Application of Digital Image Correlation in Structural Health Monitoring of Bridge Infrastructures: A Review

Stereo Camera Setup for 360° Digital Image Correlation to Reveal Smart Structures of Hakea Fruits

About forty years after its first application, digital image correlation (DIC) has become an established method for measuring surface displacements and deformations of objects under stress. To date, DIC has been used in a variety of in vitro and in vivo studies to biomechanically characterise biological samples in order to reveal biomimetic principles. However, when surfaces of samples strongly deform or twist, they cannot be thoroughly traced. To overcome this challenge, different DIC setups have been developed to provide additional sensor perspectives and, thus, capture larger parts of an object's surface. Herein, we discuss current solutions for this multi-perspective DIC, and we present our own approach to a 360° DIC system based on a single stereo-camera setup. Using this setup, we are able to characterise the desiccation-driven opening mechanism of two woody Hakea fruits over their entire surfaces. Both the breaking mechanism and the actuation of the two valves in predominantly dead plant material are models for smart materials. Based on these results, an evaluation of the setup for 360° DIC regarding its use in deducing biomimetic principles is given. Furthermore, we propose a way to improve and apply the method for future measurements.

R3-DICnet: an end-to-end recursive residual refinement DIC network for larger deformation measurement

Digital image correlation (DIC) is an optical metrology method for measuring object deformation and has been widely used in many fields. Recently, the deep learning based DIC methods have achieved good performance, especially for small and complex deformation measurements. However, the existing deep learning based DIC methods with limited measurement range cannot satisfy the needs of real-world scenarios. To tackle this problem, a recursive iterative residual refinement DIC network (R3-DICnet) is proposed in this paper, which mimics the idea of the traditional method of two-step method, where initial value estimation is performed on deep features and then iterative refinement is performed on shallow features based on the initial value, so that both small and large deformations can be accurately measured. R3-DICnet not only has high accuracy and efficiency, but also strong generalization ability. Synthetic image experiments show that the proposed R3-DICnet is suitable for both small and large deformation measurements, and it has absolute advantages in complex deformation measurement. The accuracy and generalization ability of the R3-DICnet for practical measurement experiments were also verified by uniaxial tensile and wedge splitting tests.

Analysis of FRP-Strengthened Reinforced Concrete Beams Using Electromechanical Impedance Technique and Digital Image Correlation System

Fiber-reinforced polymer (FRP) strengthening systems have been considered an effective technique to retrofit concrete structures, and their use nowadays is more and more extensive. Externally bonded reinforcement (EBR) and near-surface mounted (NSM) technologies are the two most widely recognized and applied FRP strengthening methods for enhancing structural performance worldwide. However, one of the main disadvantages of both approaches is a possible brittle failure mode provided by a sudden debonding of the FRP. Therefore, methodologies able to monitor the long-term efficiency of this kind of strengthening constitute a challenge to be overcome. In this work, two reinforced concrete (RC) specimens strengthened with FRP and subjected to increasing load tests were monitored. One specimen was strengthened using the EBR method, while for the other, the NSM technique was used. The multiple cracks emanating in both specimens in the static tests, as possible origins of a future debonding failure, were monitored using a piezoelectric (PZT)-transducer-based electromechanical impedance (EMI) technique and a digital image correlation (DIC) system. Clustering approaches based on impedance measurements of the healthy and damaged states of the specimens allowed us to suspect the occurrence of cracks and their growth. The strain profiles captured in the images of the DIC system allowed us to depict surface hair-line cracks and their propagation. The combined implementation of the two techniques to look for correlations during incremental bending tests was addressed in this study as a means of improving the prediction of early cracks and potentially anticipating the complete failure of the strengthened specimens.

Bridge Damage Identification Using Deep Neural Networks on Time-Frequency Signals Representation

For the purpose of maintaining and prolonging the service life of civil constructions, structural damage must be closely monitored. Monitoring the incidence, formation, and spread of damage is crucial to ensure a structure's ongoing performance. This research proposes a unique approach for multiclass damage detection using acceleration responses based on synchrosqueezing transform (SST) together with deep learning algorithms. In particular, our pipeline is able to classify correctly the time series representing the responses of accelerometers placed on a bridge, which are classified with respect to different types of damage scenarios applied to the bridge. Using benchmark data from the Z24 bridge for multiclass classification for different damage situations, the suggested method is validated. This dataset includes labeled accelerometer measurements from a real-world bridge that has been gradually damaged by various conditions. The findings demonstrate that the suggested approach is successful in exploiting pre-trained 2D convolutional neural networks, obtaining a high classification accuracy that can be further boosted by the application of simple voting methods.

