Full-field Displacement Research Articles

Digital image correlation and infrared thermography data for seven unique geometries of 304L stainless steel

Material Testing 2.0 (MT2.0) is a paradigm that advocates for the use of rich, full-field data, such as from digital image correlation and infrared thermography, for material identification. By employing heterogeneous, multi-axial data in conjunction with sophisticated inverse calibration techniques such as finite element model updating and the virtual fields method, MT2.0 aims to reduce the number of specimens needed for material identification and to increase confidence in the calibration results. To support continued development, improvement, and validation of such inverse methods—specifically for rate-dependent, temperature-dependent, and anisotropic metal plasticity models—we provide here a thorough experimental data set for 304L stainless steel sheet metal. The data set includes full-field displacement, strain, and temperature data for seven unique specimen geometries tested at different strain rates and in different material orientations. Commensurate extensometer strain data from tensile dog bones is provided as well for comparison. We believe this complete data set will be a valuable contribution to the experimental and computational mechanics communities, supporting continued advances in material identification methods.

Full-field displacement measurements of structural vibrations using a novel two-stage neural network

This paper presents a novel two-stage neural network for quantifying full-field displacements of vibrating structures. A graphics software was used to simulate structural vibrations with random displacement fields and generate image datasets through batch rendering. Subsequently, a two-stage neural network model comprising an object segmentation subnetwork for extracting structural shapes and an optical-flow subnetwork for identifying displacement fields was constructed. A coordinate convolution module was embedded in the optical-flow estimation model and the two subnetworks were integrated to develop a comprehensive model. An experiment was designed to monitor the vibration displacements in a cantilever column. The results showed that the proposed model achieved optimal recognition accuracy with a mean absolute error of 0.1477 pixels and root-mean-squared error of 0.2335 pixels. Additionally, it exhibited robustness to various lighting conditions. Furthermore, case studies on real-world structural vibration videos substantiated the practical engineering application value of the proposed model.

Deformation ability of modular gymnasium steel inner sleeve all-bolt cross connection joint based on 3D-DIC method

Immense roof loads present a significant hurdle for the functionality of continuously supported modular connection joints in movable modular gymnasium structures. Consequently, we introduced leveraging inner sleeves and bolts to interconnect the modular units of the upper and lower columns, aiming to bolster the strength of inter-unit connections. Despite its noteworthy benefits, the deformation behavior and force distribution characteristics of this novel design remain underexplored. Hence, our study employs three-dimensional digital image correlation (3D-DIC) measurement to precisely capture the full-field displacement and strain distributions within the core area of this joint. Four distinct joint specimens were designed, and conducted comprehensive static tests, encompassing variables such as the inclusion or exclusion of inner sleeves, reinforcing ribs, and diverse loading directions. 3D-DIC detected subtle deformations and local buckling phenomena, undetectable by the naked eye, and revealed their occurrence patterns. Furthermore, the detections underscore the critical role of weld quality between beams and columns in ensuring the overall safety of the joints. The joint exhibited earlier yielding in beam bending compared to column bending. To delve deeper into this joint, we generated moment-rotation curves leveraging DIC results, deducing that this joint exhibits rigid-joint characteristics. The specimens primarily showed damage modes such as beam-root fractures or member buckling, while the core area remained intact. This observation reinforces the classification of the joint as a full-strength entity. These insights enrich our comprehension of this novel joint and serve as invaluable references and aids for designers.

