-counting method was used as a practical tool to calculate the fractal dimension. To verify the effective distance of the algorithm, three distances of 0.5 m,0.8 m, and 1.2 m were set. The results show that the method proposed in this study can effectively detect cracks in submerged concrete members within 0.6 m and help managers correctly determine the health of the structure using the fractal dimension.

Monitoring data motivated health condition assessment of cement concrete pavements in field based on FBG sensing technology

Health condition of cement concrete pavements in field is difficult to characterize due to the random action of traffic loads and environmental variations. It is also difficult to accurately get the deformation merely based on theoretical or simulation analysis, for the heterogeneous material and complicated interfacial bonding properties of multilayered concrete pavements. Therefore, in situ health monitoring is particularly significant for structural condition assessment. To contribute this topic, improved fiber Bragg grating sensors have been designed and embedded in the two-layered pavement to measure the strain, deformation, and temperature. Deformation information of the cement concrete pavement under environmental action in about 3 years has been analyzed. Data analysis indicates that the longitudinal and transverse deformations of the pavement have behaved in nonuniform distribution, and local points suffered from the large tensile strains. The vertical measurement data declared that local interfacial micro void defects existed in the soil base and kept stable during the following 3 years. The temperature variation inside the pavement was highly correlated with the environmental temperature, and the variation amplitude was relatively smaller than the environmental temperatures, with the maximum amplitude smaller than 4°C. The load and temperature transfer principles in different layers have also been checked. Generally, the monitoring data in field from the year 2020 to year 2023 have declared that the concrete pavement has good performance to resist the combined action of traffic loads and environmental temperatures. The study can help to understand the actual structural performance of cement concrete pavements in field based on the optical fiber sensing system.

Intelligent segmentation and quantification of tunnel lining cracks via computer vision

Aiming to automatically, precisely, and rapidly detect tunnel lining cracks from images and extract geometric information for structural condition assessment, this study proposes a novel tunnel lining crack segmentation network (TCSegNet) and establishes a framework for calculating key geometric parameters of cracks. A tunnel lining crack segmentation dataset is first built by conducting on-site inspections of metro tunnels and collecting open-sourced tunnel images. Afterward, the TCSegNet, conforming to the encoder–decoder architectural paradigm, is designed to separate cracks from lining images pixel-to-pixel. An improved ConvNeXt and developed efficient atrous spatial pyramid pooling module constitute the encoder. The skip connections, upsampling modules, and tailored segmentation head form the decoder. Upon the segmentation results of TCSegNet, a computing framework integrating multiple digital image processing techniques is proposed to obtain the length, average width, and maximum width of cracks. The experimental results show that the TCSegNet achieves leading results among several dominant models, with 70.78% mean intersection over union (mIoU) and 57.43% F1score. Furthermore, the TCSegNet has 32.01 million parameters, requires 55.13 billion floating point operations, and gets 107.28 frames per second, proving that it has low time and space complexities and implements real-time segmentation. Also, the rationality and effectiveness of TCSegNet in alleviating the crack disjoint problem and preserving crack edge details are verified through comparative experiments. In addition, the TCSegNet achieves 71.99%, 70.45%, and 70.23% mIoU in high-resolution image segmentation, robustness, and generalization tests, respectively, demonstrating that it is competent for detecting high-resolution lining images, has a solid resistance to illumination variations, and can be well generalized to other tunnel lining image datasets. Finally, the applicability of the crack quantification framework is validated by practical application examples. The developed approaches in this study provide pixel-level segmentation results and detailed measurements of concrete lining cracks to assess tunnel structural safety status.

