Strain Data Research Articles

Orientation Influence on Tensile Strain Hardening in Forged Plates of AA2014 Aluminium Alloy

Strain hardening is an effective strengthening method for alloys that requires plastic deformation of the material during manufacturing. The strength will significantly increase due to the number of dislocations formed during plastic flow of material. This study examines the plastic flow activity of forged plates of an AA2014 aluminium alloy with tensile test conditions. The effects of solution treatment and artificial ageing on strain hardening characteristics and tensile behaviour were investigated using tensile testing and scanning electron microscopy. Hollomon plastic flow relationship was employed for solution treated and age hardening conditions by experimental engineering stress- engineering strain data of the aluminium alloy AA2014. In both solution-treated and age-hardened conditions, the alloy displays three distinct strain hardening rate levels. The highest rate of strain hardening occurs both in solution-treated and age hardening conditions in regions with lower strain. It is also noted that specimens experience higher and lower strain hardening rates in the forging direction (Longitudinal, L) and perpendicular to the forging direction (Transverse, T) respectively in both solution-treated and age hardening conditions. In order to bring out the degree of in-plane anisotropy, test are conducted in L, L+45° (45° to L direction) and T in the plane of forging. Lower and higher magnification SEM images of the alloy under study, clearly exhibited that the ductile dimple fracture associated with a lower shear fracture contributions.

Development of Biofidelic Skin Simulants Based on Fresh Cadaveric Skin Tests

The development of artificial skin that accurately mimics the mechanical properties of human skin is crucial for a wide range of applications, including surgical training for burn injuries, biomechanical testing, and research in sports injuries and ballistics. While traditional materials like gelatin, polydimethylsiloxane (PDMS), and animal skins (such as porcine and bovine skins) have been used for these purposes, they have inherent limitations in replicating the intricate properties of human skin. In this work, we conducted uniaxial tensile tests on freshly obtained cadaveric skin to analyze its mechanical properties under various loading conditions. The stress–strain data obtained from these tests were then replicated using advanced skin simulants. These skin simulants were specifically formulated using a cost-effective and moldable multi-part silicone-based polymer. This material was chosen for its ability to accurately replicate the mechanical behavior of human skin while also addressing ethical considerations and biosafety concerns. In addition, the non-linear mechanical behavior of the developed skin simulants was characterized using three different hyperelastic curve-fit models (i.e., Neo-Hookean, Mooney–Rivlin, and Yeoh models). Moreover, these innovative simulants offer an ethical and practical alternative to cadaveric skin for use in laboratory and clinical settings.

An unobserved lava fountain deciphered in real-time by high-precision borehole strainmeter and contribution to hazard evaluation: the Etna 21 May 2023 eruption

A powerful lava fountain took place on 21 May 2023 at Mt. Etna after a year of repose, preceded a few days earlier by a shallow seismic swarm. The main critical issue with the eruption was the difficulty to track this eruptive event with conventional remote sensing devices, such as webcams and satellite instruments, due to the bad weather conditions and especially the dense cloud cover. Despite the sophisticated monitoring available at the volcano, the eruption effectively remained “hidden”. It was here that the borehole dilatometers excelled: as with all recent lava fountains at Etna, these high-precision instruments detected significant strain changes and proved a valuable contribution to real-time monitoring of the event. Through recently implemented approaches, the analysis of the strain data allowed us to decipher the eruption: namely identify the timing of the events, evaluate the “size” of the fountain (i.e. its eruptive intensity), and also estimate the erupted volumes in near real-time. This provided a useful support during the Civil Protection emergency meeting at the Prefecture of Catania, where these results were presented and jointly discussed. Overall, the 21 May 2023 lava fountain was an important showcase, demonstrating the strategic contribution that real-time high-precision strain signals may have in defining and assessing the hazard associated with an eruptive event even in adverse weather conditions, when remote sensing systems may be unable to furnish direct information on the ongoing phenomenon.

