Approach For Damage Assessment Research Articles

A new approach for environmental damage assessment pursuant to the European Union Environmental Liability Directive.

The European Union and Member States national laws require competent authorities to promptly and effectively address environmental incidents, noncompliances, and criminal offenses, necessitating thorough planning of investigation and assessment activities. To enhance environmental damage assessments in line with the European Environmental Liability Directive (ELD, 2004/35/EC), the European Union Network for the Implementation and Enforcement of Environmental Law (IMPEL Network) has introduced the Criteria for the Assessment of the Environmental Damage (CAED) framework. This framework, outlined in a Practical Guide, offers a methodological approach to environmental damage assessment (EDA) focusing on three key objectives: case screening, identification of "clues" of damage, and determination of "evidence" of damage. Given the critical importance of structured data collection and evaluation, the CAED project has adopted a Driver, Pressure, State, Impact, and Response (DPSIR) model adapted to environmental damage and developed the Practical Tables. These tables serve as a comprehensive tool for systematically identifying investigative priorities and collecting standardized data and information using a predefined list of qualitative or quantitative indicators. This article provides an overview of the Practical Guide and Practical Tables, collectively referred to as the CAED Toolkit, aiming to establish a common framework for environmental damage assessments among various competent authorities and stakeholders across Europe. Integr Environ Assess Manag 2024;00:1-10. © 2024 SETAC.

Machine learning prediction models for ground motion parameters and seismic damage assessment of buildings at a regional scale

This study examines the feasibility of using a machine learning approach for rapid damage assessment of reinforced concrete (RC) buildings after the earthquake. Since the real-world damaged datasets are lacking, have limited access, or are imbalanced, a simulation dataset is prepared by conducting a nonlinear time history analysis. Different machine learning (ML) models are trained considering the structural parameters and ground motion characteristics to predict the RC building damage into five categories: null, slight, moderate, heavy, and collapse. The random forest classifier (RFC) has achieved a higher prediction accuracy on testing and real-world damaged datasets. The structural parameters can be extracted using different means such as Google Earth, Open Street Map, unmanned aerial vehicles, etc. However, recording the ground motion at a closer distance requires the installation of a dense array of sensors which requires a higher cost. For places with no earthquake recording station/device, it is difficult to have ground motion characteristics. For that different ML-based regressor models are developed utilizing past-earthquake information to predict ground motion parameters such as peak ground acceleration and peak ground velocity. The random forest regressor (RFR) achieved better results than other regression models on testing and validation datasets. Furthermore, compared with the results of similar research works, a better result is obtained using RFC and RFR on validation datasets. In the end, these models are utilized to predict the damage categories of RC buildings at Saitama University and Okubo Danchi, Saitama, Japan after an earthquake. This damage information is crucial for government agencies or decision-makers to respond systematically in post-disaster situations.

Composite encoder–decoder network for rapid bridge damage assessment using long-term monitoring acceleration data

In recent years, the deployment of structural health monitoring (SHM) systems has become paramount for safeguarding critical infrastructures. Notwithstanding, the development of an unsupervised deep learning framework capable of learning from long-term sensor data remains a critical challenge, particularly in accurately assessing the exact damage location. This study addresses this gap by proposing a novel approach for rapid bridge damage assessment. The proposed method employed a deep overcomplete encoder–decoder network (DOEDN) to reconstruct the acceleration data acquired from each sensor node on the bridge. The reconstruction losses generated by the DOEDN framework are then used as damage-sensitive features. Additionally, a damage indicator based on Gaussian processes is introduced to assess the damage location and evaluate its severity. The performance and sensitivity of the proposed DOEDN framework are evaluated through long-term monitoring acceleration data from a numerical highway bridge model and the well-known full-scale Z24 bridge. Furthermore, comparative assessments against the regular deep undercomplete encoder–decoder network are conducted using metrics including mean absolute error, coefficient of determination ( R2), and mean intersection over union. The results show that the proposed DOEDN framework can reasonably assess the damage location and evaluate its severity across various structural scenarios in the bridge, even in the presence of temperature variations, thus providing a practical and effective solution for bridge health monitoring.

Enhancing Seismic Damage Detection and Assessment in Highway Bridge Systems: A Pattern Recognition Approach with Bayesian Optimization.

