Accurate Structural Health Monitoring Research Articles

Barely visible impact damage detection in composite structures using deep learning networks with varying complexities

Visual inspection is one of the most common non-destructive testing (NDT) methods that offers a fast evaluation of surface damage in aerospace composite structures. However, it is highly dependent on human-related factors and may not detect barely visible impact damage (BVID). In this research, low velocity impact tests with different energy levels are conducted on two groups of composite panels, namely ‘reference’ and ‘sensor-integrated’ samples. Then, the results of impact tests, together with C-scan and visual inspection images, are analysed to define the BVID range and create an original image dataset. Next, four different deep learning models are trained, validated and tested to capture the BVID only from the images of the impacted and non-impacted surfaces. The results show that all four networks can learn and detect BVID quite well, and the sensor-integrated samples reduce the training time and improve the accuracy of deep learning models. ResNet outperforms other networks with the highest accuracy of 96.2% and 98.36% on the back-face of reference and sensor-integrated samples, respectively. The proposed damage recognition method can act as a fast, inexpensive and accurate structural health monitoring tool for composite structures in real-life applications.

High-Sensitivity RFID Sensor for Structural Health Monitoring.

Structural health monitoring (SHM) is crucial for ensuring operational safety in applications like pipelines, tanks, aircraft, ships, and vehicles. Traditional embedded sensors have limitations due to expense and potential structural damage. A novel technology using radio frequency identification devices (RFID) offers wireless transmission of highly sensitive strain measurement data. The system features a thin, flexible sensor based on an inductance-capacitance (LC) circuit with a parallel-plate capacitance sensing unit. By incorporating tailored cracks in the capacitor electrodes, the sensor's capacitor electrodes become highly piezoresistive, modifying electromagnetic wave penetration. This unconventional change in capacitance shifts the resonance frequency, resulting in a wireless strain sensor with a gauge factor of 50 for strains under 1%. The frequency shift is passively detected through an external readout system using simple frequency sweeping. This wire-free, power-free design allows easy integration into composites without compromising structural integrity. Experimental results demonstrate the cracked wireless strain sensor's ability to detect small strains within composites. This technology offers a cost-effective, non-destructive solution for accurate structural health monitoring.

Experimental verification for a mechanics‐based digital cloning method predicting structural performance and damage

AbstractThis paper introduces a novel digital twin modelling framework combining accurate structural health monitoring (SHM) results delivered by hysteresis loop analysis (HLA) with the basis function modelling to predict structural damage and responses of earthquake‐affected structures under future seismic events. The measured data sets of a 12‐storey 1/10 scale reinforced concrete (RC) structure shaken by three damaging base excitations (Events 1, 2 and 3) using a shake table has been employed to validate the performance of the introduced predictive framework. The results show that the digital twin created under the first base excitation (Event 1) predicts the inter‐storey displacement responses of the test structure due to the second and third excitations (Events 2 and 3) with an average correlation coefficient of ∼0.88 and ∼0.94, respectively. The inter‐storey drift ratio (IDR) values predicted by the digital twin also result in the same seismic performance delivered by the measured values, making the introduced framework a promising tool for assessing structural collapse and subsequent financial risk using incremental dynamic analysis (IDA). Moreover, the maximum difference of 5% between the identified and predicted stiffness values illustrates the robust and accurate performance of the proposed framework in diagnosing structural damage in highly nonlinear and damaged structures.

Structural performance and damage prediction using a novel digital cloning technique

This paper presents a novel, mechanics-based framework to create digital twins for earthquake-affected pinched structures, like reinforced concrete framed buildings. It uses robust and accurate structural health monitoring (SHM) results delivered by the hysteresis loop analysis method as an input to create digital twin models to predict nonlinear dynamic responses of previously damaged structures under potential future seismic events. Method validation is implemented using unique real-world data from the Bank of New Zealand (BNZ) building in Wellington, New Zealand, which experienced severe structural damage due to three earthquakes (Events 1, 2, and 3) between 2013 and 2016.Results show the digital twin derived from the SHM results of Event 1 can predict the inter-story displacement of the BNZ building for Events 2 and 3 with average correlation coefficients of ∼0.95 and ∼0.97 between predicted and measured responses, respectively. Moreover, the maximum difference between the measured and predicted peak values of inter-story displacements was 13 mm, a negligible difference in inter-story drift ratio (IDR). These results were accurate and consistent for all stories. Finally, the accuracy of this framework in capturing IDR values makes it a promising tool for assessing potential future structural collapse risk and its consequent financial risks using well-known incremental dynamic analysis methods.

Evaluation of the sparse reconstruction and the delay-and-sum damage imaging methods for structural health monitoring under different environmental and operational conditions

In this paper, the performance of the sparse reconstruction (SR) and the delay-and-sun (DAS) methods for damage localization, were evaluated for various environmental and operational conditions, both numerically and experimentally. To assess these damage localization methods, a methodology based on the Taguchi method was used to make the experimental design, and a modified performance-index was defined to represent the quality of reconstructed images. Then, the robustness and the accuracy of each method, in a well-defined performance region relevant to in-service aerospace structures, were investigated using the Taguchi and analysis of variance methods. It was concluded that for the defined conditions, the robustness of the delay and sum method is better than the sparse reconstruction method for uncontrolled factors. However, the sparse reconstruction method is more robust to poor baseline subtraction than the delay and sum method, while the delay and sum method was more robust to factors that lead to a model mismatch. These results provide additional insight into the design of reliable accurate structural health monitoring systems. The outcomes of this work can be used in future reaserch into SHM imaging techniques.