Performance Assessment of Reinforced Concrete Structures Using Self-Sensing Steel Fiber-Reinforced Polymer Composite Bars: Theory and Test Validation

This paper presents a novel self-sensing steel fiber-reinforced polymer composite bar (SFCB). The SFCB combines damage control, self-sensing, and structural reinforcement functions using distributed fiber optic sensing (DFOS) technology. By combining DFOS strains with theoretical and numerical models, a multilevel performance method for damage assessment is proposed from the perspectives of safety, suitability, and durability. Stiffness is a metric used to assess the complete service history of the reinforced concrete (RC) structure, which was used to define the damage variables. Initially, a basic correlation is created between the SFCB strain and several performance characteristics, such as moment, curvature, load, deflection, stiffness, and crack breadth, at characteristic points. The threshold values of damage variables for safety, serviceability, and durability were determined based on loading peak, mid-span deflection limits, and crack width limits corresponding to the damage variables. Then, a modified fiber damage model based on DFOS strain data is proposed to improve identification, quantification, and tracking for fiber damage. Finally, the reliability of the proposed theoretical and numerical models was verified by three-point flexural tests of SFCB-RC beams, and the test beams were analyzed using the proposed method. The results show that increasing the reinforcement ratio can lower the threshold at all levels and improve the ability of the flexural beams to control damage. This paper contributes to advancing the intelligence of RC structures and offers valuable insights for the design of intelligent RC structures.

Research on Testing Methods for the Modulus of Elasticity of Inorganic Binder-Strengthened Materials based on the Direct Strain Method

Abstract In order to obtain the elastic modulus of inorganic binder-stabilized materials based on the lateral method, firstly, by analyzing the constitutive model of the material’s elastic modulus, a testing system for the elastic modulus of inorganic binder-stabilized materials was constructed using pressure sensors, resistance strain gauges, and dynamic and static strain data testing systems. Secondly, based on the stability test of the strain gauges glued to the surface of the specimen, it is proposed that the zero drift of the strain gauges should be controlled within 0~6με within 6 minutes of the test load. Meanwhile, based on the experimental loading results, it is determined that the length of the strain gauge wire mesh used for modulus testing of inorganic binder-stabilised materials should be 30mm. Finally, elastic modulus testing of inorganic binder-stabilised materials was carried out on core samples drilled at Longxun highway in Guangdong Province. The modulus of elasticity of on-site drilled core specimens was determined by the test and the test results were at the same data level as those measured by the rigid ring method, indicating reliable analysis results. The experimental results show that the direct strain method with the elastic modulus testing system as the core can greatly reduce the test preparation time, ensure stable system operation and provide reliable test results.

Application of fiber-optic strain sensing technology in high-precision load prediction of aircraft landing gear

In this paper, a compact and lightweight high-precision airborne fiber-optic strain sensor is designed, and a strain-load prediction model based on convolutional neural network and long short-term memory network (ConvLSTM) is proposed to validate the collected landing gear strain data. Firstly, based on the fiber Bragg grating (FBG) sensing principle and simulation validation, small and lightweight strain and temperature sensors were fabricated, and key performance parameters such as sensitivity and linearity were comprehensively investigated, and they were mounted on the left landing gear of the aircraft to conduct strain monitoring by loading a three-way load, and the ConvLSTM model was used to train and test the strain-load mapping with the maximum relative error, average relative error and variance as indicators to evaluate its prediction accuracy and stability. The experimental results show that the strain measurement accuracy is maintained within 2.5 % and the strain sensitivity is as high as 1.3 pm/με within the ± 5000 με measurement range of the strain sensor; the maximum relative errors of the X, Y, and Z load predictions are 6.03 %, 3.75 %, and 4.12 %, respectively, and the overall average relative errors are 2.38 %, 0.27 %, and 0.76 %, with variances of 0.23 N, 0.61 N, and 0.61 N, respectively. 0.23 N, 0.61 N and 0.12 N, indicating that the model predictions are stable and highly accurate, demonstrating higher prediction accuracy when compared with traditional multiple linear regression methods. The results of this research have important application value in the field of aircraft structural health monitoring.

Evaluation of ring test with reference to deformation rate and specimen geometry in assessing the tensile behaviors of granite

