bluuebunny/crossref_metadata_2025 huggingface dataset:
Reducing Artefacts in FRF-based Damage Detection Using Novel Mode Extraction Approach for Guided Ultrasonic Waves
Authors:   Technische Universität Braunschweig (TU), Roloff Thomas ,   Technische Universität Braunschweig (TU), Froböse Jörn ,   Technische Universität Braunschweig (TU), Hühne Christian ,   Technische Universität Braunschweig (TU), Sinapius Michael
Published: June 2024

Damage in thin-walled structures can be detected by guided ultrasonic wave (GUW) based structural health monitoring systems. The application of phased arrays enables the scanning of large-scale structures from a single position. However, physical wave focusing requires a lot of effort. This can be significantly reduced if frequency response functions (FRFs) are used. They enable the calculation of virtual response signals for virtual focusing on any position. Although this technique offers an energy-efficient and fast possibility of damage detection, it has the disadvantage of artefacts that occur due to the multimodal nature of GUW, as damage detection is generally designed for single-mode signals. These artefacts are often reduced by mode-selective excitation/sensing or by subtracting a baseline measurement.

This work presents a concept to combine an existing FRF-based damage detection algorithm and a previously presented method for mode extraction. It enables the extraction of GUW mode components from broadband, temporally sampled, single-input single-output sensor data during signal processing on the basis of the respective dispersion relations. The aim is to reduce artefacts without using mode-selective excitation/sensing or baseline measurements. A finite element simulation of GUW propagation in an isotropic structure is used to demonstrate the advantages and limitations of this approach. The simulations include multimodal and single mode evaluation to point out the added value of performing mode extraction prior to damage detection.

It is supposed that the successful extraction of the mode components from the temporally sampled data results in a decreasing amplitude of the occurring artefacts compared to the multimodal case, while the case of mode-selective excitation apparently results in no artefacts.

The presented concept of adding mode extraction to damage detection algorithms can lead to an increase in performance of FRF-based phased array systems. Artefacts that would lead to false detection of damage are reduced inherently during signal processing which eliminates the need for mode-selective excitation/sensing or baseline measurements.
Amplitude Dispersion Compensation in Time-Reversed Lamb Waves for Enhanced Sensitivity in Damage Detection
Authors:   Indian Institute of Technology Delhi (IIT), Jana Souvik ,   Indian Institute of Technology Delhi (IIT), Kapuria Santosh
Published: June 2024

In Lamb wave-based baseline-free damage detection employing the time-reversal method (TRM), the damage index (DI) is computed from the similarity between the main mode of the reconstructed signal obtained after the time-reversal process (TRP) and the input signal, both normalized. However, a perfect similarity with a zero DI value cannot be achieved for undamaged structures due to the amplitude dispersion of Lamb waves, which is not compensated in the TRP. Thus, a nonzero DI does not necessarily indicate the presence of damage. Even worse is the fact that the similarity and, therefore, the DI for the pristine structure depend on the probing frequency, making it difficult to estimate a unique threshold value of DI for damage identification. The imperfect reconstruction also significantly reduces the relative sensitivity of the DI to the presence of damage. In this article, we propose a method to compensate the amplitude dispersion in the main mode of the reconstructed signal for undamaged metallic plates to achieve a zero threshold of DI. First, the transfer function of the main mode of the reconstructed signal for an undamaged path (the path with the least DI for a network of sensing paths) is obtained by exciting the actuator with a broadband excitation and extracting the S0 and A0 modes from the forward response measured at the sensor. To compensate the amplitude dispersion in the TRP-generated main mode, the transfer function of the recorded signal of a path is divided by the transfer function of the main mode obtained for the undamaged path. A comprehensive numerical study shows that the DI computed with the compensated reconstructed signal has much higher sensitivity to damage than that without applying the proposed compensation at all frequencies, providing the flexibility to choose the probing frequency according to the desired damage size to be detected.
Comparison between acoustic emission sensors and piezoelectric patches for damage detection in concrete beams
Authors:   Politecnico di Milano, Cazzulani Gabriele ,   Aston University, Alexakis Haris
Published: June 2024

