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With the increasing application of artificial intelligence (AI) techniques in the field of structural health monitoring (SHM), there is a growing interest in explaining the decision-making of the black-box models in deep learning-based SHM methods. In this work, we take explainability a step further by using it to improve the performance of AI models. In this work, the results of explainable artificial intelligence (XAI) algorithms are used to reduce the input size of a one-dimensional convolutional neural network (1D-CNN), hence simplifying the CNN structure. To select the most accurate XAI algorithm for this purpose, we propose a new evaluation method, feature sensitivity (FS). Utilizing XAI and FS, a reduced dimension 1D-CNN regression model (FS-X1D-CNN) is proposed to locate and predict the torque of loose bolts in a 16-bolt connected aluminum plate under varying temperature conditions. The results were compared with 1D CNN with raw input vector (RI-1D-CNN) and deep autoencoders-1D-CNN (DAE-1D-CNN). It is shown that FS-X1D-CNN achieves the highest prediction accuracy with 5.95 mm in localization and 0.54 Nm in torque prediction, and converges 10 times faster than RI-1D-CNN and 15 times faster than DAE-1D-CNN, while only using a single lamb wave signal path., Structural Integrity & Composites, Group Yue, Group Groves

With the improvements in computational power and advances in chip and sensor technology, the applications of machine learning (ML) technologies in structural health monitoring (SHM) are increasing rapidly. Compared with traditional methods, deep learning based SHM (Deep SHM) methods are more efficient and have a higher accuracy. However, due to the black box nature of deep learning, the trained models are usually difficult to interpret, which blocks their practical application. Therefore, it is of great importance to develop explainable artificial intelligence (XAI) methods to understand the internal decision-making mechanisms of damage classification in Deep SHM. In this paper, a novel XAI algorithm named Deep Gradient-weighted Class Activation Mapping (Deep Grad CAM) is proposed by combining the existing method Grad CAM with the convolutional neural network (CNN) deconvolution mechanism. In this paper, Deep Grad CAM is used to interpret a one-dimensional convolutional neural network trained to detect bolt loosening based on guided wave propagation. The interpretation performance of Deep Grad CAM is compared with Grad CAM, and their performances are quantified using Infidelity. The results show that the Infidelity of Deep Grad CAM is much smaller than that of Grad CAM, indicating significant improvements in explanation accuracy and reliability., Structural Integrity & Composites, Group Yue, Group Groves

In this paper we execute a complex test campaign to develop a novel methodology for the Remaining Useful Life (RUL) estimation of complex multi-stiffened composite aeronautical panels utilizing Machine Learning models trained with Structural Health Monitoring (SHM) data from hierarchically simpler elements, i.e., single-stiffened panels. Distributed Fiber Optical sensors (DFOS) are employed to monitor the panels’ behavior undergoing variable amplitude compression-compression fatigue after multiple impacts. A data processing methodology is first applied to the DFOS data, to both alleviate the effect of the variable loading conditions on the monitored strain and ease the computational burden. In this upscaling endeavor, an advanced strain-based Health Indicator (HI) based on Genetic algorithms, created and validated on the single-stiffened panel data, is utilized as the prognostic feature for the RUL estimations of the multi-stiffened panels. The HI displays favorable characteristics in terms of monotonicity and prognosability which are highly desirable for more accurate RUL estimations. For the prognostic task, standard machine learning models are trained using the historical degradation data of the single-stiffened panels and a similarity analysis is performed to enhance the accuracy when predicting the RUL of the multi-stiffened panels. Despite the increased structural complexity of the multi-stiffened panels, we demonstrate that the RUL is able to be predicted with reasonable accuracy. The present work paves the road for upscaling and applying prognostic methodologies to more complex structures beyond simple coupons or generic elements., Structural Integrity & Composites

