Beam Damage Research Articles

The Potential of Subsampling and Inpainting for Fast Low-Dose Cryo FIB-SEM Imaging.

Traditional image acquisition for cryo focused ion-beam scanning electron microscopy (FIB-SEM) tomography often sees thousands of images being captured over a period of many hours, with immense data sets being produced. When imaging beam sensitive materials, these images are often compromised by additional constraints related to beam damage and the devitrification of the material during imaging, which renders data acquisition both costly and unreliable. Subsampling and inpainting are proposed as solutions for both of these aspects, allowing fast and low-dose imaging to take place in the Focused ion-beam scanning electron microscopy FIB-SEM without an appreciable loss in image quality. In this work, experimental data are presented which validate subsampling and inpainting as a useful tool for convenient and reliable data acquisition in a FIB-SEM, with new methods of handling three-dimensional data being employed in the context of dictionary learning and inpainting algorithms using a newly developed microscope control software and data recovery algorithm.

Monitoring damage of concrete beams via self-sensing cement mortar coating with carbon nanotube-nano carbon black composite fillers

Self-sensing concrete used in coating form for structural health monitoring of concrete structures has the merits of cost-effectiveness, offering protective effect on structural components, enabling electrical measurements unaffected by steel reinforcement and is also convenient to maintain and replace. This paper investigates the feasibility of using self-sensing cement mortar coating containing carbon nanotube-nano carbon black (CNT-NCB) composite fillers (CNCFs) for damage monitoring of concrete beams. The self-sensing cement mortar coated to concrete beams demonstrated outstanding electrical conductivity (resistivity ranging from 18 to 85 Ω·cm). Under monotonic flexural loadings, self-sensing cement mortar coating with 1.8 vol.% CNCFs featured sensitive self-sensing performance in terms of capturing the initiation of vertical cracks at pure bending span of concrete beams, with fractional change in resistivity (FCR) reaching up to 60.6%. Moreover, FCR variations of self-sensing cement mortar coating exhibited good synchronization and stability with the variation of mid-span deflections of concrete beams during cyclic flexural loadings irrespective of the contents of CNCFs and cyclic amplitudes. Remarkably, it was found that FCR of cement mortar coating basically showed a progressive upward tendency, representing irreversible increase in the resistance during cyclic loading. The irreversible residual FCR indicated the crack occurrence and damage accumulation of concrete beams.

Identification of damages in a concrete beam: a modal analysis based method

Structural Health monitoring (SHM) strategies can play a pivotal role in the perspective of enhancing structures and infrastructures life cycle and maintenance operations. A plethora of sensors and technologies can be employed in this field; in a seismic context, vibrational tests are particularly relevant, being able to give an insight on the dynamic characteristics of the structure itself. In particular, modal parameters can be considered in order to detect damages. A comparison between a certain test time and 0 test time (i.e., undamaged structure) is commonly performed; to this aim numerical models result particularly useful to provide baseline data (often unavailable in pre-existing structures), but they need to be validated before use. Non-contact techniques, like scanning laser Doppler vibrometry, can be exploited to do this. In this paper a numerical model of a scaled concrete beam is realized and validated through LDV data, then it is used to design load tests for progressive damages generation. Modal analysis is conducted after different load trials to evaluate changes of modal parameters in relation to the damage occurred; also, damage-related indices are proposed. The results confirmed the suitability of LDV for dynamic analyses of cement-based structures and this can be particularly useful when big structures (e.g., bridges) have to be monitored in-field. The numerical model was validated with acceptable absolute errors in terms of natural frequencies (between 26 Hz and 131 Hz) and high Modal Assurance Criterion (MAC) values (0.85-0.93). Moreover, the proposed methodology allows to detect damages also in a concise way through synthetic indices (with changes >50% in damaged vs undamaged conditions) and early warnings could be generated according to their values, hence supporting decision-making procedures in the building management scenario.

Digital twins and deep learning segmentation of defects in monolayer MX2 phases

Developing methods to understand and control defect formation in nanomaterials offers a promising route for materials discovery. Monolayer MX2 phases represent a particularly compelling case for defect engineering of nanomaterials due to the large variability in their physical properties as different defects are introduced into their structure. However, effective identification and quantification of defects remain a challenge even as high-throughput scanning transmission electron microscopy methods improve. This study highlights the benefits of employing first principles calculations to produce digital twins for training deep learning segmentation models for defect identification in monolayer MX2 phases. Around 600 defect structures were obtained using density functional theory calculations, with each monolayer MX2 structure being subjected to multislice simulations for the purpose of generating the digital twins. Several deep learning segmentation architectures were trained on this dataset, and their performances evaluated under a variety of conditions such as recognizing defects in the presence of unidentified impurities, beam damage, grain boundaries, and with reduced image quality from low electron doses. This digital twin approach allows benchmarking different deep learning architectures on a theory dataset, which enables the study of defect classification under a broad array of finely controlled conditions. It thus opens the door to resolving the underpinning physical reasons for model shortcomings and potentially chart paths forward for automated discovery of materials defect phases in experiments.