Acoustic emission (AE) sensing is an established technique to monitor crack nucleation and propagation in different types of structural elements and for different damage scenarios.
Although effective for crack monitoring in civil infrastructure, off-the-shelf AE sensors can be expensive, discouraging their installation in large numbers for assessing the global structural performance or monitoring multiple assets in the infrastructure network. For this reason, this paper explores the use of piezoelectric patches as an alternative low-cost solution to AE sensors. These patches are considerably cheaper, do not require external power supply and can be easily attached to existing structural elements or embedded into new structures. Moreover, thanks to the direct and inverse piezoelectric effect, the same set of patches can act both as sensors and actuators, meaning that they can be used to “passively” monitor crack propagation over time, as well as to locate and characterize damage in ageing structures at a certain time by using the patches as emitters and receivers of waves.
In this framework, this paper proposes a comparison between traditional AE sensors and piezoelectric patches. The comparison is performed on a set of three concrete beams subjected to four-point bending loading, each equipped with two grids of sensors, i.e. 8 AE sensors and 8 patches. The beams are tested under increasing monotonic loading, from zero-load uncracked condition up to collapse.
Acoustic emission monitoring for engineered barriers in nuclear waste disposal
Authors:   BAM Federal Institute for Materials Research and Testing, Skłodowska Anna Maria ,   BAM Federal Institute for Materials Research and Testing, Lay Vera ,   Deutsches Institut für Normung, DIN, Baensch Franziska ,   BAM Federal Institute for Materials Research and Testing, Niederleithinger Ernst ,   BAM Federal Institute for Materials Research and Testing, Kühne Hans-Carsten
Published: June 2024

To safely dispose of nuclear waste in underground facilities, engineered barrier systems are needed to seal shafts and galleries. The material used in these barriers must be adapted to the host rock parameters. Shrinking and cracking must be avoided to provide a barrier with almost zero permeability. For repositories in salt rock environments, several types of salt concrete (SC) are used.

Within the project SealWasteSafe, we compared the performance of an innovative alkali-activated material (AAM) with standard SC in their hydration and hardening phase. To monitor the microstructural changes within the two materials SC and AAM, acoustic emission (AE) signals have been recorded for up to ~250 days on 340-liter-cubic specimens.

The phenomenon of AE is defined as the emission of elastic waves in materials due to the release of localized internal energy. Such energy release can be caused by the nucleation of micro-fracture, e.g. in concrete while curing or when exposed to load. The occurrence of AE events gives first rough indications of microstructural changes and potentially occurring cracking and thus, provides insights for structural health monitoring (SHM).

The results show, that for the first 28 days after casting, less AE activity was detected in the AAM compared to SC. After 61 days, in the AAM material, the number of AE events exceeded those observed in the SC. However, the majority of the AE detected in AAM was related to surface effects, and not to microstructural changes within the matrix. Additionally, the source location analysis indicated, that despite lower activity in SC, we observed some clustering of the events. In contrast, in AAM, the activity inside the specimen is randomly distributed over the whole volume. The monitoring results help to estimate the material’s sealing properties which are crucial to assess their applicability as sealing material for engineered barriers.
A Study on XANNs for Analyzing Failures in Guided Ultrasonic Wave-based Damage Localization
Authors:   Faserinstitut Bremen (FIBRE), Polle Christoph ,   University of Bremen, Bosse Stefan
Published: June 2024

