Seismic Response of a Platform-Frame System with Steel Columns

Timber platform-frame shear walls are characterized by high ductility and diffuse energy dissipation but limited in-plane shear resistance. A novel lightweight constructive system composed of steel columns braced with oriented strand board (OSB) panels was conceived and tested. Preliminary laboratory tests were performed to study the OSB-to-column connections with self-drilling screws. Then, the seismic response of a shear wall was determined performing a quasi-static cyclic-loading test of a full-scale specimen. Results presented in this work in terms of force-displacement capacity show that this system confers to shear walls high in-plane strength and stiffness with good ductility and dissipative capacity. Therefore, the incorporation of steel columns within OSB bracing panels results in a strong and stiff platform-frame system with high potential for low- and medium-rise buildings in seismic-prone areas

An Optimal Shaped Sensor Array Derivation

In Structural Health Monitoring (SHM) applications, the Direction of Arrival (DoA) estimation of Guided Waves (GW) on sensor arrays is often used as a fundamental means to locate Acoustic Sources (AS) generated by damages growth or undesired impacts in thin-wall structures (e.g., plates or shells). In this paper, we consider the problem of designing the arrangement and shape of piezo-sensors in planar clusters in order to optimize the DoA estimation performance in noise-affected measurements. We assume that: (i) the wave propagation velocity is unknown, (ii) the DoA is estimated via the time delays of wavefronts between sensors, and (iii) the maximum value of the time delays is limited. The optimality criterion is derived basing on the Theory of Measurements. The sensor array design is so that the DoA variance is minimized in an average sense by exploiting the Calculus of Variations. In this way, considering a three-sensor cluster and a monitored angles sector of 90°, the optimal time delays–DoA relations are derived. A suitable re-shaping procedure is used to impose such relations and, at the same time, to induce the same spatial filtering effect between sensors so that the sensor acquired signals are equal except for a time-shift. In order to achieve the last aim, the sensors shape is realized by exploiting a technique called Error Diffusion, which is able to emulate piezo-load functions with continuously modulated values. In this way, the Shaped Sensors Optimal Cluster (SS-OC) is derived. A numerical assessment via Green’s functions simulations shows improved performance in DoA estimation by means of the SS-OC when compared to clusters realized with conventional piezo-disk transducers

Non-destructive testing on aramid fibres for the long-term assessment of interventions on heritage structures

High strength fibre reinforced polymers (FRPs) are composite materials made of fibres such as carbon, aramid and/or glass, and a resin matrix. FRPs are commonly used for structural repair and strengthening interventions and exhibit high potential for applications to existing constructions, including heritage buildings. In regard to aramid fibres, uncertainties about the long-term behaviour of these materials have often made the designers reluctant to use them in structural engineering. The present study describes simple and non-destructive nonlinearity tests for assessing damage or degradation of structural properties in Kevlar fibres. This was obtained by using high precision measurements to detect small deviations in the dynamic response measured on fibres and ropes. The change in dynamic properties was then related to a damage produced by exposure of the sample to UV rays for a defined time period, which simulated long-term sun exposure. In order to investigate the sensitivity of such an approach to damage detection, non-linearity characterisation tests were conducted on aramid fibres in both damaged and undamaged states. With the purpose of carrying out dynamic tests on small fibre specimens, a dedicated instrumentation was designed and built in cooperation with the Metrology Laboratory of the Department of Electronics at the Politecnico di Torino

Enabling Spatial Multiplexing in Guided Waves-based Communication: the case of Quadrature Amplitude Modulation realized via Discrete Frequency Steerable Acoustic Transducers

