Increasing Damage Severity Research Articles

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

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

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

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

VIBRATION-BASED DAMAGE DETECTION FOR ONE-STORY STEEL FRAME STRUCTURE USING MODE SHAPE CURVATURE

Structural health monitoring techniques, particularly vibration-based damage detection, have gained significance in assessing civil structure condition. This paper focuses on utilising mode shape curvature for damage detection in a one-story steel frame structure. The study aims to overcome traditional inspection limitations by exploring vibration-based approaches. Experimental investigation is conducted to analyse intact and damaged structural modal behaviour. Modal analysis technique extracts modal frequencies and mode shapes, enabling analysis of mode shape curvature for damage detection and localisation. Preliminary findings show that damaged structures display deviations in mode shapes and reduced natural frequencies, providing evidence of structural damage. However, a significant issue arises near the support, where unexpected patterns emerge in the Total Damage Index (TDI) with increasing damage severity. This finding challenges the expected correlation between severity levels and TDI values, highlighting the need to consider factors like fixed supports. Misleading signs of damage in some segments underscore the importance of cautious result interpretation and accounting for noise. Future studies should focus on noise resistance, false indication mitigation, and understanding segments with fixed supports to enhance mode shape curvature analysis’s reliability for damage detection in civil structures.

Feasibility study on a full‐scale wind turbine blade monitoring campaign: Comparing performance and robustness of features extracted from medium‐frequency active vibrations

AbstractThe present work investigates the performance of different features, extracted from vibration‐based data, for structural health monitoring of a 52‐meter wind turbine blade during fatigue testing. An active vibration monitoring system was used during the test campaign, providing periodic excitation of single frequencies in the medium‐frequency range, and using accelerometers to measure the vibration output on different parts of the blade. Based on previous work from the authors, data is available for the wind turbine blade in healthy state, with a manually induced damage, and with progressively increasing damage severity. Using the vibration data, different signal processing methods are used to extract damage‐sensitive features. Time series methods and time‐frequency domain methods are used to quantify the applied active vibration signal. Using outlier analysis, the health state of the blade is classified, and the classification accuracy through use of the different features is compared. Highest performance is generally obtained by auto‐regressive modeling of the vibration outputs, using the auto‐regressive parameters as features. Finally, suggestions for future improvements of the present method toward practical implementation are given.

Monitoring Damage Caused by Pantana phyllostachysae Chao to Moso Bamboo Forests Using Sentinel-1 and Sentinel-2 Images

Pantana phyllostachysae Chao (PPC) is one of the deadliest defoliators of Moso bamboo. Accurately locating and evaluating PPC damage is essential for the management of bamboo forests. Moso bamboo has a unique biennial growth cycle, consisting of the on-year period (bamboo shoots are incubated and then produced) and the off-year period (old leaves are dropped and then new leaves are grown, and no bamboo shoots are produced in the coming year). The similar physiological characteristics of off-year bamboo and damaged on-year bamboo create difficulties in monitoring PPC damage using remote sensing data. In this study, we synergistically used Sentinel-1, Sentinel-2, and field inventory data to construct machine learning (extreme gradient boosting, XGBoost) models monitoring PPC damage. The results show that the single-time observation feature-based model (using images from October) outperformed the double-time observation feature-based model (using the differences between remote sensing signals from October and February or April) due to the interference from other disturbance agents (e.g., logging and weeding). The overall accuracy (OA) values of the single-time observation feature-based model were at least 3% and 10% higher than those for double-time observation feature-based models for on- and off-year samples, respectively. With the consideration of the on- and off-year phenological differences, OA was improved by over 4%. The model without differentiation of the phenological difference tended to underestimate the damaged area of on-year bamboo and overestimate that of off-year bamboo. We also found that the responses of optical and SAR (synthetic aperture radar) features to PPC damage were different. The optical features increased or decreased with increasing damage severity. SAR features decreased significantly at the initial stage of PPC damage and then changed marginally with the increase in damage severity. The addition of SAR features to optical features improved the model performance, mainly for healthy and mildly damaged samples. The methodology developed in this study provides technical and theoretical support for the pest monitoring of bamboo forests using remote sensing data.