In the field of Structural Health Monitoring (SHM) using Guided Ultrasonic Waves (GUW), machine learning (ML) approaches for data analysis are becoming increasingly popular. Especially, deep learning with artificial neural networks (ANN) has proven effective due to its ability to process a large amount of data. ANN are commonly employed in damage localization tasks, where, with appropriate training, they deliver good results. However, a significant challenge persists in the application of ANN, namely, their "black-box" nature, which hinders the investigation of decision processes within the ANN and makes it difficult to understand why an ANN model fails under certain inputs.
In the present work, an already introduced explainable artificial neural network (XANN) approach will be utilized to investigate when and why a trained XANN model fails with given inputs. The Open Guided Wave (GUW) platform's data is employed to train and test a XANN model designed to predict the location of artificial damage. Subsequently, individual inputs are modified (simulating malfunctions in specific sensor paths), and the impact on decision processes within the XANN architecture is examined. The aim of this study is to investigate and enhance the resilience and explainability of ANN approaches in the SHM domain.
An Implementation of a Generalised Irreducible Element Model Builder for use within a Population-based Framework
Authors:   Sheffield University, McCulloch Josie ,   Sheffield University, Gowdridge Tristan ,   Sheffield University, Worden Keith ,   Sheffield University, Brennan Daniel S.
Published: June 2024

Civil infrastructure encompasses diverse geometries, materials, joints, and foundations. The seemingly infinite forms that modern infrastructure exhibit only makes structural health monitoring all that more challenging, particularly when data is traditionally viewed as unique to each structure. This philosophy often leads to limited data for any given structure. However, these challenges might be mitigated by identifying symmetries among structures at a broader population level. Given the various structural forms, it is beneficial to abstract out topological and structural information, by systematically breaking down complex and continuous geometries into simple and discrete blocks.

A promising approach is using irreducible element (IE) models. These models overlay graph theory principles atop structures, accounting for intrinsic attributes like material and joint type. IE models offer a Canonical Form for structures apt for statistical, structural, and topological comparisons between homogeneous and heterogeneous populations. Currently, IE model construction is a manual and time-consuming task. This paper presents an automated visual methodology for building an IE model implementation within a database framework; highlighting IE models as a user interface between real-world structures and database technologies for industrial applications. While the current implementation is broad in scope, it does lay the groundwork for future papers focussing on specific applications, such as bridges, wind turbines, and high-guided masts.
Railroad bridge monitoring using Dragonfly® piezoelectric strain gauge
Authors:   Wormsensing, Cadieux Catherine ,   Wormsensing, Garnell Emil
Published: June 2024

Structural Health Monitoring (SHM) applied to civil structures such as bridges, high-rise towers, and electricity pylons can help plan their maintenance, detect damages or un-expected events, and extend their remaining operating lifetime.

Metallic foil strain gauge and vibrating wire are the historic sensors used to measure strain on civil structures. However, civil structures are designed to undergo little deformation in service (0.1-50 µdef). Both SG and vibrating wire are limited by their resolution around 1 µdef. Moreover, they are known for their DC drift over time, which complexifies long term monitoring. There is therefore a need for more sensitive and less prone to drift sensors in the time domain.
Dragonfly new piezoelectric strain gauges, with a resolution three order of magnitude over classic sensors, solve these issues and enable new applications. Dragonfly sensors consist in an extremely thin piezoelectric ceramic packaged in a flexible PCB and pre-wired with a SMA connector. The flexible sensor is glued with cyanoacrylate glue to the object to be studied.

This paper presents the implementation of Dragonfly sensors on a railroad bridge. With a single sensor, it has been possible to identify the bridge girder first two resonance using ambient noise, train passage loading for fatigue-life analysis, train passage reproducibility, and pedestrian passage on the walkway. A complete comparison with classic metallic foil strain gauge is detailed.
Elastodynamic shape derivative: focus on the sampling of a circular distribution
Authors:   University Paris-Saclay, Thomas Axel ,   University Paris-Saclay, Druet Tom ,   University Paris-Saclay, Recoquillay Arnaud ,   University Paris-Saclay, Chapuis Bastien
Published: June 2024