Distributed Optical Fiber Sensors (DOFS) show several inherent benefits with respect to conventional strain-sensing technologies and represent a key technology for Structural Health Monitoring (SHM). Despite the solid motivation behind DOFS-based SHM systems, their implementation for real-time structural assessment is still unsatisfactory outside academia. One of the main reasons is the lack of rigorous methodologies for uncertainty quantification, which hinders the performance assessment of the monitoring system. The concept of Probability of Detection (POD) should function as the guiding light in this process, but precautions must be taken to apply this concept to SHM, as it has been originally developed for Non-Destructive Evaluation techniques. Although DOFS have been the object of numerous studies, a well-established methodology for their performance evaluation in terms of PODs is still missing. In the present work, the concept of Probability of Delamination Detection (POD2) is proposed for a DOFS network; Carbon Fiber-Reinforced Polymers (CFRP) Double-Cantilever Beam (DCB) specimens equipped with DOFS have been tested under static loading, and the strain patterns along with the relative observed delamination size have been collected to generate an adequate database for the POD analysis, suggesting a reference methodology to quantify the performance of DOFS for delamination detection., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Structural Integrity & Composites

Optical fiber sensors (OFSs) represent an efficient sensing solution in various structural health monitoring (SHM) applications. However, a well-defined methodology is still missing to quantify their damage detection performance, preventing their certification and full deployment in SHM. In a recent study, the authors proposed an experimental methodology to qualify distributed OFSs using the concept of probability of detection (POD). Nevertheless, POD curves require considerable testing, which is often not feasible. This study takes a step forward, presenting a model-assisted POD (MAPOD) approach for the first time applied to distributed OFSs (DOFSs). The new MAPOD framework applied to DOFSs is validated through previous experimental results, considering the mode I delamination monitoring of a double-cantilever beam (DCB) specimen under quasi-static loading conditions. The results show how strain transfer, loading conditions, human factors, interrogator resolution, and noise can alter the damage detection capabilities of DOFSs. This MAPOD approach represents a tool to study the effects of varying environmental and operational conditions on SHM systems based on DOFSs and for the design optimization of the monitoring system., Structural Integrity & Composites

During the service of composite airframes, damage initiates and accumulates due to the manufacturing imperfections, impact damage and cyclic loadings, leading to the degradation in its load-bearing capacity. The nature of the degradation process is complicated due to the multi-mode damage propagation and complexity in the structural details of airframes. In the condition-based health management of airframe structures, the degradation is expressed in the concept of remaining useful life (RUL). Online prognostic health management is an emerging field dedicated to the timely prediction of RUL using onboard sensors. This work presents a mechanics-informed approach to the prognosis of a typical airframe element, stiffened CFRP composite panel, under compression-compression fatigue. The fatigue degradation of axial stiffness is monitored by Lamb wave velocity and utilised for online RUL prediction via particle filter., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Structural Integrity & Composites

To move towards a condition-based maintenance practice for aircraft structures, design of reliable health management methodologies is required. Development of diagnostic methodologies is commonly realised on simplified sample structures with assumptions that methodologies can be adapted for application to realistic aircraft structures under in-service conditions. Yet such actual applications are not conducted. In this work, we study the development of diagnostic methodologies to training structures and their application to dissimilar testing structures. A heterogeneous population is considered, consisting of single-stiffener composite panels for methodology development and training and a multi-stiffener composite panel for application and testing. Characteristics as its composite material, lay-up, and temperature condition are constant while topologies and applied loads differ between the dissimilar structures. Damage in the structural panels is monitored on multiple diagnostic levels using a variety of structural health monitoring (SHM) techniques, including acoustic emission and distributed strain sensing. Specifically, we develop diagnostic methods for localising and monitoring disbond growth after impact using strain data collected during fatigue testing of multiple single-stiffener panels and apply these for disbond monitoring in an upscaled version of a multi-stiffener panel. In this manner, this study aids in the maturement and application of SHM methodologies to realistic aircraft structures., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Structural Integrity & Composites