Fragility assessment for new and deteriorated portal framed industrial buildings subjected to tropical cyclone winds

Low-rise portal framed industrial buildings are widely used as warehouses, workshops and storage facilities. Damage surveys following severe tropical cyclones have shown that both structural and non-structural damage can occur to these buildings. While non-structural damage to the building envelope of portal framed industrial buildings are considered by several existing fragility models, almost none exist that include structural failures in the wind fragility assessment. This study develops a fragility assessment method for portal framed industrial buildings that accounts for wind-induced damage to both structural (steel portal frame and end wall frame) and non-structural (metal cladding system and fenestration) building components. Fragility curves are developed for a prototype industrial building located on the cyclone-prone east coast of Australia considering four damage states with increasing damage severity. A Monte Carlo simulation incorporating probabilistic models of the spatio-temporal variable wind pressures, wind-induced demands and component capacities is employed to conduct the fragility assessment for both individual building components and the building system under critical failure modes. It was found that the fragility of structural framing is non-negligible when wind speed exceeds 80% of the design level. Structural framing failure is a major contributor to the most severe damage state and is a necessary inclusion in the fragility assessment for this type of building. A sensitivity analysis also showed that fastener corrosion can significantly increase the fragility of deteriorated buildings near to coastlines, particularly for low levels of damage.

Nonlinear structural damage detection using output‐only Volterra series model

Volterra series is a promising technique with great potential for nonlinear system identification. The conventional Volterra series model computes the output responses by performing multiple convolutions between the input excitation and Volterra kernels function. However, the difficulty in acquiring the excitation forces of civil engineering structures under operating conditions greatly limits the application of using Volterra series-based method for system identification. This paper proposes an output-only-based approach using Volterra series model for nonlinear structural damage detection, by quantifying the nonlinear behavior of structures without the prior knowledge of external excitations. The proposed approach uses the structural responses measured at two different locations to identify the kernel function parameters and evaluate the contribution of nonlinear components in the measured responses. The ratio between the standard deviation of the nonlinear components and that of the overall structural response is adopted as damage-sensitive index to quantify the contributions from these two adjacent sensors for performing nonlinear structural damage detection. Numerical studies on a beam structure with a breathing crack under different levels of white noise excitations and experimental studies on a precast segmental concrete column subjected to ground motions with different peak ground acceleration (PGA) values are conducted to validate the capability and accuracy of using the proposed approach for nonlinear structural damage detection. The results demonstrate that the proposed approach is capable of performing nonlinearity quantification effectively and locating structural nonlinear damage. The increasing damage index value can also be used to register the increasing damage severity.

A Variable Data Fusion Approach for Electromechanical Impedance-Based Damage Detection.

There is continuing research in the area of structural health monitoring (SHM) as it may allow a reduction in maintenance costs as well as lifetime extension. The search for a low-cost health monitoring system that is able to detect small levels of damage is still on-going. The present study is one more step in this direction. This paper describes a data fusion technique by combining the information for robust damage detection using the electromechanical impedance (EMI) method. The EMI method is commonly used for damage detection due to its sensitivity to low levels of damage. In this paper, the information of resistance (R) and conductance (G) is studied in a selected frequency band and a novel data fusion approach is proposed. A novel fused parameter (F) is developed by combining the information from G and R. The difference in the new metric under different damage conditions is then quantified using established indices such as the root mean square deviation (RMSD) index, mean absolute percentage deviation (MAPD), and root mean square deviation using k-th state as the reference (RMSDk). The paper presents an application of the new metric for detection of damage in three structures, namely, a thin aluminum (Al) plate with increasing damage severity (simulated with a drilled hole of increasing size), a glass fiber reinforced polymer (GFRP) composite beam with increasing delamination and another GFRP plate with impact-induced damage scenarios. Based on the experimental results, it is apparent that the variable F increases the robustness of the damage detection as compared to the quantities R and G.