The detection and quantification of defects plays a crucial role in several industries. Assessing the severity enables to schedule maintenance appropriately, optimizing costs and reducing risks. Guided elastic waves are particularly suitable for Structural Health Monitoring applications on thin structures thanks to their long range and high sensitivity.
Guided wave tomography, based on an acoustic propagation hypothesis, has been studied over the last decade to quantitatively image defects in waveguides. The acoustic approximation of this family of methods allows fast computation and good estimation of defects for simple geometries such as plates or pipes. However, this hypothesis induces that a single guided mode is considered, without mode conversion, which limits its use to defects larger than two wavelengths in structures close to ideal waveguides. Hence the need of new imaging algorithms for smaller defects in less ideal structures such as pipes with welded supports. We present here an adaptation of the shape derivative method to ultrasounds in order to reconstruct surface defect on structures with complex geometries. The physical model used is the full elastodynamic problem. It allows taking into account all physical phenomena occuring during wave propagation. Iterative methods such as shape derivative are well known to give precise results but at a high computational cost as the method needs to solve the full elastodynamic problem at each iteration, and a sensitivity to local minima, which may prevent convergence toward the true defect. To suppress these two drawbacks, a spectral finite element method is used to reduce the computation and memory costs. The result of acoustic guided wave tomography is also used as a first guess to reduce the number of iterations and the sensitivity to local minima.
After explaining the principle of the shape derivative, validations on simulated and experimental data will be presented in this talk.
A living lab for Structural Health Monitoring at the Nibelungen Bridge Worms for Transfer Learning of Structural Dynamics
Authors:   BAM Federal Institute for Materials Research and Testing, Herrmann Ralf ,   BAM Federal Institute for Materials Research and Testing, Ramasetti Eshwar Kumar ,   BAM Federal Institute for Materials Research and Testing, Degener Sebastian ,   BAM Federal Institute for Materials Research and Testing, Hille Falk ,   BAM Federal Institute for Materials Research and Testing, Baeßler Matthias
Published: June 2024

The Nibelungen Bridge in Worms, Germany has been selected as a national demonstration structure for advanced non-destructive testing (NDT) and structural health monitoring concepts to extend the lifetime of civil structures and to optimize O&M actions. Parts of the research that involves this bridge as a demonstrator belong to the focus area program SPP100+. In this program, the bridges SHM System has been extended and combined with an additional setup of vibration sensors. The used digital smart sensor with pre-processing functions, the arrangement of the sensors at the structure and additional edge computing capability allows the investigation of transfer learning and other methods directly into the real structure. The living lab with seven triaxial accelerometers can be reconfigured in real-time and adjusted to the needs of AI models for classification. The comparison with the existing conventional SHM sensors has been made possible by hardware synchronization to the existing SHM System and collocating sensors at similar positions, so that a hardware exchange can be an investigated use-case for the transfer learning. During idle times, the system collects vibration data like a conventional SHM system.

Keywords: Structural Health Monitoring (SHM), Data acquisition, Sensor technology.
Non-destructive evaluation and Structural Health Monitoring of impact induced damage in composite structures using ultrasonic approaches and shearography
Authors:   University of Naples Federico II, Memmolo Vittorio ,   University of Naples Federico II, Maio Leandro ,   University of Naples Federico II, Monaco Ernesto ,   University of Naples Federico II, Ricci Fabrizio ,   National Council of Research (NCR), Vespini Veronica ,   National Council of Research (NCR), Graziano Fabiana ,   National Council of Research (NCR), Grilli Simonetta ,   National Council of Research (NCR), Coppola Sara ,   National Council of Research (NCR), Ferraro Pietro
Published: June 2024