Highway bridges stand as paramount elements within transportation infrastructure systems. The ability to ensure swift recovery after extreme events, such as earthquakes, is a fundamental trait of resilient communities. Consequently, expediting the recovery process necessitates near real-time diagnosis of structural damage to provide dependable information. In this study, a data-driven approach for damage detection and assessment is investigated, focusing on bridge columns-the pivotal supporting elements of bridge systems-based on simulations derived from nonlinear time history analysis. This research introduces a set of cumulative intensity-based damage features, whose efficacy is demonstrated through unsupervised learning techniques. Leveraging the support vector machine, a prominent pattern recognition algorithm in supervised learning, alongside Bayesian optimization with a Gaussian process, seismic damage detection and assessment are explored. Encouragingly, the methodology yields high estimation accuracies for both binary outcomes (indicating the presence of damage or the occurrence of collapse) and multi-class classifications (indicating the severity of damage). This breakthrough opens avenues for the practical implementation of on-board sensor computing, enabling near real-time damage detection and assessment in bridge structures.

Study on the relationship of design parameters and damage modes for RC slabs subjected to large-scale hard missile impacts base on task-driven approach

This study proposes a novel hybrid ensemble learning (EL) model that integrates CatBoost and SMOTE-NC algorithms to evaluate the damage modes of reinforced concrete (RC) slabs under large-scale hard missile impacts, overcoming the limitations of conventional approaches. To develop the optimization model, a database was constructed using publicly published experimental data from 304 large-scale missile impact tests conducted on RC plates. Furthermore, the SMOTE-NC oversampling algorithm is employed to optimize the database and address the issue of class imbalance. Subsequently, four ensemble machine learning models and empirical formulas are assessed. The findings indicate that the hybrid ensemble learning model combining CatBoost and SMOTE-NC exhibits superior performance, showcasing robustness and efficacy. Furthermore, through the application of SHAP analysis, it was revealed that velocity, thickness, concrete compressive strength, missile mass, reinforcement ratio, and missile diameter are the most influential variables, and they interact with each other in significant ways. Notably, the analysis indicated that once the concrete compressive strength reaches a certain value, further improvements may have a detrimental effect on penetration resistance. Additionally, the impact of yield strength on slab performance becomes more pronounced as the slab thickness decreases. Drawing upon these findings, a proposed approach for damage assessment and protection design utilizing an interpretable hybrid EL model was introduced. However, it is crucial to acknowledge that the applicability of this model is confined to RC targets with finite thickness, specifically engineered to withstand large-scale hard missile impacts.

A time-domain fatigue damage assessment approach for the tripod structure of offshore wind turbines

Welded features in the tubular joints are one of the most severe sources of fatigue failure in supporting structure for offshore wind turbines (OWTs). Therefore, fatigue resistance becomes a vital design, inspection and maintenance criterion for tripod supporting structures. This paper proposes a time-domain fatigue damage assessment approach for tripod structures by establishing a detailed finite element model for the critical zones and simplified beam model for the remaining regions. The stress influence matrix (SIM) is formed once by utilizing multiple finite element analyses. Based on the equivalent static loads and linear superposition principle, the time-variant stress can be efficiently calculated. By comparing the fatigue damage caused by multiple loads to that caused by a single load in a particular direction, we conclude that the advantage of the proposed method over the conventional stress concentration factors method. Afterwards, we investigate the effects of environmental conditions on the cumulative fatigue damage of critical zones. The fatigue resistances of various structures by altering angles between the main barrel and up brace, and a novel structure inspired by topology optimization, are promptly verified. All results indicate that the present approach can determine the fatigue damage for the tripod structure with precision and efficiency.