Software development firmware system for broken rotor bar detection and diagnosis of induction motor through current signature analysis

The induction motors (IMs) are undoubtedly the most used machines in industries because of the advantages they offer such as simplicity, service continuity and low cost. Due to wear and tear, the motor suffers different types of mechanical and electrical failures. Depending on the criticality of the plant motors, it could be necessary to implement predictive techniques in order to detect the faults before they can cause unnecessary downtime. Therefore, in this paper, the research approach was to develop a low cost measurement system based on a micro controller platform for machine diagnosis. The FRDM K64F developing board was selected as the most suitable for satisfying the system conditions, and it was used to collect induction motor`s current data. In order to validate the accuracy of the developed system, the Frequency Transfer Functions (FRF) of the developed measurement system and the standard system (NI USB-6009) were compare. It showed a flat frequency spectrum from 0 to 1 KHz, with small fluctuations of about 0.25 dB standard deviation. A fully automated test bench was implemented, which allows to perform all the measurement tests with the IMs, and in this case, the detection and diagnosis of broken bars. Around 240 tests were performed with varying loads, different rotation speeds, and with different severity damage levels in the rotor. The data analysis procedure for broken rotor bar detection and motor diagnosis was performed by the Motor Current Signature Analysis (MCSA), FFT and Enveloped Analysis (EA). Finally, the research approach was successfully accomplished, by the team by developing a software firmware measurement ultra-low cost development platform for machine diagnosis. It was also developed a proper antialiasing filter to reduce industrial noise. The effectiveness of the proposed system is detecting a weak fault in a noise signal. It was found out a new consistent and robust parameter called the pole pass frequency (fpsf), which could be used as a diagnosis parameter for detection of broken rotor bars faults, with their damage severity degree. The detected parameter can be found around 2.6 Hz, and it increases in amplitude with increasing damage severity.

The influence of childhood nature experience on attitudes and tolerance towards problem-causing animals in Singapore

Low vegetation cover in cities result in urbanites generally receiving less exposure to nature compared to people living in rural areas. Consequently, childhood experiences in a city tend to be less nature-oriented, leading to a detachment from nature in adulthood. However, some cities may have pockets of green spaces that harbour wildlife, and interactions between people and the wildlife around them may have an influence on wildlife conservation attitudes. To investigate the relationships between childhood nature experience and attitudes towards wildlife, we carried out a survey on 1004 Singapore residents about their attitudes and tolerance towards three types of wildlife commonly encountered in Singapore. Structured equation models (SEMs) were used to model the relationship between childhood experience, attitudes towards wildlife, and tolerance levels in three scenarios of increasing damage severity to humans. We found that most respondents had low childhood nature experience, and had neutral/negative attitudes towards all three types of wildlife. Childhood experience was the strongest predictor of wildlife attitude, which varied with age, gender, education level and type of wildlife. Attitude towards wildlife was the strongest predictor of tolerance in all scenarios, while tolerance decreased with increasing severity of damage. Our findings point to the importance of childhood nature experience in shaping adult perceptions of wildlife and their willingness to coexist with wildlife. Given that Singapore is continually developing on forested land for residential and commercial purposes, wildlife encounters are predicted to increase in the future. With proper planning and education, residents near wildlife habitats can learn to live with and appreciate the wildlife around them.

Monitoring Hydrological Processes for Land and Water Resources Management in a Mediterranean Ecosystem: The Alento River Catchment Observatory

Core Ideas Advanced sensor networks gather dense information on hydro‐meteorological variables. The Alento CZO helps assess the impact of climate and land‐use change on ecosystem services. Our research supports effective supply‐side and demand‐side adaptation strategies. In recent years, the critical zone (CZ) of catchments across the Mediterranean region has been influenced by rapid changes in both climate seasonality and land use–land cover. Rural ecosystems in southern Europe are experiencing prolonged droughts, seriously compromising water resource availability and crop yields while increasing the risk of wildfire occurrence. Rainy seasons are likely to be characterized by intense storms that trigger floods, resulting in increasing damage severity. The negative effects of anthropogenic disturbance on hydrological ecosystem services can be tempered by demand‐side adaptation options and appropriate investments to ensure water supply under drought conditions. To shed light on some of the scientific challenges related to these issues, a critical zone observatory (CZO) has been established in the Alento River catchment. Although sampling campaigns and monitoring investigations have been performed in this area for >25 yr, a more systematic research program was recently started to take comprehensive measurements in representative subcatchments of the study area. These sites are instrumented with advanced ground‐based sensor network platforms that provide hydro‐meteorological variables and fluxes in the groundwater–soil–vegetation–atmosphere system. Hydrological models of different complexity exploit the dense information gathered to assess the impact of land use and climate changes on key functions and services of the CZO in the Alento River catchment.