Dynamic digital image correlation method for rolling convective contact

Digital image correlation (DIC) is an increasingly popular and effective non-contact method for measuring full-field displacements and strains of deformable bodies under load. Current DIC methods applied to bodies undergoing large displacements and rotations require a large measurement area for both the reference (i.e., undeformed) image and the deformed images. This can limit the resulting resolution of the displacement and strain fields. To address this issue, we propose a two-stage dynamic DIC method capable of measuring displacements and strains under material convection with high resolution. During the first stage, the reference image is assembled from smaller, high-resolution images of the undeformed object obtained using a spatially-fixed or moving frame. Following capture, each sub-image is rigidly translated and rotated into its appropriate place, thereby producing a full, high-resolution image of the reference body. In stage two, images of the loaded and deformed body, again obtained using a small camera frame with high resolution, are aligned with matching regions of the undeformed composite image using BRISK feature detection before performing DIC. We demonstrate the method on a contact problem whereby an elastomeric roller travels along a rigid surface. In doing so, we obtain fine-resolution measurements of the state of strain of the region of the roller sidewall in contact with the substrate, even as new material convects through the region of interest. We present these measurements as a series of images and videos capturing strain evolution as the roller transitions from static loads to a fully dynamic steady-state, documenting the effectiveness of the method.

A High-Precision Inverse Finite Element Method for Shape Sensing and Structural Health Monitoring

In the contemporary era, the further exploitation of deep-sea resources has led to a significant expansion of the role of ships in numerous domains, such as in oil and gas extraction. However, the harsh marine environments to which ships are frequently subjected can result in structural failures. In order to ensure the safety of the crew and the ship, and to reduce the costs associated with such failures, it is imperative to utilise a structural health monitoring (SHM) system to monitor the ship in real time. Displacement reconstruction is one of the main objectives of SHM, and the inverse finite element method (iFEM) is a powerful SHM method for the full-field displacement reconstruction of plate and shell structures. However, existing inverse shell elements applied to curved shell structures with irregular geometry or large curvature may result in element distortion. This paper proposes a high-precision iFEM for curved shell structures that does not alter the displacement mode of the element or increase the mesh and node quantities. In reality, it just modifies the methods of calculation. This method is based on the establishment of a local coordinate system on the Gaussian integration point and the subsequent alteration of the stiffness integration. The results of numerical examples demonstrate that the high-precision iFEM is capable of effectively reducing the displacement difference resulting from inverse finite element method reconstruction. Furthermore, it performs well in practical engineering applications.

Phase nonlinearity–weighted optical flow for enhanced full-field displacement measurement and vibration imaging

In this study, we developed a technique to utilize phase nonlinearity as a quantifiable confidence measure of the reliability of component displacement vectors extracted from multi-scale and multi-direction phases. The proposed approach includes three novel concepts. First, relative confidence values are established based on phase nonlinearity to quantify the reliability of the displacement components across various environmental conditions. Second, by analyzing the distribution of the relative confidence values, adaptive confidence measures are extracted and are used to reject unreliable displacement components; these confidence measures can dynamically adapt to various environmental conditions, such as varying image noise and large amplitudes of motion. Third, phase nonlinearity–weighted motion integration is conducted to ensure accurate estimation of the full displacement vector from the component vectors. By integrating these three approaches, a remarkable enhancement was achieved in full-field displacement measurement in terms of accuracy, robustness, and signal-to-noise ratio, especially in environments with high noise levels. The capability of the proposed technique was evaluated through numerical experiments using artificial patterns that simulate identical motions under varying noise levels. Experimental validation was also performed on an air compressor to substantiate the accuracy and robustness of the proposed technique. The findings verify its improvements over conventional techniques for full-field displacement measurement.

An optimized single-side inverse finite element method for displacement field reconstruction of wind turbine tower

