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The deployment of machine learning (ML) and deep learning (DL) in structural health monitoring (SHM) faces multiple challenges. Foremost among these is the insufficient availability of extensive high-quality data sets essential for robust training. Within SHM, high-quality data is defined by its accuracy, relevance, and fidelity in representing real-world structural scenarios (pristine as well as damaged). Although methods like data augmentation and creating synthetic data can add to datasets, they frequently sacrifice the authenticity and true representation of the data. Sharing real-world data encapsulating true structural and anomalous scenarios offers promise. However, entities are often reluctant to share raw data, given the potential extraction of sensitive information, leading to trust issues among collaborating entities.
Our study introduces a novel methodology leveraging Federated Learning (FL) to navigate these challenges. Within the FL framework, models are trained in a decentralized manner across different entities, preserving data privacy. In our research, we simulated several scenarios and compared them to traditional local training methods. Employing guided wave (GW) datasets, we distributed the data among different parties (clients) using IID (independent, identically distributed or in other words, statistically identical) mini batches of dataset, as well as non-IID configurations. This approach mirrors real-world data distribution among varied entities, such as hydrogen refueling stations.
In our methodology, the initial round involves individualized training for each client using their unique datasets . Subsequently, the model parameters are sent to the FL server, where they are averaged to construct a global model. In the second round, this global model is disseminated back to the clients to aid in predictive tasks. This iterative process continues for several rounds to reach convergence.
Our findings distinctly highlight the advantages of FL over localized training, evidenced by a marked improvement in prediction accuracy . This research underscores the potential of FL in GW-based SHM, offering a remedy to similar challenges tied to data scarcity in other SHM approaches and paving the way for a new era of collaborative, data-centric monitoring systems.
A semi-automatic thermographic procedure for the assessment of the welded area of resistance projection welded joints has been developed. Currently, to assess the quality of RPW joints destructive tests are used and the more commonly used non-destructive technique is the ultrasonic one. The possibility for a quantitative evaluation of the welded area by thermographic technique has been proved by means of an innovative procedure applied on steel RPW joints with ‘as it’ surface conditions. Measurements obtained by thermography and ultrasound have been compared, to verify the developed procedure.
Simulation-Assisted Augmentation of Missing Wedge and Region-of-Interest Computed Tomography Data
(2024)
This study reports a strategy to use sophisticated, realistic X-ray Computed Tomography (CT) simulations to reduce Missing Wedge (MW) and Region-of-Interest (RoI) artifacts in FBP (Filtered Back-Projection) reconstructions. A 3D model of the object is used to simulate the projections that include the missing information inside the MW and outside the RoI. Such information augments the experimental projections, thereby drastically improving the reconstruction results. An X-ray CT dataset of a selected object is modified to mimic various degrees of RoI and MW problems. The results are evaluated in comparison to a standard FBP reconstruction of the complete dataset. In all cases, the reconstruction quality is significantly improved. Small inclusions present in the scanned object are better localized and quantified. The proposed method has the potential to improve the results of any CT reconstruction algorithm.
Until the 1980s radiography was used to inspect civil structures in case of special demands and showed a much better resolution than other NDT techniques. However, due to safety concerns and cost issues, this method is almost never used anymore. Meanwhile, non-destructive techniques such as ultrasound or
radar have found regular, successful practical application but sometimes suffer from limited resolution and accuracy, imaging artefacts or restrictions in detecting certain features when applied to reinforced or prestressed concrete inspection.
Muon tomography has received much attention recently. Muons are particles generated naturally by cosmic rays in the upper atmosphere and pose no risk to humans. Novel detectors and tomographic imaging algorithms have opened new fields of application, mainly in the nuclear sector, but also in spectacular cases such as the Egyptian pyramids.
As a first step towards practical application in civil engineering and as a proof of concept we used an existing system to image the interior of a reference reinforced 600 kg concrete block. Even with a yet not optimized setup for this kind of investigation, the muon imaging results have been at least of similar quality compared to ultrasonic and radar imaging, potentially even better. Recently, the research was expanded to more realistic testing problems such as the detection of voids in certain structural elements. However, before practical implementation, more robust, mobile, and affordable detectors would be required as well as user-Friendly imaging and simulation software.
The built infrastructure ages and requires regular inspection and, when in doubt, monitoring. To ensure that older concrete bridges showing signs of deterioration can be used safely, several innovative monitoring tools have been introduced, including but not limited to optical, fiber-optic, or acoustic emission techniques. However, there are gaps in the portfolio. A sensing technique that covers a wide range of damage scenarios and larger volumes, while still being sensitive and specific, would be beneficial.
For about 15 years, research has been conducted on ultrasonic monitoring of concrete structures that goes beyond the traditional ultrasonic pulse velocity test (PV test), mostly using a very sensitive data evaluation technique called coda wave interferometry. At BAM we have developed sensors and instrumentation specifically for this method.
We have instrumented a 70-year-old, severely damaged prestressed concrete bridge in Germany in addition to a commercial monitoring system. We have now collected data for almost 3 years. We can show that we can provide information about the stress distribution in the bridge. We have also been able to confirm that there has been no significant additional damage to the bridge since the installation.
X-rays without X-rays: Can muon tomography provide pictures from within concrete and other objects?
(2024)
Until the 1980s radiography was used to inspect civil structures in case of special demands and showed a much better resolution than other NDT techniques. However, due to safety concerns and cost issues, this method is almost never used anymore. Meanwhile, non-destructive techniques such as ultrasound or
radar have found regular, successful practical application but sometimes suffer from limited resolution and accuracy, imaging artefacts or restrictions in detecting certain features when applied to reinforced or prestressed concrete inspection.
Muon tomography has received much attention recently. Muons are particles generated naturally by cosmic rays in the upper atmosphere and pose no risk to humans. Novel detectors and tomographic imaging algorithms have opened new fields of application, mainly in the nuclear sector, but also in spectacular cases such as the Egyptian pyramids.
As a first step towards practical application in civil engineering and as a proof of concept we used an existing system to image the interior of a reference reinforced 600 kg concrete block. Even with a yet not optimized setup for this kind of investigation, the muon imaging results have been at least of similar quality compared to ultrasonic and radar imaging, potentially even better. Recently, the research was expanded to more realistic testing problems such as the detection of voids in certain structural elements. However, before practical implementation, more robust, mobile, and affordable detectors would be required as well as user-Friendly imaging and simulation software.
The talk also discusses other applications , such as volcanology, mining and geothermal exploration.