Physiological responses to folivory and phytopathogens in a riparian tree, Brabejum stellatifolium, native to the fynbos biome of South Africa

AbstractThe canopies of many tree species sustain a large diversity of folivorous arthropods and phytopathogenic fungi. These organisms are thought to influence overall tree and stand productivity. Leaf diseases caused by Phyllosticta owaniana and Periconiella velutina, phytopathogenic fungi commonly found on the native riparian tree Brabejum stellatifolium (wild almond), like any other leaf disease, can potentially reduce a plant's photosynthetic efficiency. In addition to these two phytopathogens, the weevils Setapion provinciale and Setapion quantillum are abundant in wild almond canopies. Despite their pervasive occurrence, the impacts of these phytopathogens and arthropods on host tree leaf physiology have not been examined. The gas exchange response of wild almond leaves to phytopathogens and folivore damage was assessed. Leaf nitrogen, phosphorus and water content were also determined. Declines in photosynthetic rates and other physiological parameters were associated with increasing damage severity by weevils and phytopathogens in leaves of B. stellatifolium. Nitrogen and phosphorus contents were negatively associated with disease severity. Water and phosphorus contents were also negatively correlated with increased weevil damage, while nitrogen content was positively correlated with it. The observed responses of B. stellatifolium metabolic functioning to fungal pathogen and folivory indicate a possibility of suppressed wild populations of wild almond.

The role of the pericardium in the valveless, tubular heart of the tunicate Ciona savignyi.

Tunicates, small invertebrates within the phylum Chordata, possess a robust tubular heart which pumps blood through their open circulatory systems without the use of valves. This heart consists of two major components: the tubular myocardium, a flexible layer of myocardial cells that actively contracts to drive fluid down the length of the tube; and the pericardium, a stiff, outer layer of cells that surrounds the myocardium and creates a fluid-filled space between the myocardium and the pericardium. We investigated the role of the pericardium through in vivo manipulations on tunicate hearts and computational simulations of the myocardium and pericardium using the immersed boundary method. Experimental manipulations reveal that damage to the pericardium results in aneurysm-like bulging of the myocardium and major reductions in the net blood flow and percentage closure of the heart's lumen during contraction. In addition, varying the pericardium-to-myocardium (PM) diameter ratio by increasing damage severity was positively correlated with peak dye flow in the heart. Computational simulations mirror the results of varying the PM ratio experimentally. Reducing the stiffness of the myocardium in the simulations reduced mean blood flow only for simulations without a pericardium. These results indicate that the pericardium has the ability to functionally increase the stiffness of the myocardium and limit myocardial aneurysms. The pericardium's function is likely to enhance flow through the highly resistive circulatory system by acting as a support structure in the absence of connective tissue within the myocardium.

A strain energy-based damage severity correction factor method for damage identification in plate-type structures

A strain energy-based damage identification method for plate-type structures is presented. The concepts of a damage location factor (DLF) matrix and a damage severity correction factor (DSCF) matrix, which can be derived from the elemental modal strain energy, are proposed. The damage identification method using the DLF and DSCF is developed for damage localization and quantification in plate-type structures. The method consists of three steps: sensitive mode selection, damage localization, and damage quantification. The proposed method is a response-based damage identification technique which requires the modal frequencies and curvature mode shapes before and after damage. Numerical study demonstrates its viability to correctly detect the damage, approximate the damage area, and estimate the damage severity under high measurement noise and low damage severity conditions. The possibility of damage identification using the partial modal strain energy from the modal strain/curvature mode shape in one direction of plate is also explored based on the numerically simulated data. The method is further implemented on the experimental modal testing data to identify damage at three stages of increasing damage severity on a fiber-reinforced plastic (FRP) sandwich deck panel using a surface-bonded PVDF sensor array. The present DSCF-based damage identification method, as demonstrated in this study, can be used as a viable and effective technique for damage localization and quantification of plate-type structures.