Integrating Image Processing and Machine Learning for the Non-Destructive Assessment of RC Beams Damage

Non-destructive testing (NDT) is a crucial method for detecting damages in concrete structures. Structural damage can lead to functional changes, necessitating a range of damage detection techniques. Non-destructive methods enable the pinpointing of the location of the damage without causing harm to the structure, thus saving both time and money. Damaged structures exhibit alterations in their static and dynamic properties, primarily stemming from a reduction in stiffness. Monitoring these changes allows for the determination of the failure location and severity, facilitating timely repairs and reinforcement before further deterioration occurs. A systematic approach to damage detection and assessment is pivotal for fortifying structures and preventing structural collapse, which can result in both financial and human losses. In this study, we employ image processing to categorize damaged beams based on their crack growth and propagation patterns. We also utilize support vector machine (SVM) and k-nearest neighbor (KNN) methods to detect the type, location, and extent of failures in reinforced concrete beams. To provide context and relevance for the laboratory specimens, we will compare our findings to the results from controlled experiments in a controlled laboratory setting.

Beam damage and dynamics modelled with equivariant neural networks

Beam damage and dynamics modelled with equivariant neural networks

Identification of Damage in Steel‒Concrete Composite Beams Based on Wavelet Analysis and Deep Learning

Identification of Damage in Steel‒Concrete Composite Beams Based on Wavelet Analysis and Deep Learning

Mitigation of beam damage on MoS2 using electrostatic beam blanking in TEM

Mitigation of beam damage on MoS2 using electrostatic beam blanking in TEM

X-ray dose effects and strategies to mitigate beam damage in metal halide perovskites under high brilliance X-ray photon sources

X-ray dose effects and strategies to mitigate beam damage in metal halide perovskites under high brilliance X-ray photon sources

Synthesis technique and electron beam damage study of nanometer-thin single-crystalline thymine.

Samples suitable for electron diffraction studies must satisfy certain characteristics such as having a thickness in the range of 10-100 nm. We report, to our knowledge, the first successful synthesis technique of nanometer-thin sheets of single-crystalline thymine suitable for electron diffraction and spectroscopy studies. This development provides a well-defined system to explore issues related to UV photochemistry of DNA and high intrinsic stability essential to maintaining integrity of genetic information. The crystals are grown using the evaporation technique, and the nanometer-thin sheets are obtained via microtoming. The sample is characterized via x-ray diffraction and is subsequently studied using electron diffraction via a transmission electron microscope. Thymine is found to be more radiation resistant than similar molecular moieties (e.g., carbamazepine) by a factor of 5. This raises interesting questions about the role of the fast relaxation processes of electron scattering-induced excited states, extending the concept of radiation hardening beyond photoexcited states. The high stability of thymine in particular opens the door for further studies of these ultrafast relaxation processes giving rise to the high stability of DNA to UV radiation.

Probing chemical pathways in polymer membranes despite electron beam damage

Probing chemical pathways in polymer membranes despite electron beam damage

Analytical model for predicting localized damage in RC beams under contact explosion

The determination of damage to reinforced concrete (RC) beams when buildings are exposed to explosives is critical for assessing building destruction and overall stability. However, a well-established theoretical framework for determining the damage of RC beams under contact explosion is currently lacking. In this study, we developed an analytical model to determine the size and shape of the local damage zone in the RC beam subjected to contact explosion. The model analyzes the evolution of damage in the RC beam, considering the function of steel reinforcement and calculating the damage zone. Experimental investigations were conducted using various weights of explosives to study the damage caused by contact explosions on RC beams. By comparing the model with the experimental results, the average error of the analysis model was found to be 9.0 %, providing an accurate calculation of local damage in RC beams and an effective reflection of the dynamic response process. Consequently, these research results can be further used to assess the residual load-bearing capacity of damaged RC beams and predict the extent of damage.