Wind turbines are subjected to complex dynamic loads during actual operation, which are difficult to predict accurately in advance, which may lead to inaccurate structural strength assessment in the structural design stage, and thus bring safety risks to wind power towers. Inverse Finite Element Method (iFEM) is a reconstruction method of structural displacement distribution, which can estimate the full-field displacement distribution of tower, and used for structural on-line health monitoring. However, conventional iFEM require sensors to be placed on both sides of the structure, which greatly limits its engineering practicality. For this reason, a single-side inverse finite element (iFEM_S) was developed in this study to enhance the engineering applicability of iFEM. Both iFEM and iFEM_S are based on the principle of least squares variation, and the solution equations can be established when the measured strain is known. The core idea of iFEM_S is to find the optimal node displacement s to minimize the sum of mean square errors between the theoretical and measured strains on one side of the structure, thereby generating a set of algebraic equations. Solving these linear equations can achieve the structural displacement field. Additionally, A cyclic optimization algorithm (COA) was developed in this study to improve the robustness of reconstruction results through optimizing strain gauge locations, in which the condition number of pseudo stiffness matrix was used as the objective function, and the condition number is minimized, named iFEM_S_O. Finally, the reconstruction accuracy and robustness and of iFEM, iFEM_S and iFEM_S_O were verified through several numerical examples. The simulated reconstruction results indicated that iFEM_S_O is more accurate and stable than iFEM_S, and iFEM_S is more accurate and stable than iFEM.

From quasi-static to dynamic: Experimental study of mechanical and fracture behaviour of epoxy resin

Epoxy polymers are extensively used in various engineering applications such as aerospace, defence, sports, automotive etc. This article focuses on the in-depth mechanical characterisation of EPOFINE®-1564, a Bisphenol-A-based liquid epoxy resin under various loading conditions. To predict the tensile and compressive behaviour of the representative epoxy resin, quasi-static experiments were performed in the range of 10−4 to 10−2s−1 on Universal testing machine (UTM) while the dynamic experiments were conducted using Split Hopkinson Pressure Bar (SHPB) for high strain rates (1136–2833 s−1). In this study, 3D Digital Image Correlation (DIC) was also used to evaluate the specimen's full-field displacement profile over a wide range of strain rates. Analysis of various mechanical properties such as elastic modulus, yield strength, and ultimate strength, revealed that the epoxy polymer is strain rate dependent within the considered strain rate range. For understanding the fracture behaviour, three-point bend (TPB) experiments were also carried out for both quasi-static (1–10 mm/min) as well as dynamic (10–15 m s-1) regimes. Dynamic fracture experiments were performed using the modified Hopkinson Pressure Bar (MHPB). The fracture toughness was determined through load vs crack mouth opening displacement (CMOD). Fracture toughness was found to increase with the displacement rate due to the significant plastic deformation under quasi-static range. Conversely, it was found to decrease under dynamic loading because of absence of plastic deformation resulting in brittle fracture. The fracture surface of the specimen was examined through a high magnification digital microscope.

3D displacement measurement using a single-camera and mesh deformation neural network

Computer vision-based methods for civil structure’s vibration displacement measurement have emerged as useful tools in the recent years. These methods offer several benefits including non-contact measurements, cost-effectiveness, and the ability to capture full-field displacement. Yet, there remain certain challenges. Measuring vibration displacement in 3D typically requires multiple cameras, adding complexity to camera configurations. Moreover, existing methods relied heavily on physical markers or natural key points. Placing physical markers on structures is often impractical, and natural key points are difficult to detect on structures with few distinct features or during rapid movements. Contrary to previous approaches, this paper presents a novel technique that uses a monocular camera for 3D displacement measurements. This technique obviates the need for physical markers or the reliance on natural key points, representing a significant advancement. Central to the method is a deep neural network designed to predict 3D mesh deformation directly from a single image input, combined with an initial 3D cube mesh input. A synthetic 3D dataset is generated to train the neural network. In the testing phase for real structures, advanced video segmentation method is employed to remove the background in order to enhance the prediction accuracy. The practical efficacy of this methodology is validated in a laboratory through a series of experimental tests on beam structures, demonstrating reliable results and application potentials.