Composite structures are massively used thanks to the high strength-to-weight ratio, making them ideal for lightweight and durable structures in aviation. However, Impact may arise at any time during lifetime on composite aerostructures and ensuring their structural integrity via fast inspection is essential to meet airworthiness criteria. Multimodal NDT (Non-Destructive Testing) inspection and characterization of composite structures is a critical process to verify the quality of the composite material and identify any defects. In this context, the paper shows a comparison of several techniques as nondestructive methods: shearography, laser ultrasonics and phased array ultrasonics. In particular, using shearography and laser ultrasonics as non-contact approaches can increase the inspection speed and reliability compared to the state of the art phased array ultrasonics, which is the standard approach used in the aerospace field. For this reason, a sample of interest to the aerospace industry is subject to different low velocity impacts which are characterized by different energies leading to barely visible damage. The results show the capabilities of both approaches in identifying small emerging defects along with the advantage and drawbacks of both methodologies through a critical review of performance thereof evaluating the parameters of their use. In addition, the same approaches are adopted on a stiffener composite plates and compared with continuous health monitoring approaches. In this regard, NDT approaches are demonstrated useful to enhance the SHM warnings.
Embedded passive guided wave tomography. Application to corrosion monitoring in multilayered pipes
Authors:   University Paris-Saclay, Recoquillay Arnaud ,   University Paris-Saclay, Gallet Antoine ,   University Paris-Saclay, Druet Tom ,   University Paris-Saclay, Albertini Julien ,   University Paris-Saclay, Duval Théo ,   University Paris-Saclay, Laffont Guillaume ,   University Paris-Saclay, Roussel Nicolas ,   University Paris-Saclay, Chapuis Bastien
Published: June 2024

Guided wave tomography techniques have been studied in the last decade to quantitatively image defects in waveguides such as plates or pipes. Recent developments based on the use of an autocalibration method, removing the need of a reference acquisition, enabled the absolute imaging of the structure and drastically reducing the sensitivity to environmental and operational conditions.
Another interesting feat for the structural health monitoring (SHM) is the use of passive methods relying on the measurement of the ambient noise, related to the use of the structure and its environment, and signal processing based on a cross-correlation to recover signals equivalent to the ones measured when a sensor is emitting. Such system prevents from the need of cumbersome circuitry and energy consumption related to wave emission. It also enables the sole use of sensors that cannot emit waves or only with a poor actuation such as optical fiber sensors. A SHM system relying solely on Fiber Bragg Gratings (FBGs) in optical fibers can then be designed. Several FBGs can be enscribed in a single optical fibers, reducing further the size and weight of the system. The optical fibers being resistant to harsh environments such as extreme temperatures or ionizing environments and having a good integration capability, such a system meets the needs for an industrial application.
This talk will hence show the principle of the passive guided wave tomography with embedded sensors in a structure. It will be exemplified in the case of a multi-layered structure, showing the possibility to apply this system in a large panel of cases.
Temperature-based damage detection for the commissioning dataset of the MX3D Bridge
Authors:   Imperial College London, Glashier Theo ,   University of Twente, Kromanis Rolands ,   Imperial College London, Buchanan Craig
Published: June 2024

Environmental and operational variations (EOV) remain a major obstacle for the successful transfer of structural health monitoring (SHM) techniques from laboratory experiments, such as cracked beam experiments, to full-scale structures, such as long-span bridges. With evidence that timely interventions significantly increase the service-life and reduce the overall life-cycle cost of ageing infrastructure, carrying out SHM in the presence of EOV, such as temperature, is therefore of high priority within the civil engineering community. The temperature-based measurement-interpretation (TB-MI) approach was developed to monitor the thermal response of an instrumented structure and detect changes linked to damage by minimising the impact that temperature variations have on anomaly detection techniques. The iterative regression-based thermal response prediction (IRBTRP) methodology is utilised in the TB-MI approach, and is trained on the healthy condition of the bridge to predict its undamaged response to temperature fluctuations. Thermal response predictions are compared to the measured response and deviations between them are indicative of a variation in the thermal response, due to structural change such as damage. Small deviations can then be identified using anomaly detection techniques, which results in earlier damage detection than if thermal response predictions had not been used. The TB-MI approach and the IRBTRP methodology have been applied to detect damage on the MX3D Bridge, the world’s first structure produced through metal additive manufacturing (i.e. 3D printing). This study demonstrates that the use of the TB-MI approach enables earlier and more widespread damage detection amongst multiple sensor groups, compared to when no temperature effects are considered. The adoption of the TB-MI approach can therefore greatly increase the reliability of and our reliance on SHM techniques for critical infrastructure.
Real-Time SHM of composite wing in a wind tunnel test using PCA-Based statistics
Authors:   Tel Aviv University, Bohbot Jonathan ,   Israel Aerospace Industries (IAI), Ofir Yoav ,   Israel Aerospace Industries (IAI), Ben-Simon Uri ,   Israel Aerospace Industries (IAI), Shoam Shay ,   Israel Aerospace Industries (IAI), Kressel Iddo ,   Italian Aerospace Research Centre (C.I.R.A.), Sikorski Bogdan ,   Italian Aerospace Research Centre (C.I.R.A.), Ciminello Monica ,   Piaggio Aerospace Industries, Apuleo Gianvito ,   Tel Aviv University, Tur Moshe
Published: June 2024