Evaluation of BFRP strengthening and repairing effects on concrete beams using DIC and YOLO-v5 object detection algorithm

In this study the effectiveness of using Basalt Fiber Reinforced Polymer (BFRP) for strengthening and repairing concrete beams after pre-damage was investigated. Four-point bending tests were conducted on concrete beams, and their strain nephograms were analyzed using 3D-DIC (Digital Image Correlation) to assess the BFRP reinforcement and repair effects on concrete. A damage indicator model based on stress-displacement curves was proposed to quantify concrete damage and evaluate the BFRP strengthening effect. Additionally, the YOLO-v5 object detection algorithm was employed to detect surface damage on concrete specimens, and its results were cross-verified with the damage indicator. The study reveals that BFRP reinforcement primarily affects the concrete beams beyond the elastic limit, enhancing their load-bearing capacity by 37.91% and improving ductility. The proposed damage indicator proves successful in quantifying damage in BFRP-reinforced concrete beams, distinguishing the transition points between elastic, cracking, and failure stages. The YOLO-v5 algorithm accurately detects damage in strain nephograms, providing sensitive and timely identification of concrete damage. Results proves that the offset ratio between the damage identification results of YOLO-v5 and the experimental stress-displacement curve data is between − 3.03% and 8.66%. The proposed damage indicator and YOLO-v5 detection offer valuable tools for evaluating damage and monitoring the health of concrete structures in engineering applications.

Vibration-based structural damage detection strategy using FRFs and machine learning classifiers

In this paper, a damage detection strategy for beam-type structures based on frequency response functions (FRFs) is presented. Five aluminum beams of equal nominal dimensions are used in an experimental program under laboratory conditions to obtain experimental FRF data from both undamaged and damaged conditions. Damages are induced by creating rectangular notches on the beams using saw-cutting. A stochastic finite element model of the beam is developed in MATLAB to construct several training datasets. The damages are modeled by reducing the cross-sectional area at the corresponding damaged elements. Simple damage indexes are proposed as damage-sensitive features. Decision Tree, Support Vector Machines, and Artificial Neural Networks classifiers are trained in the first stage to perform damage detection and localization. Single and multiple damages located in a single zone and more than one zone simultaneously are considered. In the second stage, experimental data not used for training are used for validation. The results from the first stage suggest that the proposed damage indexes can effectively detect and locate structural damage in beams. Among all classifiers, Artificial Neural Networks is the classifier that best performed. High accuracy is achieved to identify the presence of damage (99.3%) and detecting its location on the beam for some of the training datasets (from 80.0% to 97.1%). In the second stage of validation, accuracy, as expected, decreased. However, misclassifications occur mainly for FRF samples in the impact zone, which indicates that the proposed strategy can be efficient to detect damages at locations other than the excitation zone.

A full-range fatigue life prediction model for RC beams strengthened with prestressed CFRP plates accounting for the impact of FRP debonding

To predict the fatigue life of a reinforced concrete (RC) flexural member strengthened with externally bonded prestressed Fiber Reinforced Polymer (FRP) laminate accurately, the interaction between components with different degrees of degradation must be carefully taken into account. Furthermore, as the debonding of FRP plates is a recurrent problem, this has to be incorporated into the prediction model. In this study, a nonlinear analysis method for the entire process of fatigue damage in RC beams strengthened with prestressed Carbon Fiber Reinforced Polymer (CFRP) plates was proposed. In this model, all the components of the strengthened beam had independent fatigue degradation properties and failure criteria, and debonding growth was also explicitly considered. The comparison between the analytical and experimental results were used for model validation and good agreement was found.

Performance assessment of RC frame buildings with open ground storey

Buildings with open ground storey (OGS) evidenced catastrophic failure in past earthquakes. Building code provisions usually: (i) avoid designing such buildings (e.g., ASCE 7–16), (ii) provide structural walls up to 2% plan density (e.g., IS 1893–1) and (iii) design by amplifying the demand at ground storey using modification factor (e.g., Eurocode 8) or reducing the response reduction factor using an irregularity factor (e.g., NTE-E030). Hence applicability of these provisions is checked with a set of 2D RC frame buildings; it includes (i) a simple OGS building (OGS1), (ii) OGS building with structural wall at ground storey, IS 1893–1 (OGS2), (iii) OGS buildings designed as per NTE-E030 (OGS3) and Eurocode 8 (OGS4). Their inelastic behaviour is obtained using nonlinear static and dynamic analysis, including infill walls as an equivalent strut. OGS2 suffered the least (damage in struts), and OGS3 sustained more damage (in struts, beams and columns). It is mandatory to provide structural walls at the ground storey rather than designing for amplified force demands. A parametric study is conducted (for OGS using IS 1893–1): (a) by varying the height of the building, and (b) including structural walls in 1, 3 and 5 bays. Increasing height or providing structural walls in more bays increases damage in struts and no damage in other elements. The response reduction factor (R) for OGS3 is more than OGS1, OGS2 and OGS4, as it shows more damage; it is necessary to avoid damage in OGS buildings. Also, the R-value reduces with building height and increases with structural walls in more bays; thus, a single R-value is not applicable to all buildings.