Fiber Optic Sensing Technology and Vision Sensing Technology for Structural Health Monitoring

Structural health monitoring is currently a crucial measure for the analysis of structural safety. As a structural asset management approach, it can provide a cost-effective measure and has been used successfully in a variety of structures. In recent years, the development of fiber optic sensing technology and vision sensing technology has led to further advances in structural health monitoring. This paper focuses on the basic principles, recent advances, and current status of applications of these two sensing technologies. It provides the reader with a broad review of the literature. It introduces the advantages, limitations, and future directions of these two sensing technologies. In addition, the main contribution of this paper is that the integration of fiber optic sensing technology and vision sensing technology is discussed. This paper demonstrates the feasibility and application potential of this integration by citing numerous examples. The conclusions show that this new integrated sensing technology can effectively utilize the advantages of both fields.

Three-Dimensional Digital Image Correlation Based on Speckle Pattern Projection for Non-Invasive Vibrational Analysis

Non-contact vibration measurements are relevant for non-invasively characterizing the mechanical behavior of structures. This paper presents a novel methodology for full-field vibrational analysis at high frequencies using the three-dimensional digital image correlation technique combined with the projection of a speckle pattern. The method includes stereo calibration and image processing routines for accurate three-dimensional data acquisition. Quantitative analysis allows the extraction of several deformation parameters, such as the cross-correlation coefficients, shape and intensity, as well as the out-of-plane displacement fields and mode shapes. The potential of the methodology is demonstrated on an Unmanned Aerial Vehicle wing made of composite material, followed by experimental validation with reference accelerometers. The results obtained with the projected three-dimensional digital image correlation show a percentage of error below 5% compared with the measures of accelerometers, achieving, therefore, high sensitivity to detect the dynamic modes in structures made of composite material.

Multi-Camera Digital Image Correlation in Deformation Measurement of Civil Components with Large Slenderness Ratio and Large Curvature

To address the limitations of conventional stereo-digital image correlation (DIC) on measuring complex objects, a continuous-view multi-camera DIC (MC-DIC) system and its two forms of camera arrangement are introduced. Multiple cameras with certain overlapping field of view are calibrated simultaneously to form an overall system for measuring the continuous full-surface deformation. The bending experiment of coral aggregate concrete beam and the axial compression experiment of timber column are conducted to verify the capability of continuous-view MC-DIC in deformation measurement of civil components with large slenderness ratio and large curvature, respectively. The obtained deformation data maintain good consistency with the displacement transducer and strain gauge. Results indicate that the continuous-view MC-DIC is a reliable 3D full-field measurement approach in civil measurements.

Structure Monitoring with BIM and IoT: The Case Study of a Bridge Beam Model

The diffusion of Building Information Modelling (BIM) as a reference methodology, applied to the world of construction, leads to important changes in the design and the management of big constructions and infrastructures. However, although the BIM approach is necessary for managing the entire life cycle of a construction, today, this methodology is still rarely adopted beyond the design phase. This represents a major flaw because the management of every phase of the life cycle of buildings needs accurate preliminary planning. Certainly, one of the most complex and important phases of the life cycle of a construction is the monitoring phase, which represents a fundamental aspect for the maintenance and the safe fruition of buildings or civil constructions. Considering this, the multidisciplinary approach of merging BIM methodology with real-time monitoring, using low-cost IoT (Internet of Things) sensors, seems to be an interesting topic to develop. In this paper, we will propose an example of a connection between an IoT system and a BIM model for monitoring the deflection of a bridge beam at the centerline using a schematic scale model reproduced in the laboratory and modelled in BIM. The developed system allows the real-time connection between the real model and its digital twin through the use of a relational database management system (RDBMS), to which the data detected by the sensor are transmitted, allowing the risk assessment of the real structure. This solution gives the possibility to remotely monitor, in real time, the behaviour of the structure visualised in the BIM model.