A Machine Learning Based-Guided Wave Approach for Damage Detection and Assessment in Composite Overwrapped Pressure Vessels

The applications of composite overwrapped pressure vessels (COPVs) in extreme conditions, such as storing hydrogen gases at very high pressure, impose new requirements related to the system's integrity and safety. The development of a structural health monitoring (SHM) system that allows for continuous monitoring of the COPVs provides rich information about the structural integrity of the component. Furthermore, the collected data can be used for different purposes such as increasing the periodic inspection intervals, providing a remaining lifetime prognosis, and also ensuring optimal operating conditions. Ultimately this information can be complementary to the development of the envisioned digital twin of the monitored COPVs. Guided waves (GWs) are preferred to be used in continuous SHM given their ability to travel in complex structures for long distances. However, obtained GW signals are complex and require advanced processing techniques. Machine learning (ML) is increasingly utilized as the main part of the processing pipeline to automatically detect anomalies in the system's integrity. Hence, in this study, we are scrutinizing the potential of using ML to provide continuous monitoring of COPVs based on ultrasonic GW data. Data is collected from a network of sensors consisting of fifteen Piezoelectric (PZT) wafers that were surface mounted on the COPV. Two ML algorithms are used in the automated evaluation procedure (i) a long short-term memory (LSTM) autoencoder for anomaly detection (defects/impact), and (ii) a convolutional neural network (CNN) model for feature extraction and classification of the artificial damage sizes and locations. Additional data augmentation steps are introduced such as modification and addition of random noise to original signals to enhance the model's robustness to uncertainties. Overall, it was shown that the ML algorithms used were able to detect and classify the simulated damage with high accuracy.

Combining remote sensing techniques and field surveys for post-earthquake reconnaissance missions

Remote reconnaissance missions are promising solutions for the assessment of earthquake-induced structural damage and cascading geological hazards. Space-borne remote sensing can complement in-field missions when safety and accessibility concerns limit post-earthquake operations on the ground. However, the implementation of remote sensing techniques in post-disaster missions is limited by the lack of methods that combine different techniques and integrate them with field survey data. This paper presents a new approach for rapid post-earthquake building damage assessment and landslide mapping, based on Synthetic Aperture Radar (SAR) data. The proposed texture-based building damage classification approach exploits very high resolution post-earthquake SAR data integrated with building survey data. For landslide mapping, a backscatter intensity-based landslide detection approach, which also includes the separation between landslides and flooded areas, is combined with optical-based manual inventories. The approach was implemented during the joint Structural Extreme Event Reconnaissance, GeoHazards International and Earthquake Engineering Field Investigation Team mission that followed the 2021 Haiti Earthquake and Tropical Cyclone Grace.

Exploring MapSwipe as a Crowdsourcing Tool for (Rapid) Damage Assessment: The Case of the 2021 Haiti Earthquake

Abstract. Fast and reliable geographic information is vital in disaster management. In the late 2000s, crowdsourcing emerged as a powerful method to provide this information. Base mapping through crowdsourcing is already well-established in relief workflows. However, crowdsourced post-disaster damage assessment is researched but not yet institutionalized. Based on MapSwipe, an established mobile application for crowdsourced base mapping, a damage assessment approach was developed and tested for a case study after the 2021 Haiti earthquake. First, MapSwipe’s damage mapping results are assessed for quality by using a reference dataset in regard to different aggregation methods. Then, the MapSwipe data was compared to an already established rapid damage assessment method by the Copernicus Emergency Management Service (CEMS). Crowdsourced building damage mapping achieved a maximum F1-score of 0.63 in comparison to the reference data set. MapSwipe and CEMS data showed only slight agreement with Cohen’s Kappa values reaching a maximum of 0.16. The results highlight the potential of crowdsourcing damage assessment as well as the importance for a scientific evaluation of the quality of CEMS data. Next steps for further integrating the presented workflow into MapSwipe are discussed.

Developing assessment flow for damage estimation of mud housing typology through a case study against coastal floods

Rising instances of prolonged inundation due to climate-aggravated high tide flooding are economically burdensome for resource-crunched developing nations that bear enormous damage due to loss of built infrastructure (housing in this case). Regardless of the loss, coastal flood impact on buildings is rarely given precedence. The mud building typology in India captures 34% of existing housing demand primarily within ruralIndia; for instance, 75% of the housing in Sagar Island uses mud as the dominant construction material, making it an ideal case for the proposed research. The multivariate nature of damage and empirical data constraint associated with mud buildings propels the development of two unconventional damage assessment approaches using multivariate-probabilistic technique. The proposed literature-based approach uses logical reasoning based on the available scientific evidence whereas the lab-based approach uses the insights from structural analysis of scaled model. The damage matrix created from both the approaches are used to analyse a common flood data (depth & duration) generated using 1000 Montecarlo simulations. The resultant Damage Stage values confirm the versatility of either approach over spatial (local to regional)—temporal (flood character and intensity) dimensions. The lab-based approach proved to be a better alternative considering the availability of continuous records on damage behaviour and precise information on the flood threshold of dominant building material, a crucial component of the multivariate damage assessment process.