Dynamic impact mechanical properties of red sandstone based on digital image correlation method

ABSTRACT In the process of underground blasting excavation, the safety and stability of rock mass are significantly influenced by dynamic load. To study the full-field strain and displacement behaviour of rock materials subjected to dynamic compressive force, the impact tests were done on red sandstone specimens using an electromagnetically driven Hopkinson pressure bar. At the same time, the data were collected by a high-speed camera, and non-contact measurements of the red sandstone were carried out with the digital image correlation method. The resulting images were screened and analysed, based on which the stress-strain curve, full-field strain cloud diagram and displacement cloud diagram were obtained. Results show that the dynamic peak stress increases with the increase of impact velocity. During the impact process, substantial internal uneven deformation occurs in the red sandstone close to the transmission bar’s end face. Under the impact load, the cracks initiate from the upper and lower sides near the transmission bar’s end face, followed by formation of cracks in the middle part. These cracks expand throughout the rock to form through-cracks, eventually resulting in complete failure. For rock materials, the evolution of their strain field during the failure process can reflect the initiation and propagation of their internal cracks. Therefore, the use of digital image correlation method proves effective for studying the deformation and failure mechanisms of rock. The conclusions obtained in this study provide a valuable reference for the macro-meso deformation and failure mechanisms of geotechnical materials.

Shear crack kinematics in reinforced engineered cementitious composite (ECC) beams

The fiber’s bridging effect across the shear cracks is considered to play an important role of resisting shear in engineered cementitious composite (ECC), and fiber reinforced material in general. To quantify the shear crack kinematics (i.e., shear crack opening and sliding displacements) in reinforced ECC (R/ECC) beams, a crack measuring algorithm based on the full-field displacement spectrum is developed by using the Digital Image Correlation (DIC) technology. In addition, a novel distributed strain-measuring methodology was used to detect the strain distribution along the transverse and longitudinal reinforcement. Reinforced beams made of traditional concrete (R/C) and mortar (R/M) were used as reference. Through aforementioned monitoring schemes, the role of matrix (Vc) and stirrups (Vs) in shear resistance mechanism could be independently understood and evaluated. The R/ECC beams exhibited much higher Vc than the reference reinforced concrete (R/C) beams (by 68%∼104%). Nevertheless, the shear crack measuring results revealed that the higher shear strength in R/ECC did not always result from the fiber’s bridging effect across the critical shear crack (CSC) but of high shear-resisting contribution from ECC in shear-compression zone. For a better understanding of the shear failure mechanisms, phenomenological models of shear crack kinematics in R/C and R/ECC beams are proposed.

Multi-scale investigation of silane coupling agent for enhanced corrosion resistance in reinforced concrete

Reinforcement corrosion is a significant factor leading to the deterioration of building performance. In order to enhance the serviceability of reinforced concrete under corrosive environments, this study employs a silane coupling agent (γ-aminopropyltriethoxysilane) for steel reinforcement treatment. The anti-corrosion mechanism, crack propagation patterns, and modified corrosion resistance of the steel are investigated at multiple scales. Experimental techniques such as scanning electron microscopy (SEM), X-ray energy-dispersive spectroscopy (EDS), and Fourier-transform infrared spectroscopy (FT-IR) are utilized to explore the influence of the self-assembled coating of the silane coupling agent on the microscopic morphology, elements, and functional groups of the steel. Concrete specimens with treated reinforcement are fabricated, and electrochemical accelerated corrosion tests are conducted to compare corrosion differences among different specimens. Using untreated specimens as a control group, digital image correlation (DIC) technology is employed to capture the full-field strain and displacement distribution during the loading process. The crack propagation and slip behavior at the steel-concrete interface in the composite specimens are analyzed. The research findings indicate that, at the optimal concentration of 1.2 %, the self-assembled coating of the silane coupling agent successfully adheres to the steel surface, significantly enhancing the interfacial abrasion resistance and mechanical strength of the steel-concrete interface, thereby validating the improved corrosion resistance of the steel. This study proposes the self-assembly mechanism and interfacial enhancement mechanism of the silane coupling agent on steel at multiple scales, contributing to a comprehensive understanding of its effectiveness.