The application of structural health monitoring (SHM) in composite airframe structural elements under long-term realistic fatigue loading needs to consider the structural behavior on the global level, which is an intricate task. The overall structural stiffness is a key design parameter for composite structures and the stiffness degradation under fatigue loading is closely related to the damage accumulation and failure mechanism which can be used as an indicator for the structural degradation. Therefore, this paper investigates the use of guided waves in axial stiffness degradation estimation for stiffened carbon fiber reinforced polymer (CFRP) composite panels under post-buckling compression-compression (C-C) fatigue loads. Impacted or artificially debonded stiffened composite panels are tested under fatigue until failure and guided waves are acquired using a network of piezoelectric (PZT) sensors at fixed cycle intervals. The guided wave phase velocity along the loading direction is extracted to estimate the axial stiffness degradation with the consideration of mode conversion and failure of PZT sensors. The estimated stiffness of five stiffened composite panels matches well with the stiffness calculated from the load–displacement curves. The estimated stiffness is also assessed using prognostic performance metrics and shows good potential for being used as a health indicator for prognostic purposes., Structural Integrity & Composites

Machine learning (ML) methods for the structural health monitoring (SHM) of composite structures rely on sufficient domain knowledge as they typically demand to extract damage-sensitive features from raw data before training the ML model. In practice, prior knowledge is not available in most cases. Deep learning (DL) methods, on the other hand, can obtain higher-level features from raw input data and have proven superior in several applications. This paper proposes a Convolutional Neural Network (CNN) based approach for the delamination prediction in CFRP double cantilever beam (DCB) specimens using raw local array strain measurements via distributed optical fiber sensors. The conventional CNN architecture is modified to perform regression, as the delamination size is a continuous value. 1D and 2D CNN architectures are deployed and compared and different techniques are exploited to encode 1D spatial strain pattern series as 2D images. Raw strain patterns collected during static testing are used to train the CNNs, while testing is performed on unseen raw fatigue strain patterns, showing the CNN ability to automatically extract discriminative features from the non-pre-processed static strain pattern-based signals that generalize to raw fatigue signals as well. This strategy has the potential to reduce fatigue testing expenditures while also shortening the time required to gather training data., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Structural Integrity & Composites

Despite the promising application of Distributed Optical Fiber Sensors (DOFS) in monitoring damage in composite structures, their implementation outside academia is still unsatisfactory due to the lack of a systematic approach to assessing their damage detection performance. The existing tool developed for non-destructive evaluation, Probability of Detection (POD) curves, needs to be adapted for structural health monitoring applications to account for spatial and temporal dependency. Damage detection performance with DOFS is deeply related to the inherent variability sources of the system, the strain transfer properties of the optical fiber, and the loading conditions, which determine the damage-induced strain on the structure. This paper establishes a systematic approach based on the Length at Detection (LaD) method to qualify DOFS for damage detection in composites under different scenarios. Specifically, this study considers two DOFS with different strain transfer properties for monitoring delamination in carbon fiber reinforced polymers double-cantilever beam specimens under mode I quasi-static and fatigue loading. The POD curves derived from the LaD method confirm that this methodology can quantify the change in the detection performance due to the DOFS type and the loading conditions. The study also proposes a practical solution to compare POD curves obtained with different sample sizes, by introducing the concept of virtual specimens to simulate the lower confidence bound convergence., Structural Integrity & Composites

Health management methodologies for condition-based maintenance are often developed using sensor data collected during experimental tests. Most tests performed in laboratories focus on a coupon level or flat panels, while structural component testing is less commonly seen. As researchers, we often consider our experimental tests to be representative of a structure in a final application and consider the developed methodologies to be transferrable to these real-life structures. Yet, structures in their final applications such as wind turbines or aircraft are often larger, more complex, might contain various assembly details, and are loaded in complex conditions. These factors might influence the performance of developed diagnostic and prognostic methodologies and should therefore not be ignored. In our work, we consider the aspects of upscaling structural health monitoring (SHM) methodologies for stiffened composite panels with the design of the panels inspired by an aircraft wing structure. For this, we examine two levels of panels, namely a single- and multi-stiffener composite panel, where we consider the single-stiffener panel to be a representative lower-level version of the multi-stiffener panel. Multiple SHM sensors (acoustic emission, Lamb waves, strain sensing) were installed on both composite panels to monitor damage propagation during testing. We identify and analyse challenges and further discuss considerations that must be taken during upscaling of diagnostics and prognostics, and with that, aid in the development of health management methodologies for condition-based maintenance., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Structural Integrity & Composites

