Expectations boost the reconstruction of auditory features from electrophysiological responses to noisy speech.

Online speech processing imposes significant computational demands on the listening brain, the underlying mechanisms of which remain poorly understood. Here, we exploit the perceptual "pop-out" phenomenon (i.e. the dramatic improvement of speech intelligibility after receiving information about speech content) to investigate the neurophysiological effects of prior expectations on degraded speech comprehension. We recorded electroencephalography (EEG) and pupillometry from 21 adults while they rated the clarity of noise-vocoded and sine-wave synthesized sentences. Pop-out was reliably elicited following visual presentation of the corresponding written sentence, but not following incongruent or neutral text. Pop-out was associated with improved reconstruction of the acoustic stimulus envelope from low-frequency EEG activity, implying that improvements in perceptual clarity were mediated via top-down signals that enhanced the quality of cortical speech representations. Spectral analysis further revealed that pop-out was accompanied by a reduction in theta-band power, consistent with predictive coding accounts of acoustic filling-in and incremental sentence processing. Moreover, delta-band power, alpha-band power, and pupil diameter were all increased following the provision of any written sentence information, irrespective of content. Together, these findings reveal distinctive profiles of neurophysiological activity that differentiate the content-specific processes associated with degraded speech comprehension from the context-specific processes invoked under adverse listening conditions.

Analysis of FRP-Strengthened Reinforced Concrete Beams Using Electromechanical Impedance Technique and Digital Image Correlation System.

Fiber-reinforced polymer (FRP) strengthening systems have been considered an effective technique to retrofit concrete structures, and their use nowadays is more and more extensive. Externally bonded reinforcement (EBR) and near-surface mounted (NSM) technologies are the two most widely recognized and applied FRP strengthening methods for enhancing structural performance worldwide. However, one of the main disadvantages of both approaches is a possible brittle failure mode provided by a sudden debonding of the FRP. Therefore, methodologies able to monitor the long-term efficiency of this kind of strengthening constitute a challenge to be overcome. In this work, two reinforced concrete (RC) specimens strengthened with FRP and subjected to increasing load tests were monitored. One specimen was strengthened using the EBR method, while for the other, the NSM technique was used. The multiple cracks emanating in both specimens in the static tests, as possible origins of a future debonding failure, were monitored using a piezoelectric (PZT)-transducer-based electromechanical impedance (EMI) technique and a digital image correlation (DIC) system. Clustering approaches based on impedance measurements of the healthy and damaged states of the specimens allowed us to suspect the occurrence of cracks and their growth. The strain profiles captured in the images of the DIC system allowed us to depict surface hair-line cracks and their propagation. The combined implementation of the two techniques to look for correlations during incremental bending tests was addressed in this study as a means of improving the prediction of early cracks and potentially anticipating the complete failure of the strengthened specimens.

Performance of Linear Mixed Models to Assess the Effect of Sustained Loading and Variable Temperature on Concrete Beams Strengthened with NSM-FRP.

Although some extended studies about the short-term behavior of NSM FRP strengthened beams have been carried out, there is a lack of knowledge about the behavior of this kind of strengthening under sustained loads and high service temperatures. Electromechanical impedance method formulated from measurements obtained from PZT patches gives the ability for monitoring the performance and changes experienced by these strengthened beams at a local level, which is a key aspect considering its possible premature debonding failure modes. This paper presents an experimental testing program aimed at investigating the long-term performance of a concrete beam strengthened with a NSM CFRP laminate. Long term performance under different levels of sustained loading and temperature conditions is correlated with EMI signatures processed using Linear Mixed-effects models. These models are very powerful to process datasets that have a multilevel or hierarchical structure as those yielded by our tests. Results have demonstrated the potential of these techniques as health monitoring methodology under different conditions in an especially complex problem such as NSM-FRP strengthened concrete structures.

Analysis of the Impact of Sustained Load and Temperature on the Performance of the Electromechanical Impedance Technique through Multilevel Machine Learning and FBG Sensors.