Ring test for indirect tensile strength measurements utilizes disc specimens with a hole at the center of the disc. Such specimens are found to limit the stresses at the specimen-platen contact and transfer the tensile stress to the upper- and lower-hole boundaries in ring specimens. Researchers observed that the tensile strength of a ring specimen tends to decrease as the ratio of the hole-radius to the disc-radius (ρ) increases. However, there has been no research on how the tensile strength derived from ring specimens varies when loaded under different quasi-static strain rates or deformation rates. The strain analysis in case of ring tests also does not seem to have gained attention. In this study, ring specimens of Malanjkhand granite (India) with varying ρ (0.13, 0.17, 0.21 and 0.25) were quasi-statically loaded at 0.5 mm/min, 1.5 mm/min and 5.5 mm/min corresponding to strain rates of 1.75 × 10−4 s−1, 5.26 × 10−4 and 1.93 × 10−3 s−1, respectively. The combined effect of the deformation rate and specimen geometry (ρ) on the indirect tensile strength and deformation behavior was investigated. The Ring Tensile Strength (RTS) is found to be higher than the Brazilian Tensile Strength (BTS). RTS shows dependency on both the geometry of the ring specimen as well as the deformation rate. The tensile Tangent Deformation Modulus (Dv) and the ratio of the horizontal strain to vertical strain, coined as Ring Strain Ratio (RSR), were estimated in this study from the tensile stress and vertical-horizontal strain data. A numerical finite element analysis was also performed in Abaqus to observe the stress distribution in both ring and Brazilian discs, where the results were found to be broadly conformable.

Investigation of full-field strain evolution behavior of Cu/Ni clad foils by interpretable machine learning

Void characteristics are fundamentally correlated with the macroscopic deformation responses of materials, yet traditional modeling methods exhibit inherent limitations in data mining. In this study, a machine learning (ML) framework is proposed to predict the full-field strain evolution of Cu/Ni clad foils, and the impact of intrinsic voids is quantitatively assessed using interpretative analysis methods. The local strain and void data are extracted and integrated through digital image correlation and computed tomography. To accommodate the nature of the constructed dataset, a ML model is established with reference to the concept of time series forecasting. Subsequently, the influence of microstructural features such as volume fraction (VVF), area, and size of voids are investigated, alongside their role in driving local strain evolution. This approach successfully predicts strain localization, and accurately pinpoints the onset of plastic instability and the location of crack initiation. The VVF is identified as the most predominant factor, followed by void size along the tensile direction and grain size. The strongest association is observed between the VVF and grain size, which intensifies over extended time scales. Moreover, as void coalescence is almost completed, the promoting effect of the concentrated void distribution on macroscopic strain concentration will become increasingly pronounced. These findings provide novel perspectives for exploring the intricate relationship between deformation and damage.

An integrated deep learning model for intelligent recognition of long-distance natural gas pipeline features

Pipeline feature recognition is crucial for the reliability and safety of long-distance natural gas pipelines. Utilizing manual or machine learning methods to recognize pipeline features is not only inefficient, but also has problems such as high misjudgment rate and poor robustness. To overcome the above problems, this paper proposes a pipeline feature recognition method based on Multi-scale Attention Convolutional Neural Network (MACNN) and Gated_Twins_Transformer. MACNN is used to extract multi-scale information of pipeline features, and then the attention mechanism in it to focus on the more important feature information and suppress the less important feature information. It is then transmitted to the Gated_Twins_Transformer model, which uses the gated mechanism and the twins encoder module to determine the importance of the data length and input dimensions, focusing on the feature information of both with different weights, and the Transformer enhances the extraction of global features. Finally, the measured pipeline bending strain data are used as model input, trained and tested, and compared with other advanced models, the superiority of the proposed model in this paper is verified by comparing the metrics of Accuracy, Precision, Recall and F1-score.

A dynamic strain prediction method for malfunction of sensors in buildings subjected to seismic loads using CWT and CNN

Structural health monitoring (SHM) is a technique used to evaluate the safety of a structure based on the structural response. In the SHM, sensors may fail to measure the structural response, or data loss can occur during the data transmission and reception. In this regard, this paper proposes a method of predicting the strain response when a sensor installed in a structural member suffers a malfunction. To this end, a convolutional neural network (CNN) is introduced to predict the strain response. Time-history strain data for seismic loads measured in adjacent structural members are set as the input layer of the CNN, and continuous wavelet transform (CWT) results are additionally set in the input layer of the CNN to reflect the dynamic structural behavior during earthquakes in the CNN predictions. Time-history strain data for a member, the target of prediction, are set as the output layer of the CNN. Then, the trained CNN uses the time-history strain data of adjacent members with no sensor failure and the CWT result of the data to predict the strain response of a structural member with sensor failure. A numerical study on the seismic load of a single-span three-story steel moment frame and an experimental study on a single-span three-story reinforced concrete frame were carried out to verify the validity of the proposed method. This study presents a method of applying CWT data to the input layer of the CNN and discusses the effectiveness of CWT data for predicting the nonlinear response of a structural member with damaged sensors.