Damage assessment using the Lamb wave factorization method

The Lamb wave technique has been widely used for damage detection in plate-like structures. However, for two-dimensional (2D) defects like corrosions or one-dimensional (1D) defects like cracks, conventional Lamb wave-based methodology usually selects the detection algorithm in terms of the damage type, which is restricted if the defect remains unknown. To address the issues, this paper proposes a damage assessment approach based on the Lamb wave factorization method, which is a general method that can adapt to the detection of both 2D and 1D defects. In the process, both the forward-scattered wave across the inspection area and the wave back-scattered from the defect are used for detection, so that the enclosed rich information can be fully exploited. To measure the wave field of defect, a transducer array is firstly set around the inspection area, whose transducers would take turns to excite the Lamb wave while others remain listeners. On the basis, the scattered waves of all directions are then extracted and transformed from the time domain to frequency domain. Under a certain frequency, the inspection area is next visualized by sampling the area and solving the inverse scattering problem using the spectral data at each sampling point. By fusing the results of different frequency together, the final image can be eventually obtained. The method is then numerically investigated and also validated through experiments. The results demonstrate that it can give out the outline description of 2D defect and the size evaluation of 1D defect accurately, thus being an effective tool for damage assessment.

SPECTRAL-ACOUSTIC MONITORING OF DAMAGE IN POLYCRYSTALLINE MATERIALS BASED ON A SIMULATION-MECHANICAL MODEL

In this study, a modified simulation-mechanical model is developed for dispersed damage accumulation in a polycrystalline material based on continuum damage mechanics and physical mesomechanics. The main damaging factors are assumed to be the field of dispersed microcracks in the material and the field of internal stresses arising primarily from linear structural defects. The statistical limit state criterion of a material following from the proposed model is consistent with the known information about the failure conditions of brittle and ductile structural materials. The possible application of the limit state criterion is discussed for typical cases of failure and transition from elastic to elastic-plastic deformation of polycrystalline structural materials. The proposed spectral-acoustic method for material damage assessment is used to analyze elastic pulse propagation in the simulated medium. The existence of some acoustic effects in the damaged medium is substantiated, which can be useful for the instrumental assessment of material damage at any time during loading of a sample or service of a structural element. It is shown that the spectral-acoustic method can provide a basis for a technique of measuring the damage parameters included in the proposed model. Available experimental data confirm the correctness of the damage assessment approach for structural materials. Further experimental studies will develop methods for express monitoring of dispersed damage in metal structures subjected to thermomechanical loads.

Damage detection on antarctic ice shelves using the normalised radon transform

Areas of structural damage mechanically weaken Antarctic ice shelves. This potentially preconditions ice shelves for disintegration and enhanced grounding line retreat. The development of damage and its feedback on marine ice sheet dynamics has been identified as key to future ice shelf stability and sea level contributions from Antarctica. However, it is one of the least understood processes that impact ice shelf instability since quantifying damage efficiently and accurately is a challenging task. Challenges relate to the complex surface of Antarctica, variations in viewing-illumination geometry, snow or cloud cover and variable signal-to-noise levels in satellite imagery. Therefore, automated damage assessment approaches require careful pre- and post-processing, lacking the option to be applied to wider spatiotemporal domains. Simultaneously, studies that use manual mapping are usually limited due to the effort required for extensive mapping, which either results in a limited spatial domain or the use of low resolution data.This study proposes the NormalisEd Radon transform Damage detection (NeRD) method to detect damage features and their orientations from multi-source satellite imagery. NeRD performs robust, high resolution, large-scale damage assessments. NeRD is applied to the ice shelves in the Amundsen Sea Embayment (ASE) and validated with both manually labelled and existing fracture maps. Validation shows that NeRD detects damage with high recall and provides an accurate physical representation of multi-scale damage features and their orientation. Sensitivity analyses indicate NeRD is robust to different resolution parameter settings. NeRD consistently detects damage for different data sources ranging from optical Landsat 7/8 and Sentinel-2 optical to Synthetic Aperture Radar Sentinel-1 data. Therefore, NeRD paves the way for synergistic multi-source damage detection that overcomes remaining limitations from individual sources. Results show varying damage patterns on the ice shelves in the ASE area in austral summer 2020–2021, with most damage located on the Pine Island, Crosson and Thwaites ice shelves. We show a damage increase on the Pine Island ice shelf between 2013–2019, and display advection and rotation of crevasses. The detected damage orientation can provide insight in the type of crevasse opening mode and the development of damage over time. The damage maps produced with NeRD can help evaluate ice sheet models or machine learning approaches, improving our understanding of damage evolution.