Cost-benefit analysis for SHM systems based on minimum detectable parameter change

Value of information (VoI) analysis can be used to assess the benefit of monitoring the health of infrastructure such as bridges and waterway locks. Such information supports decision-making processes, guiding operators towards the best actions for the infrastructure. In recent studies, VoI analysis is integrated with the modeling of the temporal evolution of structural conditions, so that the value of Structural Health Monitoring (VoSHM) can be assessed over the life cycle of the infrastructure. However, none of these studies considers the ability of the measurement system to detect small changes and its impact on the assessment of the VoI. This paper presents an approach to VoI assessment based on the concept of minimum change detectability and Bayesian linear filters. By exploiting a feature of the Kalman filter whereby posterior covariance is independent of observations, it is possible to predict the minimum shift in the mean value of a structural parameter for which its posterior density function would exceed a user-defined safety threshold. By constructing the decision tree and considering the cost of monitoring and maintenance, the value of detecting smaller changes can be estimated, which is the value of early change detection and performing preventive maintenance. As a proof of concept, this approach is applied to assess the VoSHM for a navigation lock built with precast reinforced concrete elements, where the steel bars in the chamber are damaged by fatigue and corrosion. The VoSHM is compared with the VoI associated to other Non Destructive Tests. The paper then discusses the implications of applying Bayesian linear filters in SHM for parameter change detection, the main one being the development of refined scales for structural condition assessment before any data from the changed state is available. Ultimately, the VoSHM is the benefit brought by adopting these refined scales in maintenance management.

Mitigation of Steel Corrosion Threshold Utilizing Plant-Based Green Corrosion Inhibitors through Electrochemical Techniques

Corrosion of steel reinforcement is a major cause of deterioration in reinforced concrete structures. The corrosion process is influenced by the concrete-steel interface, with the alkaline concrete pore solution initially providing passivation. However, ingress of aggressive substances like chlorides can disrupt the passive layer, initiating active pitting corrosion above a threshold level often taken as 0.4% chloride by cement weight. Once the chloride threshold is exceeded, corrosion propagation depends on oxygen and moisture availability. The resulting rust formation causes expansive cracking and spalling of the concrete cover. Corrosion damage can be mitigated through use of inhibitors like calcium nitrite though high dosages impair concrete strength. Recently, plant-based organic compounds have shown promise as green corrosion inhibitors. The corrosion behavior of steel in concrete can be evaluated through impressed accelerated corrosion testing along with electrochemical techniques like half-cell potential mapping and resistivity measurements. These allow assessment of the probability of corrosion and corrosion rate. Techniques like linear polarization resistance and electrochemical impedance spectroscopy can also quantify instantaneous corrosion rate. Proper structural condition assessment and repair using both conventional and green inhibitors is crucial to control steel corrosion, maintain service life and ensure safety. Further research is needed on green corrosion mitigation methods and advanced non-destructive testing techniques.

Improved bridge modal identification from vibration measurements using a hybrid empirical Fourier decomposition

Bridge health monitoring has been a prominent focus within the global engineering community. Bridge owners, stakeholders, and engineers face the formidable tasks of ensuring efficient monitoring, conducting reliable data analysis, interpreting data logically, and making timely decisions. With the increasing global infrastructure deficit, there is an ever-increasing need to develop reliable and economical bridge monitoring solutions. In this paper, a bridge condition assessment technique is proposed that can utilize the vibration data collected from the instrumented sensors and provide reliable system identification results. The proposed method develops a hybrid approach by integrating the Natural Excitation Technique (NExT) and Empirical Fourier Decomposition (EFD) to analyze ambient bridge vibration data and determine the modal parameters of the bridge. First, NExT is formulated to determine the cross-correlation functions of the bridge measurements, and then EFD is explored to decompose the signals into their monocomponents to identify the bridge modal parameters. The proposed methodology can overcome mode mixing and perform modal identification of a system with closely spaced frequencies and low energy modes. The estimated modal parameters such as bridge frequencies, mode shapes, and damping ratio are used for condition assessment of numerical, experimental and full-scale structures, including a short-span steel bridge located in Ontario, Canada. The results demonstrate that the proposed methodology can provide accurate and robust estimates of bridge modal parameters. Future research is reserved for real-time implementation of the proposed methodology for a wide range of civil structures.