Novel SoC-Based FBG Calibration Method for Decoupled Temperature and Strain Analysis within LIB Cells

Abstract Internal temperature monitoring of battery cells can be very useful, as the core temperature can deviate significantly from that of the housing, especially in case of cells with a thick electrode stack. Conventional resistance temperature detectors can accurately measure temperature, but are limited to the outer surface of the cell due to induction effects. They are therefore not suitable for internal in situ measurements. Fiber Bragg grating (FBG) sensors are unaffected by the electric field as they operate by reflecting light. However, a specific difficulty is the distinction of temperature vs. strain effects as the grating is sensitive to both. In this work a calibration routine to separate the influences of temperature and strain in a lithium-ion battery cell is presented and examined for two multi-layer stack pouch cells (10 and 20 Ah). The obtained in situ temperature data reveal a difference of up to 2°C between center and cell housing at elevated discharge rate (4C) and a delay in detection of temperature peaks by the external sensor by 12 s. Strain data correlate with numbers of electrode layers in the stack and yield a stress of up to 27.3 MPa in the center of the 20 Ah cell.

Impact localization of CFRP laminate based on FBG sensing and GWO-BP neural network

Carbon fiber reinforced plastic (CFRP) structures are susceptible to external energy impact during service, resulting in invisible damage. Therefore, accurate localization of these impact sites is crucial for the maintenance and safety of CFRP structures. In this paper, the strain data of the impact response of the CFRP laminate is obtained by using the fiber grating (FBG) sensor, and the data are mapped to the impact location by BP neural network. However, the traditional BP neural network is susceptible to local minima and slow convergence speed. Therefore, Grey Wolf optimization algorithm (GWO) is introduced in this study to optimize the BP neural network, which improves the convergence speed and positioning accuracy of the model. The experimental results show that the GWO-BP neural network model has excellent performance in impact positioning, with the maximum positioning error of 17.01 mm and the average positioning error of 13.05 mm. Compared with other machine learning methods, it has higher accuracy and efficiency.

Abstract 4138701: Phenotypic clustering of HFpEF using cardiac magnetic resonance imaging

Background: Improved understanding of the high-risk features associated with HFpEF may facilitate risk stratification. Cardiac magnetic resonance (CMR)-based strain and structural data can define detailed myocardial structure, which can be leveraged to define heterogenous HFpEF phenotypes. This study used phenotypic clustering integrating CMR-based strain and structural data to identify a high-risk HFpEF phenotype. Methods: Retrospective analyses of 48 HFpEF participants, who underwent cardiac MRI and invasive cardiopulmonary exercise testing (median time between the two studies=40 days), was performed. We conducted unsupervised and unbiased K-means clustering analyses using CMR-based strain and myocardial structural variables. We associated two identified clusters with mortality using Cox proportional hazards modeling and constructed Kaplan-Meier survival curves. A p-value was considered significant if < 0.05. Results: We identified a low (n=32) and high-risk (n=16) cluster. Patients in the identified clusters had similar ages and comorbidities. The high-risk cluster had more male participants and hemodynamically had a higher exercise mPAP/CO and PAWP/CO slope, which are metrics of pulmonary vascular disease. The high-risk cluster displayed greater LV mass, worse biventricular longitudinal strain, decreased right ventricular ejection fraction, and greater interventricular septal angle (all P<0.05) as characterized by CMR as compared to the low-risk cluster (Table 1, Figure 1A). Mortality for the high-risk cluster was 5.43 times that for the low-risk cluster at 12 months (95% CI 1.69-17.42, Figure 1B). Conclusions: Using clustering analyses with CMR strain and structural variables, we identified a high-risk HFpEF phenotype with increased LV mass, reduced biventricular strain, and reduced RV ejection fraction.

A deep kernel regression-based forecasting framework for temperature-induced strain in large-span bridges

The strain data from health monitoring systems of large-span bridges is influenced by various load effects, with the extraction and forecasting of temperature-induced strain being particularly significant for precise analysis and early warning of monitoring data. This paper presents a forecasting framework for temperature-induced strain in large-span bridges, employing a deep kernel regression (DKR) approach that integrates deep learning with Bayesian regression to enhance accuracy and certainty. Initially, this paper addresses the influence of additional response increment induced by vehicle strain effects and employs a robust data smoothing algorithm to extract temperature-induced effect components from measured strain data offline. Subsequently, a DKR model is proposed, integrating a long short-term memory (LSTM) layer with a fully connected layer. The output of the deep learning module serves as the kernel function parameter for the Gaussian process regression (GPR) module, and the GPR module with updated hyperparameters is used for time series forecasting. This method effectively extracts and utilizes time series features from historical data alongside key environmental factors, enabling real-time forecasting of strain effects and significantly improving the performance and utility of health monitoring systems in large-span bridges. Compared to commonly used time series forecasting algorithms, the algorithm proposed in this paper exhibits significantly improved accuracy, stability, and certainty. Through comparing the inference time, it was verified that the algorithm can meet the performance requirements of real-time inference, which underscores the model's potential as a robust tool in bridge structural health monitoring.