Vibrational analysis of a coupled damaged Timoshenko beam-arch mechanical system with von Kármán nonlinearities and layer discontinuity

The present study investigates vibrations in the nonlinear regime of a partially elastically connected beam with damage and either a curved or straight beam, focusing on the time domain. The mechanical system under consideration consists of two beams with varying discontinuities: one in an elastic layer and the other in a narrow cross-section area of the beam. To analyze the vibrations in this coupled system, a p-version finite element method is employed. This method allows for the examination of shear deformable beam-beam or beam-arch systems with small or large discontinuities in the elastic layer and cross-section area of the beam. The main contribution of this work lies in the dynamic analysis of a novel coupled geometrically nonlinear mechanical system. It has been discovered that in specific cases of combinations of discontinuities in the upper beam and elastic layer, a different energy transmission occurs, caused by geometrically nonlinear couplings of the nonlinearity of the elastic layer, to the lower arch, resulting in multiple cycles of oscillations executed by the system in the steady-state regime of vibrations. The analysis considers various scenarios, including the effects of the discontinuity in the viscoelastic connection layer, beam damage, and the varying curvature of the lower arch. General mode shapes are determined, and forced damped vibrations in the time domain are analyzed using the Newmark method. The findings from this dynamic analysis have broad applications in engineering and technical practice.

Bending damage and fractal characteristics of steel fiber-reinforced concrete under three-point bending test

Steel fiber-reinforced concrete (SFRC) has outstanding mechanical properties and is widely used in the construction industry. To investigate the damage characteristics of SFRC beams with different steel fiber (SF) contents (0, 15, 30, 45, and 60 kg/m3) accurately, this study analyzed the bending damage (including the load-crack mouth opening displacement curve, flexural–tensile strength, fracture toughness, and microstructure and macrostructure) and fractal characteristics of SFRC beams under three-point bending tests. The crack propagation process in the SFRC beams was monitored by the digital image correlation technique. Information on microcrack evolution and macrocrack development during the loading process was collected using acoustic emission techniques. Furthermore, the correlation dimension reflecting the development state of the cracks was obtained based on the Grassberger–Procaccia algorithm. Test results showed that the performance of concrete beams in terms of deformation and cracking significantly improved by adding SFs. The beam with an SF content of 45 kg/m3 had the highest flexural–tensile strength of 6.979 MPa, which was 1.668 times higher than that of a plain concrete beam. The beam with a 60 kg/m3 SF content had the highest fracture toughness of 1.691 MPam1/2, which was 53.87% higher than that of the plain concrete beam. The fractal analysis results showed that when the correlation dimension increased, numerous small-sized, low-evolution microcracks accumulated in the fracture process zone. When the correlation dimension decreased, the microcracks converged and formed macrocracks. The peak value of the correlation dimension can be used as precursor information for SFRC beam fracture prediction.

Important aspects of investigating optical excitations in semiconductors using a scanning transmission electron microscope.

Since semiconductor structures are becoming smaller and smaller, the examination methods must also take this development into account. Optical methods have long reached their limits here, but small dimensions are also a challenge for electron beam techniques, especially when it comes to determining optical properties. In this paper, electron microscopic methods of investigating optical properties are discussed. Special attention is given to the physical limits and how to deal with them. We will cover electron energy loss spectrometry as well as cathodoluminescence spectrometry. We pay special attention to inelastic delocalisation, radiation damage, the Čerenkov effect, interference effects of optical excitations and higher diffraction orders on a grating analyser for the cathodoluminescence signal.

Detection and evaluation of heat damage in reinforced concrete beams using linear and nonlinear guided waves

This study utilizes the linear and nonlinear features of guided waves (GWs) for detecting and evaluating heat damage in reinforced concrete (RC) beams. The RC beams with embedded sensors attached at rebar ends are experimentally studied using longitudinal GW at 200 kHz after heating the specimens in a furnace from 100°C to 300°C. For the studies investigating the effect of heat damage on the RC beams beyond 300°C, the rebar ends are exposed outside the concrete so that the longitudinal transducers can be attached there. These specimens are then experimentally studied using GW with an excitation frequency of 100 kHz. In this study, the RC beams are prepared as fully bonded and debonded specimens. The experimental study shows that heat damage in the RC beams causes debonding between rebar and concrete enabling GW signal to generate second harmonics. The experimental study also discussed the linear features of GW, which shows that the amplitude of the GW signal increases with elevated temperatures in the RC beams. To distinguish material nonlinearity and contact nonlinearity, two types of nonlinear parameters are defined in this study. The nonlinear parameter due to the contact acoustic nonlinearity effect in the RC beams is defined as β, whereas the nonlinear parameter due to material nonlinearity is defined as β m . The study shows that β m is negligible in comparison to β at relevant heated temperatures. With the increase in temperature, the nonlinear parameter β is significantly increased at elevated temperatures. The peak amplitude of the nonlinear parameter β is observed at the maximum heated temperature 800°C for both bonded and debonded specimens.