Impact Damage Detection and Assessment in Composite Panels using Macro Fibre Composites Transducers

Structural health monitoring of composite components is vital to the use of these materials in aerospace applications. Being highly susceptible to impact damage, composite materials can sustain internal damage that is very difficult to detect externally. This paper explores the use of macro fibre composite (MFC) transducers not only to detect acoustic emission (AE) released during the impact event but also as pulse/receivers to further quantify damage. To assess the dual functionality of these transducers, two were adhered to a composite panel and subjected to a number of impacts. A commercial AE system recorded signals during the impact event. After each impact, signals were pulsed and received between the two transducers and a C-scan inspection of the panel was conducted to assess the extent of the damage. Post test analysis demonstrated a clear correlation between the AE signal energy and the increase in delamination size. Furthermore, a cross correlation technique was utilised to compare signals in the undamaged plate with those at varying damage levels. The results demonstrated a decreasing correlation of signals with increasing damage severity. The results have shown that MFC transducers offer a real possibility for identifying and sizing impact damage in composite structures.

Flood risk modelling based on tangible and intangible urban flood damage quantification

The usual way to quantify flood damage is by application stage-damage functions. Urban flood incidents in flat areas mostly result in intangible damages like traffic disturbance and inconvenience for pedestrians caused by pools at building entrances, on sidewalks and parking spaces. Stage-damage functions are not well suited to quantify damage for these floods. This paper presents an alternative method to quantify flood damage that uses data from a municipal call centre. The data cover a period of 10 years and contain detailed information on consequences of urban flood incidents. Call data are linked to individual flood incidents and then assigned to specific damage classes. The results are used to draw risk curves for a range of flood incidents of increasing damage severity. Risk curves for aggregated groups of damage classes show that total flood risk related to traffic disturbance is larger than risk of damage to private properties, which in turn is larger than flood risk related to human health. Risk curves for detailed damage classes show how distinctions can be made between flood risks related to many types of occupational use in urban areas. This information can be used to support prioritisation of actions for flood risk reduction. Since call data directly convey how citizens are affected by urban flood incidents, they provide valuable information that complements flood risk analysis based on hydraulic models.

Damage Identification Based on Dead Load Redistribution: Methodology

A new method for damage identification in large, massive civil structures is presented, which is based on the idea that dead load is redistributed when damage occurs in the structure. The method uses static strain measurements due to dead load only as input to the identification procedure. An analytical model of a fixed-fixed beam is developed in which the damage is represented by a section of reduced flexural rigidity. The damage state is determined by the location, length, and severity of the stiffness reduction. A forward analysis of the beam response is first presented to illustrate how the dead load is redistributed for different damage scenarios. The inverse problem is defined by a constrained optimization problem and is solved using a genetic algorithm. The proposed method correctly identified damage in the beam for a wide range of locations and damage severities. The identification procedure, in general, has a greater degree of success with increasing damage severity. Results show that damage is difficult to identify when it is close to the inflection point of the undamaged beam, where the dead load strain is zero. The effect of measurement noise on the ability to identify damage is investigated in the companion paper.

Factors Influencing the Ultrasonic Stress Wave Factor Evaluation of Composite Material Structures

To demonstrate that the finite-element model can be used to investigate some of the factors influencing the ultrasonic stress wave evaluation of materials, a hypothetical case was studied in which classical vibration theory was used. Vibration analysis and experiments for the undamaged case were conducted on an isotropic aluminum plate and a unidirectional graphite-epoxy plate, using a point source to excite the plates. The finite-element solution correlated within eight percent with the exact method. The frequencies predicted by the finite-element model were observed in the experiments in both plates, although in the composite plate, additional frequencies were observed which could not be accounted for. Damaged isotropic plates were also considered. The effects of increasing damage severity with constant damage area, and increasing damage area with constant severity on the resonant frequencies were analyzed.