Identification, uncertainty quantification and validation of orthotropic material properties for additively manufactured polymers

The mechanical behavior of polymeric parts manufactured by fused filament fabrication is often characterized as orthotropic because of the layer-wise extrusion of the material. To perform reliable simulations of the mechanical response of these parts subjected to certain loads, the material parameters have to be known. This leads to the task of material parameter identification from suitable experimental data. In this work, the identification of the material parameters for orthotropic material behavior is studied in detail with a focus on the specific characteristics of additively manufactured parts. This comprises working out the differences that arise compared to the parameter identification for classical orthotropic composite materials. Moreover, the material parameters are identified from tensile and shear test information together with full-field displacement data from digital image correlation measurements. Then, the obtained parameters are compared for analytical and numerical approaches, where a non-linear least-squares method with finite elements is drawn on. The parameters are not only identified, but also an uncertainty analysis is carried out and, subsequently, the different methods are validated. Further, a clear derivation of commonly applied analytical equations is provided with a special focus on the underlying assumptions. It turns out that the analytical parameter identification procedures cannot be easily transferred to numerical procedures, especially when using full-field data, because of correlations between the parameters. Moreover, the identified parameters are employed to prove the reasonability of orthotropic material behavior for specimens manufactured by fused filament fabrication. Finally, during validation it turns out that all parameter identification methods provide a good agreement with experimental data, showing only small differences between the results of the different calibration strategies.

Versatile data-adaptive hyperelastic energy functions for soft materials

Applications of soft materials are customarily linked to complex deformation scenarios and material nonlinearities. In the bioengineering field, soft materials typically mimic the low stiffness of biological matter subjected to extreme deformations. Computational frameworks surge as a versatile tool to assist the design of functional applications. The constitutive model lies at the core of such frameworks. In this regard, the customary extreme non-linear behavior of elastomers poses an additional challenge to thoroughly capture the material behavior. Here, data-driven methodologies hold considerable promise for enhancing constitutive modeling when contrasted with phenomenological approaches. In this investigation, we introduce a versatile data-adaptive method tailored to the modeling of hyperelastic soft materials at finite strains. Specifically, our method substitutes an a priori chosen model for the strain energy function by a flexible interpolant defined on a discretized invariant space. Within this framework, the interpolation values assume the role of material parameters and are determined through finite element model updating to conform to measured experimental data — comprising full-field displacements coming from Digital-Image-Correlation and global reaction forces. We validate the method on uniaxial experimental tests of soft elastomers, encompassing ELASTOSILTM, DOWSILTM, and V HBTM. Overall, we aim to establish a new route for the construction of hyperelastic energy functions, untethered from any predefined existing models or assumptions regarding the shape of the energy.

High-speed multi-camera videogrammetric measurement of full-field 3D motion and deformation in full-scale crash testing of typical civil aircraft

Crashworthiness has been and will continue to be the main concern in aircraft design. Crash tests are essential approaches to investigate the crashworthiness of civil aircraft. Videogrammetric methods have been employed in crash tests to overcome the disadvantages of laborious, time-consuming measurement data collection using contact sensors. However, previous methods using a single pair of cameras still face challenges in large-scale applications due to the inherent contradiction between measurement range and accuracy. In this paper, a videogrammetric method using four high-speed cameras is proposed to measure the full-field three-dimensional (3D) dynamic deformation for evaluating the impact response characteristics of the full-scale crash test aircraft. Increasing the number of high-speed cameras not only can enhance the resolution and then measurement accuracy of object, but also expands the measurement range significantly. To ensure the performance of digital image correlation (DIC) used for large-scale measurement range, a speckle pattern optimization design and fabrication method is presented to generate the random speckle with uniform spraying size and density. A global calibration method based on the high-precision total station is proposed to unify the data of different stereovision subsystems. The implementation of the proposed method is illustrated through a 6.0 m/s full-scale crash test using a civil aircraft approximately 24 m in length. The impact velocity, full-field displacement and strain of the fuselage structure are measured. The results show that the test data are complete and reliable. After the vertical crash at 5.71 m/s, the lower structure of the cabin floor is seriously deformed, and the upper structure of the fuselage in the central wing area is obviously deformed due to the inertia effect of the wing. The stiffness difference of the different fuselage segments results in significant differences in dynamic response. The higher the stiffness is, the smaller the deformation. This work may provide some valuable technical insights that could support the crashworthy design, verification, and certification of civil aircraft.