Development of an ultra-low-cost wireless data acquisition system for monitoring strain and deflection

Strain gauges are gaining popularity for monitoring stress and strain within structural elements and for detecting and identifying structural damage. Although they are relatively inexpensive, they are not as widely used as accelerometers in structural health monitoring. This is due to several factors, such as the high cost of data acquisition systems required to collect strain data and the need for a large number of gauges to monitor a structure. Therefore, this research aims to develop a low-cost wireless strain gauge data acquisition system with acceptable accuracy and a high sampling rate. This system is composed of commonly available electronic components and open-source software, with a cost of $20. The system’s precision in measuring displacement was assessed in a laboratory experiment, and further experiments were conducted on an aluminum beam to investigate its response under both static and dynamic conditions. Additionally, the accuracy of the system for measuring deflection and strain in reinforced concrete elements was assessed through a bending test on a reinforced concrete beam. The findings indicated that the developed data acquisition system can accurately measure displacement, with a root mean square error of 0.1 mm, as well as strain values within the range of a few micro-strains.

Revealing failure loads of cyclically loaded H-steel columns from a thermodynamic perspective

Steel failure is always defined by phenomena such as yielding and buckling. A steel structure may fail under complex conditions such as cyclic or biaxial loading, but determining its critical loads is complex. Ref. [Case Stud Constr Mat., 2023; 18] used the dataset of Ref [Eng Struct., 2018; 174 826-839] (A set of cyclic loading tests of H-steel columns with material Q345B and length 1500 mm) attempts to reveal the systematic failure law of H-steel columns during cyclic loading from residual strains based on draft stage stressing state theory, but ignoring the relativity among the elements. With our lab's growing understanding of structural failure laws' systematic and evolutionary nature, ignoring the vital connection between the stressing state theory and thermodynamics is problematic. This work uses the dataset of Ref. [Eng Struct., 2018; 174 826-839] and try to reveal the failure laws of H-steel columns from a thermodynamic perspective based on the stressing state theory and new characterization methods. We first transform amplitude strain data under cyclic loading into state variables and divides them into six parts. The network-free renormalization method can be used to characterize the stressing state evolution of H-steel columns. Further, the phase transition loads can be detected by applying clustering analysis criteria because of their attractor effect. On the other hand, Wilson's phase transition theory defines phase transition loads as the fixed points of renormalization, which can be verified by comparing the before and after renormalization (part and overall) results. Lastly, the economy index (EI) is defined as the ratio of the biaxial moment triangle area S2 to the steel column volume at the elastoplastic branch (EPB) point to verify the safety and economy of the EPB-based seismic performance design for H-steel columns. EPB-based designs increase the EI of Class IV and III H-steel columns by 258.8 % and 365.8 %, respectively. Thus, this work provides a new reference for thermodynamic-based failure and seismic performance analysis of H-steel columns.

Combining long short-term memory and shape superposition for estimating moving load-induced global responses of bridges

Recently, a method of estimating the global responses of bridges was introduced, by combining a deep neural network (DNN) and shape superposition method (SSM). This DNN-SSM model demonstrated superior performance to the traditional estimation method based on the least-squares method. However, the application and validation of this approach have been restricted to static problems. For bridges, the dynamic effect induced by the moving load requires consideration. Therefore, this study proposes a long short-term memory (LSTM)-SSM model to improve the estimation performance for dynamic global responses. The LSTM-SSM model consists of LSTM layers, followed by fully connected DNN layers and a post-process part based on SSM. Limited displacement, slope, and strain data were used as the inputs, and the global displacement, slope, and strain were estimated. To validate the effectiveness and improvement of the LSTM-SSM model, a numerical model of a cable-stayed bridge was used to compare the proposed model with the previously developed DNN-SSM model. The validation results indicated that the LSTM-SSM model can provide more accurate estimates of the dynamic global response than DNN-SSM models. Finally, a technique for structural safety monitoring based on the proposed method was introduced.