In our paper, we reported a previously unobserved Raman-like band centered at around 550 cm-1 [e.g., Figs. 2(a) and 3(a)] and found that it diminished after the material degraded. Our latest study has revealed that the assignment of the 550 cm-1 band as a Raman band was erroneous, and this Raman-like peak was in fact the result of stray light of the unusually strong photoluminescence (PL) signal, peaked at around 770 nm, in the pristine sample. The strongest band-edge PL intensity was on the order of (0.5-1.0) × 105 cps [e.g., Figs. 2(b) and 3(d)] at the same or comparable power as the Raman measurements. Assuming a typical stray-light suppression ratio of 10-4 to 10-5 for a single grating spectrometer (an exact value is not available from the manufacturer of the instrument), the stray-light level would be comparable to that of the observed "Raman" peak typically on the order of 1 cps. In our new study, this broad band was eliminated after adding an appropriate short-pass filter to block the band-edge PL signal. The broad band at around 1340 cm-1, as shown in Fig. 5(a), has the same origin as the 550 cm-1 band. The observed anticorrelation between the signals of around 100 cm-1 and 550 cm-1 was the result of structure degradation that enhanced the Raman signal near 100 cm-1 and, in the meantime, reduced the PL signal near the perovskite bandgap at around 770 nm but appeared at 550 cm-1 as stray light. The wrong interpretation of 550 cm-1 does not affect the primary conclusion, i.e., the multiple-stage structure transformation and the interpretation of other results.We regret this error and intend to write a paper to report the latest findings in detail in the near future. The effort of Jose F. Castaneda, a new Ph.D. student in Zhang's group, has led to the finding of this error.

Background: Recurrent glioblastoma multiforme (GBM) is resistant to most therapeutic endeavors, with low response rates and survival rarely exceeding six months. There are no clearly established chemotherapeutic regimens and the aim of treatment is palliation with improvement in the quality of life. Patients and methods: We report an open-label, uncontrolled, multicenter phase II trial of temozolomide in 138 patients (intent-to-treat [ITI] population) with glioblastoma multiforme at first relapse and a Karnofsky performance status (KPS) ≥ 70. One hundred twenty-eight patients were histologically confirmed with GBM or gliosarcoma (GS) by independent central review. Chemotherapy-naïve patients were treated with temozolomide 200 mg/m2/day2/day orally for the first five days of a 28-day cycle. Patients previously treated with nitrosourea- containing adjuvant chemotherapy received 150 mg/m2/day for the first five days of a 28-day cycle. In the absence of grade 3 or 4 toxicity, patients on the 150 mg/m2 dose schedule were eligible for a 200 mg/m2 dose on the next cycle. Results: The primary endpoint was six-month progression-free survival assessed with strict radiological and clinical criteria. Secondary endpoints included radiological response and Health-related Quality of Life (HQL). Progression-free survival at six months was 18% (95% confidence interval (CI): 11%-26%) for the eligible-histology population. Median progression-free survival and median overall survival were 2.1 months and 5.4 months, respectively. The six-month survival rate was 46%. The objective response rate (complete response and partial response) determined by independent central review of gadolinium-enhanced magnetic resonance imaging (MRI) scans was 8% for both the ITT and eligible-histology populations, with an additional 43%;A and 45% of patients, respectively, having stable disease (SD). Objectively assessed response and maintenance of a progression-free status were both associated with HQL ben