Phase-based Full-field Displacement Measurement with Phase Nonlinearity Weighting Process

The camera, as a non-contact sensor, has evolved into a robust tool for measuring the full-field vibration of complex structures. It offers distinct advantages over traditional contact sensors, including flexible positioning, simultaneous multi-point tracking, and high spatial resolution. Vibration imaging techniques have been developed to identify structural operational conditions through feature extraction methods. Phase-based vibration imaging techniques can visualize full-field operational shapes or vibrational features in a less-supervised manner. While phase-based optical flow with Gabor filters provides accurate displacement estimation, managing a large amount of image data can pose challenges in extracting full-field displacement efficiently. In this paper, we propose a compressive sensing approach for full-field phase-based optical flow and vibration imaging. Compressive sensing serves as an efficient sampling method that requires significantly fewer frames than traditional approaches, selecting frames at less than one third of the original video. Features are extracted from the displacement of the compressed video frames. Subsequently, the vibration features are transformed into images. The proposed methodology is validated through experiments conducted on an air compressor. Comparative results between vibration imaging from video with compressive sensing and that from the original video demonstrate that vibrational features can be extracted using only a few frames of the original video. Consequently, this approach can reduce processing time and data volume while preserving the performance of vibration feature extraction.

Parametric Neural Networks as Full-Field Surrogates for Material Model Calibration

The identification of material parameters occurring in material models is essential for structural health monitoring. Due to chemical and physical processes, building structures and materials age during their service life. This, in turn, leads to a deterioration in both the reliability and quality of the structures. Knowing the current condition of the building structures can help prevent disasters and extend service life. We developed a physics-informed neural network (PINN) [1] for the calibration of the linear-elastic material model from full-field displacement data measured by digital image correlation. In an offline-phase, the PINN is trained to learn a parameterized solution of the underlying parametric partial differential equation without the need for training data [2]. We demonstrate the ability of the parametric PINN to act as a surrogate in a least-squares based material model calibration. In order to quantify the uncertainty, we further use the parametric PINN with Markov Chain Monte Carlo based Bayesian inference. Even with artificially noisy data, the calibration produces good results for reasonable material parameter ranges. Especially in sampling based methods, parametric PINNs have the advantage that model evaluation is very cheap compared to, e.g., the Finite Element Method. Thus, information on the material condition can be provided in near real-time in the online-phase. Moreover, PINNs use a continuous ansatz and thereby avoid the need to interpolate sensor locations to the simulation domain. In our ongoing work, we plan to apply the parametric PINN to more complex material models, such as those for elasto-plastic materials. We also investigate the extension of the proposed method to more complex geometries. [1] M. Raissi et al.: Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, Journal of Computational Physics 378, 686-707 (2019). [2] A. Beltrán-Pulido et al.: Physics-Informed Neural Networks for Solving Parametric Magnetostatic Problems, IEEE Transactions on Energy Conversion 37(4), 2678-2689 (2022).

