Smart monitoring of aeronautical composites plates based on electromechanical impedance measurements and artificial neural networks

This paper presents a structural health monitoring (SHM) method for in situ damage detection and localization in carbon fiber reinforced plates (CFRPs). The detection is achieved using the electromechanical impedance (EMI) technique employing piezoelectric transducers as high-frequency modal sensors. Numerical simulations based on the finite element method are carried out so as to simulate more than a hundred damage scenarios. Damage metrics are then used to quantify and detect changes between the electromechanical impedance spectrum of a pristine and damaged structure. The localization process relies on artificial neural networks (ANNs) whose inputs are derived from a principal component analysis of the damage metrics. It is shown that the resulting ANN can be used as a tool to predict the in-plane position of a single damage in a laminated composite plate

Correlating low energy impact damage with changes in modal parameters: diagnosis tools and FE validation

This paper presents a basic experimental technique and simplified FE based models for the detection, localization and quantification of impact damage in composite beams around the BVID level. Detection of damage is carried out by shift in modal parameters. Localization of damage is done by a topology optimization tool which showed that correct damage locations can be found rather efficiently for low-level damage. The novelty of this paper is that we develop an All In One (AIO) package dedicated to impact identification by modal analysis. The damaged zones in the FE models are updated by reducing the most sensitive material property in order to improve the experimental/numerical correlation of the frequency response functions. These approximate damage models(in term of equivalent rigidity) give us a simple degradation factor that can serve as a warning regarding structure safety

Smart FRP Composite Sandwich Bridge Decks in Cold Regions

INE/AUTC 12.0

A Review of Structural Health Monitoring Techniques as Applied to Composite Structures.

Structural Health Monitoring (SHM) is the process of collecting, interpreting, and analysing data from structures in order to determine its health status and the remaining life span. Composite materials have been extensively use in recent years in several industries with the aim at reducing the total weight of structures while improving their mechanical properties. However, composite materials are prone to develop damage when subjected to low to medium impacts (ie 1 – 10 m/s and 11 – 30 m/s respectively). Hence, the need to use SHM techniques to detect damage at the incipient initiation in composite materials is of high importance. Despite the availability of several SHM methods for the damage identification in composite structures, no single technique has proven suitable for all circumstances. Therefore, this paper offers some updated guidelines for the users of composites on some of the recent advances in SHM applied to composite structures; also, most of the studies reported in the literature seem to have concentrated on the flat composite plates and reinforced with synthetic fibre. There are relatively fewer stories on other structural configurations such as single or double curve structures and hybridised composites reinforced with natural and synthetic fibres as regards SHM

Damage identification in bridge structures : review of available methods and case studies

Bridges are integral parts of the infrastructure and play a major role in civil engineering. Bridge health monitoring is necessary to extend the life of a bridge and retain safety. Periodic monitoring contributes significantly in keeping these structures operational and extends structural integrity. Different researchers have proposed different methods for identifying bridge damages based on different theories and laboratory tests. Several review papers have been published in the literature on the identification of damage and crack in bridge structures in the last few decades. In this paper, a review of literature on damage identification in bridge structures based on different methods and theories is carried out. The aim of this paper is to critically evaluate different methods that have been proposed to detect damages in different bridges. Different papers have been carefully reviewed, and the gaps, limitations, and superiority of the methods used are identified. Furthermore, in most of the reviews, future applications and several sustainable methods which are necessary for bridge monitoring are covered. This study significantly contributes to the literature by critically examining different methods, giving guidelines on the methods that identify the damages in bridge structures more accurately, and serving as a good reference for other researchers and future works

Baseline free structural health monitoring using modified time reversal method and wavelet spectral finite element models

The Lamb wave based, non-contact damage detection techniques are developed using the Modified Time Reversal (MTR) method and the model based inverse problem approach. In the first part of this work, the Lamb wave-based MTR method along with the non-contacting sensors is used for structural damage detection. The use of non-contact measurements for MTR method is validated through experimental results and finite element simulations. A novel technique in frequency-time domain is developed to detect linear damages using the MTR method. The technique is highly suitable for the detection of damages in large metallic structures, even when the damage is superficial, and the severity is low. In this technique, no baseline data are used, and all the wave motion measurements are made remotely using a laser vibrometer. Additionally, this novel MTR based technique is not affected due to changes in the material properties of a structure, environmental conditions, or structural loading conditions. Further, the MTR method is improved for two-dimensional damage imaging. The damage imaging technique is successfully tested through experimental results and finite element simulations. In the second part of this work, an inverse problem approach is developed for the detection and estimation of major damage types experienced in adhesive joints. The inverse problem solution is obtained through an optimization algorithm wherein the objective function is formulated using the Lamb wave propagation data. The technique is successfully used for the detection/estimation of cohesive damages, micro-voids, debonds, and weak bonds. Further, the inverse problem solution is separately obtained through a fully connected artificial neural network. The neural network is trained using the Lamb wave propagation data generated from Wavelet Spectral Finite Element (WSFE) model which is computationally much faster than a conventional finite element model. This inverse problem approach technique requires a single point measurement for the inspection of the entire width of the adhesive joint. The proposed technique can be used as an automated quality assurance tool during the manufacturing process, and as an inspection tool during the operational life of adhesively bonded structures

Multi-damage detection in composite space structures via deep learning

The diagnostics of environmentally induced damages in composite structures plays a critical role for ensuring the operational safety of space platforms. Recently, spacecraft have been equipped with lightweight and very large substructures, such as antennas and solar panels, to meet the performance demands of modern payloads and scientific instruments. Due to their large surface, these components are more susceptible to impacts from orbital debris compared to other satellite locations. However, the detection of debris-induced damages still proves challenging in large structures due to minimal alterations in the spacecraft global dynamics and calls for advanced structural health monitoring solutions. To address this issue, a data-driven methodology using Long Short-Term Memory (LSTM) networks is applied here to the case of damaged solar arrays. Finite element models of the solar panels are used to reproduce damage locations, which are selected based on the most critical risk areas in the structures. The modal parameters of the healthy and damaged arrays are extracted to build the governing equations of the flexible spacecraft. Standard attitude manoeuvres are simulated to generate two datasets, one including local accelerations and the other consisting of piezoelectric voltages, both measured in specific locations of the structure. The LSTM architecture is then trained by associating each sensed time series with the corresponding damage label. The performance of the deep learning approach is assessed, and a comparison is presented between the accuracy of the two distinct sets of sensors: accelerometers and piezoelectric patches. In both cases, the framework proved effective in promptly identifying the location of damaged elements within limited measured time samples

Artificial Intelligence in Civil Infrastructure Health Monitoring—historical Perspectives, Current Trends, and Future Visions

Over the past 2 decades, the use of artificial intelligence (AI) has exponentially increased toward complete automation of structural inspection and assessment tasks. This trend will continue to rise in image processing as unmanned aerial systems (UAS) and the internet of things (IoT) markets are expected to expand at a compound annual growth rate of 57.5% and 26%, respectively, from 2021 to 2028. This paper aims to catalog the milestone development work, summarize the current research trends, and envision a few future research directions in the innovative application of AI in civil infrastructure health monitoring. A blow-by-blow account of the major technology progression in this research field is provided in a chronological order. Detailed applications, key contributions, and performance measures of each milestone publication are presented. Representative technologies are detailed to demonstrate current research trends. A road map for future research is outlined to address contemporary issues such as explainable and physics-informed AI. This paper will provide readers with a lucid memoir of the historical progress, a good sense of the current trends, and a clear vision for future research