Deep long short-term memory (LSTM) networks for ultrasonic-based distributed damage assessment in concrete

This paper presented a comprehensive study on developing a deep learning approach for ultrasonic-based distributed damage assessment in concrete. In particular, two architectures of long short-term memory (LSTM) networks were proposed: (1) a classification model to evaluate the concrete’s damage stage; (2) a regression model to predict the concrete’s absorbed energy ratio. Two input configurations were considered and compared for both architectures: (1) the input was a single signal; (2) the inputs were four signals from four sides of the specimen. A comprehensive experimental study was designed and conducted on ground granulated blast furnace slag-based geopolymer concrete, providing a total number of 1920 ultrasonic signals from different damage stages. Unsupervised k-means clustering based on dynamic time warping (DTW) was implemented to cluster the ultrasonic response signals from the experimental study into five defined damage stages. The proposed LSTM architectures were successfully trained and validated using the experimental dataset. Moreover, the performance of the LSTM models was evaluated in noisy environments. The proposed LSTM models in this study used the time series of response signals for damage assessment. Therefore, the damage-sensitive features were automatically extracted by the LSTM layers. For comparison, a set of linear and nonlinear ultrasonic features were manually extracted from the response signals as damage-sensitive features, and their sensitivity to damage was investigated. Artificial neural networks were implemented to combine the extracted features and perform the same tasks defined for LSTM models. Comparing the two approaches showed that using the time series of ultrasonic response signals as the input of LSTM models outperforms the idea of using the manually extracted features. This study showed that the presented method is efficient, reliable, and promising for nondestructive evaluation of damage in concrete.

Test Data-Informed Nonlinear Finite Element Model Updating and Damage Inference of a Shake-table Tested Bridge Column considering Bond-slip under Multiple Earthquakes

ABSTRACT Nonlinear finite-element model updating (FEMU) is a promising approach for post-event damage assessment of civil structures. This paper conducted FEMU for a full-scale reinforced concrete bridge column tested under a sequence of earthquakes and examined the evolution of seismic damage across different earthquake ground motions. It was found that using experimental data, FEMU can be applied to identify unknown key model parameters (e.g. bond-slip and core concrete parameters) and damage of the columns as represented by the change of the key parameters. In addition, the updated models demonstrated their ability to better predict the system response for future earthquakes.

Residential building flood damage: Insights on processes and implications for risk assessments

AbstractFlood damage assessments provide critical insights on processes controlling building damage and loss. Here, we present a novel damage assessment approach to develop an empirical residential building damage database from five flood events in New Zealand. Object‐level damage data was collected for flood hazard and building characteristics, along with relative building component and sub‐components damage ratios. A Random Forest Model and Spearman's Rank correlation test were applied to analyse damage data variable importance and monotonic relationships. Model and test results reveal flood inundation depth above first finished floor level is highly important and strongly correlated with total building damage ratios while flow velocity is important for structure component damage. Internal finishes components contribute highly to total building damage ratios as higher value sub‐component materials are susceptible to direct damage from water contact and indirect damage during repair. The empirical damage data has several implications for damage model development due to the limited heterogeneity of flood hazard intensities and building attributes observed. Extending empirical damage data with synthetic damage data in future would support development of more representative object‐specific damage models to evaluate direct tangible damages for local contexts.