Structural health monitoring could prove particularly valuable in the case of composite aerostructures, where traditional inspection methods for critical components face challenges due to limited accessibility. An effective online SHM system can be achieved by employing fiber optic sensors, facilitating the integration of a 'fiber optic nervous system' into the composite structure, enabling the detection, measurement, and communication of a number of critical structural parameters. While the success of such system relies on selecting the right parameters to be monitored and the right sensing technology, it can also greatly benefit from on-board, real-time processing of the rather large amount of streamed data that ends with some data-driven real-time and continuous assessment as to the current state of the structure: Normal/Abnormal (based, of course, upon previously selected criteria).
In this work we demonstrate fiber-optic-based structural health monitoring of a healthy/damaged composite airplane wing in a wind tunnel test. Real-time health assessment is based on Principal Component Analysis (PCA).
We utilize two PCA-based measures, the Hotelling's T-squared distribution and the Q-statistics, as fault indicators. These statistics have been demonstrated to exhibit high sensitivity to structural anomalies. A PCA model accompanied by these statistics form a decision support tool that offers dependable real-time information regarding the health of the airborne structure, including the reporting of deviations from a prescribed flight envelope.
We demonstrate the performance of the method on real-time structural data gathered in a wind tunnel test, performed on a composite wing. Here, a PCA model was built based on the wing in its healthy state under a nominal loading regime. During the test, the system, processing the data collected from 10 FBGs, using a standard PC, successfully identified deviations, such as overload and initial damage.
Temperature behavior of long-span cable-stayed bridges by using unified analysis
Authors:   The Hong Kong Polytechnic University, Shan Yushi ,   The Hong Kong Polytechnic University, Xia Yong
Published: June 2024

Varying temperatures significantly affect long-span cable-stayed bridges. However, previous studies on the temperature behavior of bridges were either limited by finite sensors, failing to capture the accurate relation between the temperature and responses, or constrained to a “divide-and-conquer” strategy, requiring considerable manual intervention. This study develops a unified approach to the investigation of thermal behaviors of cable-stayed bridges by integrating heat-transfer analysis and structural analysis based on the same refined global 3D finite element model. A navigation channel bridge of Hong Kong‒Zhuhai‒Macao Bridge is used as the testbed. First, the heat-transfer analysis is conducted based on thermal boundary conditions carefully determined from real-time ambient temperature, wind, and solar radiation, which are tailored for each surface to reflect the influence of the geometric configuration and temperature difference. Then, the detailed temperature distribution results are automatically converted to thermal loads, and thermal elements are changed to structural elements to calculate the temperature-induced responses. Results show that temperature has a significant effect on the structural responses of the bridge. The calculated temperatures and temperature-induced responses are in good agreement with their measured counterparts, verifying the effectiveness of the proposed unified approach to investigating the temperature behavior of cable-stayed bridges.
Effective early-warning tool in detecting structural anomalies and preventing progressive collapse.
Authors:   Politecnico di Torino, Abbracciavento Lorenza ,   Politecnico di Torino, De Biagi Valerio
Published: June 2024