The electro-mechanical impedance (EMI) technique has been applied successfully to detect minor damage in engineering structures including reinforced concrete (RC). However, in the presence of temperature variations, it can cause false alarms in structural health monitoring (SHM) applications. This paper has developed an innovative approach that integrates the EMI methodology with multilevel hierarchical machine learning techniques and the use of fiber Bragg grating (FBG) temperature and strain sensors to evaluate the mechanical performance of RC beams strengthened with near surface mounted (NSM)-fiber reinforced polymer (FRP) under sustained load and varied temperatures. This problem is a real challenge since the bond behavior at the concrete-FRP interface plays a key role in the performance of this type of structure, and additionally, its failure occurs in a brittle and sudden way. The method was validated in a specimen tested over a period of 1.5 years under different conditions of sustained load and temperature. The analysis of the experimental results in an especially complex problem with the proposed approach demonstrated its effectiveness as an SHM method in a combined EMI-FBG framework.

An EMI-Based Clustering for Structural Health Monitoring of NSM FRP Strengthening Systems.

The use of fiber-reinforced polymers (FRP) in civil construction applications with the near-surface mounted (NSM) method has gained considerable popularity worldwide and can produce confident strengthening and repairing systems for existing concrete structures. By using this technique, the FRP reinforcement is installed into slits cut into the concrete cover using cement mortar or epoxy as bonding materials, yielding an attractive method to strengthen concrete structures as an advantageous alternative to the external bonding of FRP sheets. However, in addition to the two conventional failure modes of concrete beams, sudden and brittle debonding failures are still likely to happen. Due to this, a damage identification technology able to identify anomalies at early stages is needed. In this work, some relevant cluster-based methods and their adaptation to electromechanical impedance (EMI)-based damage detection in NSM-FRP strengthened structures are developed and validated with experimental tests. The performance of the proposed clustering approaches and their evaluation in comparison with the experimental observations have shown a strong potential of these techniques as damage identification methodology in an especially complex problem such as NSM-FRP strengthened concrete structures.

Active Wireless System for Structural Health Monitoring Applications.

The use of wireless sensors in Structural Health Monitoring (SHM) has increased significantly in the last years. Piezoelectric-based lead zirconium titanate (PZT) sensors have been on the rise in SHM due to their superior sensing abilities. They are applicable in different technologies such as electromechanical impedance (EMI)-based SHM. This work develops a flexible wireless smart sensor (WSS) framework based on the EMI method using active sensors for full-scale and autonomous SHM. In contrast to passive sensors, the self-sensing properties of the PZTs allow interrogating with or exciting a structure when desired. The system integrates the necessary software and hardware within a service-oriented architecture approach able to provide in a modular way the services suitable to satisfy the key requirements of a WSS. The framework developed in this work has been validated on different experimental applications. Initially, the reliability of the EMI method when carried out with the proposed wireless sensor system is evaluated by comparison with the wireless counterpart. Afterwards, the performance of the system is evaluated in terms of software stability and reliability of functioning.

Damage Detection Based on Power Dissipation Measured with PZT Sensors through the Combination of Electro-Mechanical Impedances and Guided Waves.

The use of piezoelectric ceramic transducers (such as Lead-Zirconate-Titanate-PZT) has become more and more widespread for Structural Health Monitoring (SHM) applications. Among all the techniques that are based on this smart sensing solution, guided waves and electro-mechanical impedance techniques have found wider acceptance, and so more studies and experimental works can be found containing these applications. However, even though these two techniques can be considered as complementary to each other, little work can be found focused on the combination of them in order to define a new and integrated damage detection procedure. In this work, this combination of techniques has been studied by proposing a new integrated damage indicator based on Electro-Mechanical Power Dissipation (EMPD). The applicability of this proposed technique has been tested through different experimental tests, with both lab-scale and real-scale structures.

Submodelling approach to screw-to-bone interaction in additively manufactured subperiosteal implant structures.