Elaboration of a damage monitor and assessment approach for in-situ asphalt pavement layer using portable seismic property analyzer (PSPA)

The Portable seismic property analyzer (PSPA) is advanced in detecting the modulus properties of the asphalt layer in the depth profile, in a rapid and non-destructive way. This research aimed to elaborate a damage monitoring and assessment approach for in-situ asphalt pavement layer, based on the PSPA device. Firstly, the procedures for detecting and shifting PSPA moduli of field asphalt layers under different cracking and temperature conditions were developed. Then, the feasibility of using the PSPA modulus for assessing asphalt layer damage was justified based on the comparative analysis of the PSPA modulus, the asphalt mixture's strength, and the mixture's cracking levels. Finally, based on the PSPA measurements, the damage distributions in the depth profile of the pavement were determined, and different damage states of the pavement were evaluated. It was found that the varying trend of PSPA moduli with the changing sub asphalt layers is generally consistent with that of tensile strengths. Similarly, the PSPA-modulus-based indicator (integrity) of the asphalt layer decreases with the cracking level of the pavement, which proves that the PSPA modulus works well for characterizing different strength and cracking states of asphalt layers. As compared with the pavement condition index (PCI), the PSPA-modulus-based indicator (i.e., integrity) is advantageous in describing the damage evolution trend in the pavement before the macro crack appears. Indeed, as the asphalt pavement surface begins to suffer macro cracking, the integrity of the pavement has already dropped to only about 30% of its initial value. The findings of this research provide references for applying the PSPA to assess the damage states of the in-situ asphalt pavement layer.

Structural Damage Assessment Using Multiple-Stage Dynamic Flexibility Analysis

Vibration-based damage assessment technology is a hot topic in aerospace engineering, civil engineering, and mechanical engineering. In this paper, a damage assessment approach using multiple-stage dynamic flexibility analysis is proposed for structural safety monitoring. The proposed method consists of three stages. The content of Stage I is to determine the number of damaged elements in the structure by the rank of dynamic flexibility change. The content of Stage II is to determine damage locations by the minimum rank of flexibility correlation matrices. Finally, the damage extents of those damaged elements are calculated in Stage III. The proposed approach fully uses the filtering ability of matrix rank analysis for data noise. A 27-bar truss structure and a steel frame structure are used as the numerical and experimental examples to demonstrate the proposed method, respectively. From the numerical and experimental results, it is found that structure damages can be successfully identified through the multiple-stage dynamic flexibility analysis. By comparative study, the proposed method has more powerful antinoise ability and higher calculation accuracy than the generalized flexibility method. The proposed method may be a promising tool for structural damage assessment.

Assessment of damage in composites using static component generation of ultrasonic guided waves

Static component (SC) generation of guided waves (GWs), which combines the high sensitivity of acoustic nonlinearity to micro-damage and low attenuative effect, has great potential for damage assessment in large composite structures. The present work explores the use of SC generation of GWs for assessing damages in carbon fiber reinforced polymer (CFRP) composite laminates. The features including mode, waveform, and cumulative effect of the generated SC in composites are numerically investigated by three-dimensional finite element modeling and simulation. A dynamic displacement measurement method based on piezoelectric transducers is accordingly proposed and experimentally verified. The cumulative SC pulse generated from primary GW tone-burst with a finite duration, is observed and verified numerically and experimentally. It is found that the magnitude of the generated SC pulse is linearly proportional to the quadratic material nonlinearity. Experimental results demonstrate that the generated SC pulse of GW under group velocity matching condition, is an effective means to assess the hygrothermal damage and low-velocity impact damage in CFRP composite plates. The performed experimental examination validates the feasibility of the proposed approach for damage assessment in CFRP composites.

Nonlinear assessment of “breathing” fatigue crack using steady-state vibration responses

Abstract Fatigue cracks can occur in structural components under long-term cyclic loads. Such fatigue cracks can open and close like "breathing" under harmonic excitations. Fatigue cracks can develop and jeopardize safety of structures. Therefore, there is a great need to assess fatigue cracks especially in their early stages. Higher harmonics have been widely used as crack indicators, however, slight opening-closing motion can lead to weak higher harmonics with much less amplitudes compared to the first harmonics. In these situations, higher harmonics can be barely identified for crack detection. Addressing this problem, this study develops the damage assessment approach based on singular spectrum analysis to distinguish crack-induced nonlinearities in steady-state vibration responses, whereby nonlinear assessment of the crack can be achieved. The capability of the approach is experimentally validated on a steel beam with a “breathing” fatigue crack, steady-state velocity responses of which are acquired through non- contact laser measurement using a Doppler laser vibrometer.