Addressing the challenges of structural monitoring, this research introduces a cutting-edge method for Structural Health Monitoring (SHM). The study investigates the correlation between deformation work patterns occurring during random damage scenarios. The method focuses on deformation work and related parameters as key indicators of structural health, showing promising results in the early detection of potential issues. Despite evidence indicating the robust nature of structurally complex systems in parallel arrangements, no specific study has been conducted in the case of mixed systems presenting series and parallel elements configurations . In this study, the structural response of a set of systems made of rods in series and parallel arrangements has been analyzed. Damage to the system, occurring randomly on elements, is considered, and an energy-based metric is employed.The purpose of the study is not only to explore the behaviour of rods working in the coupled mechanism, but also to identify relevant trends in the case of certain load patterns or geometrical configurations in order to design an effective targeted monitoring system. The findings highlight the feasibility of this approach in detecting structural anomalies, underscoring its potential as an effective early-warning system for preemptive maintenance and contributing to the improvement of structural integrity and safety.
Experimental Verification on Deep Learning based Monitoring Algorithms for Early Detection of Damage in Buried Pipelines
Authors:   Korea Research Institute of Standards and Science, Lee Sun-Ho ,   Korea Research Institute of Standards and Science, PARK Choon-su ,   Korea Research Institute of Standards and Science, Yoon Dong-Jin
Published: June 2024

Recent increases in buried pipeline damage accidents due to third-party interference have significantly heightened attention towards buried pipeline monitoring. Especially, as the sudden damage can lead to large-scale leakage, there is a necessity for preemptive response and maintenance. However, the application of a structural health monitoring approach is difficult, since the extensive network of buried pipelines, stretching over thousands of kilometers, exhibits diverse noise environments and propagation characteristics. As a result, challenges within the buried pipeline system frequently lead to damages being overlooked. In this study, introduces a deep learning-based pipeline damage monitoring algorithm, specifically designed to early detection of accidents caused by third-party interference. This algorithm integrates a CNN-based anomaly detection model, advanced signal processing for data preprocessing, and TDoA-based source localization. The training and test data set are the acquisition under completely independent conditions, which has been experimentally validate for applicability across various environments for buried pipelines. Moreover, both the training and test dataset acquisition were performed using accelerometers on in-service buried pipelines, each with diameters of 1,100 mm, 1,200 mm, and 2,200 mm, extending over lengths ranging from approximately 200 to 500 meters. Despite the independent conditions of the datasets, our study yielded over 95% accuracy in early detection, with the results being in good agreement with the actual excavate locations.
Acoustic Emission Monitoring of Bond Deterioration in GFRP-Reinforced Concrete Members: An Entropy-Based Approach
Authors:   Indian Institute of Technology Bombay, Rather Amer Iliyas ,   Indian Institute of Technology Bombay, Banerjee Sauvik ,   Indian Institute of Technology Bombay, Laskar Arghadeep
Published: June 2024

The primary objective of acoustic emission (AE) monitoring is to accurately detect imminent critical damage, preventing premature failure by analysing the dynamic evolution of AE parameters. This study introduces AE entropy, a distinctive parameter derived from Shannon's entropy, employed to monitor the bond degradation between glass fibre-reinforced polymer (GFRP) rebars and the concrete interface in GFRP-reinforced concrete elements. Characteristic AE parameters have been synchronously collected during flexural bond tests, and information entropy and weighted information entropy, composed of typical AE characteristic parameters based on Shannon’s information entropy theory in the time domain, have been defined. Findings indicate that, with increasing bond stress, the specimens exhibit a trend characterized by "decreasing, stabilizing, and then increasing" in both AE characteristic parameters and weighted information entropy. The information entropy value shows fluctuations during any damage associated with GFRP-concrete bond deterioration. At peak bond stress values, information entropy values experience a rapid surge, indicating significant deterioration. The evolution process of debonding between GFRP rebar and concrete, switching between a chaotic state that develops randomly and an organized state that produces in order, follows specific laws (disordered–ordered–disordered), corresponding well to AE characteristic parameters weighted information entropy. Moreover, entropy values, both individual and weighted, when correlated with bond stress and slip variations in the test specimens, distinctly outline and predict various stress transfer mechanisms between the GFRP bar and concrete. In summary, this research underscores the utility of AE entropy as a valuable tool for damage monitoring in GFRP-reinforced structures. Its ability to capture microstructural deformations positions AE entropy as a promising candidate for improving critical damage identification and preventing premature failure