Novel steel connection for modular houses in indigenous communities: An experimental study

Remote Indigenous communities across Canada are facing housing shortages and the need for major repairs, which resulted from engineering challenges that obstructed the construction phases. Prefabricated steel moment-resisting frame modular houses are currently utilized as alternatives to typical conventical houses because of their technical advantages, such as better-quality control, lightweight structures, and ease of transportation to remote and rural regions. To explore the potential use of hollow structural sections (HSS) in panelized steel modular structures, this study presents a novel steel-bolted connection for connecting HSS beams to HSS columns using high-strength long bolts, indicating that the connections can be easily installed without requiring skilled workers and heavy machinery. Six specimens were fabricated and tested under monotonic load with a Digital Image correlation (DIC) camera to capture full-field 3D displacement and deformation until failure accurately. The impact of different geometric parameters such as bolt arrangement, extended plate thickness, number of bolts, and the existence of stiffener on the mechanical performance of the joint, stiffness, inelastic rotation, ductility, and failure modes were analyzed. Bolt arrangement with an aspect ratio of 1:1 was found to increase the connection capacity, initial stiffness, and ductility by 7%, 216%, and 26%, respectively. The yielding and rotation of the extended plate were eliminated using a stiffener. Increasing the number of bolts triggered the connection's ability to gain more capacity at earlier stages and reduced joint rotation by 60%.

Deformation ability of steel inner sleeve T-joint in modular gymnasia based on 3D-DIC method

Substantial loads the roofs bear exert stringent performance standards on connecting joints in movable modular gymnasiums. Previously, we introduced a novel joint design, utilizing internal sleeves and bolts to integrate the upper and lower columns of modular units. To explore the actual deformation behavior and force distribution of this joint, this study employed a three-dimensional digital image correlation (3D-DIC) measurement method to visualize its full-field displacement and strain distribution. The reliability of the 3D-DIC results was confirmed through comparison with traditional measurement methods. Building upon this solid foundation, this study proceeds to assess the static performance of this innovative joint in two distinct directions, successfully revealing micro-deformation and local buckling phenomena undetectable to the naked eye. Notably, the quality of the welds connecting the beams and columns plays a pivotal role in ensuring the overall structural integrity of the joint. Under lateral loading conditions, the destructive behavior of the joints is primarily governed by material strength. Moment-rotation curves were plotted to delve further into the mechanical characteristics of the joint, revealing that the inner-sleeve T-joint exhibits the attributes of a rigid joint. Moreover, the primary damage modes observed in the specimens were fractures at the beam root or buckling of the column, with the joint area remaining intact. Consequently, this joint qualifies as a full-strength connection. These findings will deepen knowledge and understanding of this novel joint for designers.

Evolution Characteristics of Strain and Displacement Fields in Double-Hole Short-Delay Blasting Based on DIC

In the process of porous blasting excavation in engineering projects such as mining, hydropower stations, and tunnels, the delay time between adjacent blast holes significantly influences the characteristics of rock fracture and fragmentation. In order to visually explore the changing characteristics of strain and displacement between adjacent blast holes under different delay times, polymethyl methacrylate (PMMA) plates were used to simulate rock materials, and 2D digital image correlation (2D-DIC) testing methods were employed to measure the explosive strain field with different delay times (0 µs, 5 µs, 15 µs, 40 µs, 70 µs) for dual holes. Full-field principal strain cloud maps and displacement fields of PMMA boards in two-dimensional spatial coordinates were obtained, and the representative monitoring points were analyzed. The experimental results show that the maximum values of the first compressive principal strain peak and the first tensile principal strain peak at the connecting center of the two holes exist at a delay time of 0~40 µs under blasting conditions with the same hole distance and single hole charge. At the center point of the connection between the two holes, the interval time between the two principal strain peaks decreases with the increase in delay time. The maximum principal strain on the central vertical line of the connection line decreases exponentially with the increase in hole distance, and the attenuation trend increases first and then decreases with the delay time between 0 and 40 µs. At the peak of strain, the maximum average displacement of the connecting center of the two holes exists in the delay time between 0 and 40 µs. With the increase in delay time, the displacement trend between the two explosion holes gradually changes from shear to tension, and the vulnerable damage area increases, which makes the communication between the two explosion holes easier. This study can provide a basis for the precise selection of delay times between blasting holes in engineering.