Thanks to new digital technologies, complex cases of severe maxillary atrophy may now be treated with additively manufactured subperiosteal implant structures (AMSISs). However, there are few studies addressing this topic and most of them focus on the mechanical behaviour of the AMSIS itself without considering its interaction with the maxilla bone. The aim of this study is to provide a methodology based on finite element analysis (FEA) to evaluate the effect of interaction between the maxilla bone and the screws fixing the AMSIS. The mechanical performance of an AMSIS was examined via a FEA based on submodelling. Significant differences were encountered in displacements and reaction forces when bone-screw interaction was considered. Stress in the cortical layer was found to be close to the maximum strength while the trabecular layer seems to have no effect on the results; stresses in the AMSIS are lower than the fatigue stress limit. Finally, the comparison of stresses between models with and without osseointegration shows how stresses drop once osseointegration is complete. The proposed submodelling approach considerably reduces the computational effort and enables both a detailed model of the interaction between the thread of the screws and the bone and an accurate evaluation of displacement and stress fields on the interface. The results have shown that stresses in the cortical bone are highly affected by the initial geometry of the thread inside the bone, which demonstrates the importance of modelling the effect of the thread.

Flexural Performance and End Debonding Prediction of NSM Carbon FRP-Strengthened Reinforced Concrete Beams under Different Service Temperatures.

This paper aims to evaluate the influence of relatively high service temperatures (near or beyond the glass transition temperature (Tg) of epoxy adhesive) on the flexural performance and end debonding phenomenon in near-surface mounted (NSM) carbon fiber-reinforced polymer (CFRP)-strengthened, reinforced concrete (RC) beams. To this end, an experimental program consisting of 24 beams (divided into four groups) was performed, where different parameters was combined (i.e., service temperature, steel reinforcement ratio, CFRP ratio, and concrete compressive strength). In addition, the effect of the testing temperature on the end debonding phenomenon was investigated with an analytical procedure according to fib Bulletin 90, and the predictions were compared to experimental results. Taking specimens tested at 20 °C as a reference, no considerable change was observed in the ultimate load of the specimens tested below 60 °C (being in the range of epoxy Tg), and all specimens failed by FRP rupture. On the other hand, the increase in testing temperature up to 70 and 85 °C was followed by a decrease in the capacity of the strengthened beams and a change in failure mode, moving from FRP rupture to end debonding and concrete crushing. The analytical procedure successfully predicted the occurrence of premature end debonding failure and demonstrated that the effect of temperature on the mechanical properties of materials can be a key factor when predicting the premature end debonding in a NSM joint.

Influence of Bond Characterization on Load-Mean Strain and Tension Stiffening Behavior of Concrete Elements Reinforced with Embedded FRP Reinforcement.

Based on the characterization of the bond between Fiber-Reinforced Polymer (FRP) bars and concrete, the structural behavior of cracked Glass-FRP (GFRP)-Reinforced Concrete (RC) tensile elements is studied in this paper. Simulations in which different bond-slip laws between both materials (FRP reinforcement and concrete) were used to analyze the effect of GFRP bar bond performance on the load transfer process and how it affects the load-mean strain curve, the distribution of reinforcement strain, the distribution of slip between reinforcement and concrete, and the tension stiffening effect. Additionally, a parametric study on the effect of materials (concrete grade, modulus of elasticity of the reinforcing bar, surface configuration, and reinforcement ratio) on the load-mean strain curve and the tension stiffening effect was also performed. Results from a previous experimental program, in combination with additional results obtained from Finite Element Analysis (FEA), were used to demonstrate the accuracy of the model to correctly predict the global (load-mean strain curve) and local (distribution of strains between cracks) structural behavior of the GFRP RC tensile elements.

Improvement of an additively manufactured subperiosteal implant structure design by finite elements based topological optimization.

To design a new subperiosteal implant structure for patients suffering from severe Maxillary Atrophy that lowers manufacturing cost, shortens surgical time and reduces patient trauma with regard to current implant structures. A 2-phase finite-element-based topology optimization process was employed with implants made from biocompatible materials via additive manufacturing. Five bite loading cases related to standard chewing, critical chewing force, and worst conditions of fastening were considered along with each specific result to establish the areas that needed to be subjected to fatigue strength optimization. The 2-phase topological optimization tested in this study performed better than the reference implant geometry in terms of both the structural integrity of the implant under tensile-compressive and fatigue strength conditions and the material constraints related to implant manufacturing conditions. It returns a nearly 28% lower volumetric geometry and avoids the need to use two upper fastening screws that are required with complex surgical procedures. The combination of topological optimization methods with the flexibility afforded by additively manufactured biocompatible materials, provides promising results in terms of cost reduction, minimizing the surgical trauma and implant installation impact on edentulous patients.