Remote collaborative framework for real-time structural condition assessment using Augmented Reality

Civil structures worldwide are confronted with a growing threat of structural deterioration, aggravated by various factors such as climate change, population growth, and increased traffic. The latent nature of these issues often leads to undetected vulnerabilities until a catastrophic failure occurs, resulting in substantial losses. To address this challenge, there is a critical need for improved structural monitoring, condition assessment, and maintenance practices. Traditional inspection methods, relying on visual estimation and heavy equipment for inaccessible areas, present formidable obstacles to inspectors. These methods impede the safe and fast examination of structural damage, complicating tracking of structural deterioration, and hindering efficient condition assessment. Recognizing these challenges, this paper proposes a remote collaborative framework to enhance the efficiency of structural inspections by leveraging the capabilities of Augmented Reality (AR), QR code, and 5G network. The proposed framework centers on real-time remote collaboration among on-site and off-site inspectors, aiming to elevate safety, accessibility, and overall inspection efficacy. The integration of real-time data sharing and collaboration facilitates immediate decision-making, enabling inspectors to proactively address structural vulnerabilities and prevent potential failures. This study concludes that the proposed framework effectively facilitates real-time structural condition assessment for on-site AR users. Simultaneously, off-site web users can instantly track the progression of data over time through the utilization of 5G technology. The proposed advanced AR framework effectively demonstrates real-time structural condition assessment through a lab-scale experimental beam and a full-scale bridge.

Development of large-scale synthetic 3D point cloud datasets for vision-based bridge structural condition assessment

Visual recognition of 3D point cloud data of bridge inspection scenes is a key step in automating the visual inspection process, which is currently largely manual and inefficient. To alleviate the lack of large-scale annotated point cloud datasets for training such 3D visual recognition algorithms, this research investigates an approach for developing large-scale synthetic point cloud datasets. The proposed approach proceeds in four steps: (1) random generation of different types of bridges in computer graphics environments; (2) sampling of camera trajectories that represent data collection scenarios during bridge inspection; (3) 3D reconstruction using Structure from Motion (SfM) applied to rendered synthetic images; (4) automated annotation of the reconstructed point cloud using ground truth masks obtained with synthetic images. Besides, this research proposes to store point uncertainty information defined by the error between the ground truth depth and the depth calculated from the SfM results. Prior to training, thresholds can be applied to this uncertainty information to control the levels of outliers in the dataset. This research demonstrates the proposed approach by generating point cloud datasets for two data collection scenarios. The effectiveness of the generated datasets is investigated by training 3D semantic segmentation algorithms and evaluating the performance on real and synthetic point cloud data. The proposed approach for point cloud dataset generation will facilitate the development of generalizable and high level-of-detail 3D recognition algorithms toward autonomous bridge inspection.

Exploring the effective implementation of population-based SHM in existing buildings. Part II: Damage-feature classification for decision-making

In the context of structural condition assessment, transfer learning methods overcome some of the difficulties associated with the paucity of information on the actual structural condition of a target structure. This study aims to associate the building’s response with a population of nominally identical buildings for which a form is derived from existing empirical models relating certain basic characteristics (e.g., structure height) with the fundamental resonance period. This paper presents a structural condition classification based on the measured resonance period of the target structure, presented as the Build’Health™ solution. First, damage thresholds are defined by the shift of the fundamental period, which is considered to be a damage-sensitive characteristic for a given building population form, derived from almost thirty published references. The implicit period variation due to certain weather conditions is also included. Multinomial logistic regression and Gaussian mixture models are then used to classify damage according to the performance levels used in earthquake engineering (i.e., Operational Condition, Immediate Occupancy, Life Safety and Collapse Prevention). A performance-based probabilistic framework using a traffic-light system (green-orange-red classification) is finally used to classify structural condition. The method is tested and validated on several buildings surveyed after weak to strong earthquakes with different structural conditions. We show the complementarity of combining transfer learning, which gives the actual state of the target specimen with respect to a nominally identical population form of buildings, with multinomial logistic regressions and Gaussian mixture models for operational condition-based decision-making defined by the measured resonance period. This manuscript is the second in a series aimed at developing the Build’Health™ operational method for assessing the condition of real buildings (Part I on damage detection using transfer learning and Part II on classification using Gaussian mixture models and multinomial logistic regressions) based on basic building information.