A new pinched nonlinear hysteretic structural model for automated creation of digital clones in structural health monitoring

Optimizing risk treatment of structures in post-event decision-making is extremely difficult due to the lack of information on building damage/status after an event, particularly for nonlinear structures. This work develops an automated, no human intervention, modeling approach using structural health monitoring results to create accurate digital building clones of nonlinear structures for collapse prediction assessment and optimized decision-making. Model-free hysteresis loop analysis structural health monitoring method provides accurate structural health monitoring results from which model parameters of a nonlinear computational foundation model are identified. A new identifiable nonlinear smooth hysteretic model capturing essential structural dynamics and deterioration is developed to ensure robust parameter identification using support vector machines. Method performance is validated against both numerical and experimental data of a scaled 12-story reinforced concrete nonlinear structure. Results of numerical validation show an average error of 1.5% across 18 structural parameters from hysteresis loop analysis and an average error of 2.0% over 30 identified model parameters from support vector machines in the presence of 10% added root-mean-square noise. Validation using experimental data of the scale test reinforced concrete structure also shows a good match of identified hysteresis loop analysis and predicted nonlinear stiffness changes using the digital clones created with an average difference of 1.4%. More importantly, the predicted response using the digital clones for the highly nonlinear pinched hysteretic behavior matches the measured response well, with the average correlation coefficient Rcoeff = 0.92 and average root-mean-square error of 4.6% across all cases. The overall approach takes structural health monitoring from a tool providing retrospective damage data into automated prospective prediction analysis by “cloning” the structure using computational modeling, which in turn allows optimized decision-making using existing risk analyses and tools.

Optimized damage identification in CFRP plates by reduced mode shapes and GA-ANN methods

Delamination is one of the most common failure mode in laminated composites that leads the separation along the interfaces of the layers. The structural performance can be significantly affected by this degradation. Such damages are not always visible on the surface and could potentially lead to catastrophic structural failures. The existence of delamination alters the vibration characteristics of the laminated structures, so if they are detected and measured previously, they can be used as indicator for quantifying health and the potential risk of catastrophic failures. To ensure structural performance and integrity, accurate Structural Health Monitoring (SHM) is crucial. In this study, an optimized methodology for delamination identification on laminated composite plates involving the use of reduced mode shapes and computational tools, i.e., Genetic Algorithm (GA) and Artificial Neural Networks (ANN) is performed. In a first step, the sensor distribution on the surface of the structure was optimized using Fisher Information Matrix (FIM) criteria. After, GA and ANN were applied in order to identify and predict delamination location. A feed-forward based neural network is used in order to detect damage on the laminated plate using data obtained from Finite Element Analysis (FEA). The present methodology identifies damage localization in structures and also quantifies damage severity. The applicability of the technique is demonstrated on laminated plates and results are compared with numerical algorithms. This paper shows the effectiveness of GA and ANN as tools for delamination damage identification problem. The algorithms in their inverse formulations are capable of predicting accurately delamination position in plates-like structures.

Wireless monitoring of scour and re-deposited sediment evolution at bridge foundations based on soil electromagnetic properties

Hydraulic structures constitute the most vulnerable elements of transportation infrastructure. Recent increases in precipitation have resulted in severe and more frequent flash flooding incidents. This has put bridges over waterways at higher risk of failure due to scour. This study presents a new sensor for measuring scour depth variation and sediment deposition processes in the vicinity of the foundations to underpin systems for early warning of impending structural failure. The monitoring system consists of a probe with integrated electromagnetic sensors designed to detect changes in the dielectric permittivity of the surrounding bridge foundation. The probe is equipped with a wireless interface and was evaluated to assess its ability to detect scour and sediment deposition in various soil types and under temperature and water salinity conditions that would commonly occur in a practical installation environment. A novel methodology is also developed enabling discrimination between in situ and re-deposited sediment delivering vital information about the load bearing capacity of the foundation. The experimental approach was validated using ‘static’ scour simulations and real-time open channel flume experiments. Results indicate that the sensor is highly sensitive to underwater bed level variations and can provide an economical and accurate structural health monitoring alternative to existing instruments.

광섬유 브래그 격자 센서를 이용한 보 구조물의 3차원 형상 추정

Deflection and deformation occur easily in structures with long length, such as bridges and pipelines. Shape monitoring is required for ensuring their structural health. A fiber Bragg grating (FBG) sensor can be used for monitoring a large-scale structure because of its advantage of multiplexing. In this study, FBG sensors were used for monitoring a composite beam structure, and its strains were measured at multiple points. Thereafter, a shape estimation technique based on the strains was studied. Particularly, a three-dimensional shape estimation technique was proposed for accurate structural health monitoring. A simple experiment was conducted to verify the performance of the shape estimation technique. The result revealed that the estimated shape of the composite beam structure was in agreement with the actual shape obtained after the deformation of the specimen. Additionally, the deflection at a specific point was verified by comparing the estimated and actual deformations measured using a micrometer.

Structural health monitoring of an asymmetrical SMA reinforced composite using embedded FBG sensors

Embedded actuator and sensor technology provides accurate structural health monitoring and proper structural response of a structure in any harsh servicing situation. This paper describes the fabrication of a smart composite by embedding shape memory alloy (SMA) wires and fibre Bragg grating (FBG) sensors into a glass fabric reinforced polymeric composite. Mechanical performances of the composite under martensitic and austenitic stages of the SMA wires were studied, and its natural frequencies were also measured accordingly. The result shows that the shift of the natural frequency arises from temperature change, thus changing the mechanical properties of the SMA wires. The changes of strain, stress, curvature, and damping ratio were predicted from an asymmetrical lamination model. It was found that this model demonstrates certain attractive effects, including mechanical properties, the change of shape, and the natural frequency upon activation of the SMA wires.