Application of Asaoka’s Method in Multistage Triaxial Compression Testing of Normally Consolidated Soils

Abstract Determination of shear strength parameters becomes a challenge when it is difficult to retrieve a minimum of three identical undisturbed soil specimens. In such a situation, multistage triaxial compression testing can be adopted using a single specimen. Multistage triaxial testing involves stage-wise consolidation and shearing at three confining pressures without the failure of the specimen until the last stage. A critical issue associated with multistage triaxial testing is the estimation of ultimate deviator stress and the corresponding pore pressure as the specimen was not allowed to reach failure in each stage. Conventionally, the failure stress state in each stage is computed by extrapolating the stress–strain and pore pressure–strain data to a finite strain by employing Kondner’s rectangular hyperbola model. However, it is reported in the literature that the aforementioned method often overestimates the deviator stress and pore pressure values. In this context, the present study investigates the suitability of Asaoka’s observational procedure for predicting these values at failure state. The validity of the proposed procedure is verified by comparing the ultimate deviator stress and pore pressure values obtained by analyzing the stress–strain data collected from the literature and by performing multistage triaxial testing on three reconstituted soil samples with that of the rectangular hyperbola method. From the results, it was observed that Asaoka’s method is more suited for predicting the shear strength parameters. It is believed that the proposed methodology would aid in obtaining the shear strength parameters of a soil from a single specimen by conducting multistage triaxial testing.

Pathogen and host determinants of extrapulmonary tuberculosis among 1035 patients in Frankfurt am Main, Germany, 2008–2023

ObjectivesExtrapulmonary tuberculosis (EPTB) presents with nonspecific symptoms that can pose a significant diagnostic challenge. Various factors, including age, sex, and HIV status, have been associated with an increased risk of developing EPTB. However, the influence of the lineage of the infecting Mycobacterium tuberculosis complex (Mtbc) strain remains controversial. MethodsBetween 2008 and 2023, comprehensive clinical data from 1035 cases, along with whole genome sequencing data of the respective Mtbc strains, have been collected. To examine the association between Mtbc lineage and EPTB, we calculated crude and adjusted OR using logistic regression and performed propensity score matching with a subset of the cohort. ResultsOf the 1035 patients, 272 had exclusively extrapulmonary disease and 138 had both pulmonary and extrapulmonary disease. Patients infected with a lineage one strain had the highest odds of developing EPTB in the univariate analysis (OR, 3.30; 95% CI, 1.97–5.49). However, Mtbc lineage was not a significant predictor in the multivariable model, whereas the odds of developing extrapulmonary disease were higher among patients born in the South-East Asian Region (adjusted OR, 6.00; 95% CI, 3.41–10.69) and the Eastern Mediterranean Region (adjusted OR, 5.95; 95% CI, 3.61–9.96) compared with those born in the European Region. Furthermore, female sex and age were significant positive predictors for EPTB. DiscussionOur results demonstrate that host factors, such as geographic origin, age, and sex are stronger predictors for EPTB than infection with an Mtbc strain of a particular lineage. Further investigation of this host-pathogen interaction is needed.

Analysis of microcracking processes in microconcrete under confined compression utilising synchrotron-based ultra-high speed X-ray phase contrast imaging

In the present study, microconcrete (MC) samples were exposed to dynamic quasi-oedometric compression (QOC) tests and visualised in-situ by the means of MHz synchrotron X-ray phase-contrast imaging in the ESRF synchrotron in order to analyse the damage mechanisms governing the mechanical behaviour of concrete under high-strain-rate confined compression. To do so, small cylindrical samples were placed in polymeric confinement cell and dynamically compressed along their axial direction using SHPB (Split-Hopkinson Pressure Bar) set-up available in ID19 beamline in the European Synchrotron Radiation Facility (ESRF). The damage process was visualized with MHz X-ray phase-contrast imaging along with an ultra-high-speed camera operating at a recording frequency approximately 1 Mfps (million frames per second i.e., 880 ns interframe time). The axial stress and strain temporal profiles were obtained from standard Kolsky's (SHPB) data processing. In addition, data of radial stress and strain within the sample were deduced from non-linear analysis of the mechanical behaviour of the polycarbonate confining cell instrumented with a strain gauge. Finally, the onset and growth of microcracking observed from the equatorial zone of large spherical pores is correlated with deviatoric and pressure measurements showing how the pore collapse process develops during the applied mechanical loading.