Guided Waves (GWs) communication using conventional transducers, e.g., PZT, encounters quite a few problems, such as complex hardware systems and waves multipath interference. To overcome such drawbacks, Frequency Steerable Acoustic Transducers (FSATs) which benefit from inherent directional capabilities can be fruitfully adopted to implement a spatial multiplexing strategy. The FSATs work on the frequency-dependent spatial filtering effect to generate/receive waves, resulting in a direct relationship between the direction of propagation and the frequency content of the transmitted/received signals. Thanks to this unique frequency-steering capability, FSATs are best suited to implement frequency-driven modulation protocols, such as the ones typically exploited for GWs-based data communication. Among these, the Quadrature Amplitude Modulation (QAM) scheme is advantageous in terms of noise immunity. Thus, the objective of this work is to combine QAM with the built-in spatial multiplexing capabilities of FSATs to realize, in hardware, frequency directivity, like the solutions that are currently being investigated in 5G communications

Tiny Deep Learning Architectures Enabling Sensor-Near Acoustic Data Processing and Defect Localization

The timely diagnosis of defects at their incipient stage of formation is crucial to extending the life-cycle of technical appliances. This is the case of mechanical-related stress, either due to long aging degradation processes (e.g., corrosion) or in-operation forces (e.g., impact events), which might provoke detrimental damage, such as cracks, disbonding or delaminations, most commonly followed by the release of acoustic energy. The localization of these sources can be successfully fulfilled via adoption of acoustic emission (AE)-based inspection techniques through the computation of the time of arrival (ToA), namely the time at which the induced mechanical wave released at the occurrence of the acoustic event arrives to the acquisition unit. However, the accurate estimation of the ToA may be hampered by poor signal-to-noise ratios (SNRs). In these conditions, standard statistical methods typically fail. In this work, two alternative deep learning methods are proposed for ToA retrieval in processing AE signals, namely a dilated convolutional neural network (DilCNN) and a capsule neural network for ToA (CapsToA). These methods have the additional benefit of being portable on resource-constrained microprocessors. Their performance has been extensively studied on both synthetic and experimental data, focusing on the problem of ToA identification for the case of a metallic plate. Results show that the two methods can achieve localization errors which are up to 70% more precise than those yielded by conventional strategies, even when the SNR is severely compromised (i.e., down to 2 dB). Moreover, DilCNN and CapsNet have been implemented in a tiny machine learning environment and then deployed on microcontroller units, showing a negligible loss of performance with respect to offline realizations

A sparsity promoting algorithm for time of flight estimation in Guided waves - based SHM

none3siUltrasonic Guided Waves (GW) are employed by many Structural Health Monitoring (SHM) systems. In plate-like components, GW based defect detection and localization is typically achieved through multiple piezoelectric transducers arranged in different array configurations. In active methods, one or more actuators are used to generate GWs and the sensors work as wave detectors. Defects can be detected and localized from the wave scattering that they generate. To increase the precision of localization approaches, it is important to minimize the uncertainty in the estimation of the time of flight (ToF) of the waves scattered by the defect. Such task is complicated by the dispersive and multimodal nature of ultrasonic GW propagation. In this work, we analyse two algorithms to extract the ToF from waveforms acquired with a Scanning Laser Doppler Vibrometer (SLDV). The algorithms outputs are used to feed beamforming procedures to image cracks with various orientations.openL. De Marchi; J. Moll; A. MarzaniL. De Marchi; J. Moll; A. Marzan

A Combination of Chirp Spread Spectrum and Frequency Hopping for Guided Waves-based Digital Data Communication with Frequency Steerable Acoustic Transducers

To facilitate Guided Waves (GWs) communication in terms of hardware simplification and cost reductions, shaped transducers with inherent directional properties can be used. A promising example of such devices is provided by Frequency Steerable Acoustic Transducers (FSATs), where the propagation direction of waves is controlled by the frequency content of the transmitted/acquired signals, thanks to the spatial filtering effect. These peculiar characteristics make the FSAT devices particularly suited for implementation of frequency-based modulation protocols, in which the signal content assigned to each user is uniquely encoded by a corresponding carrier tone. In this work, the special directivity of FSATs is paired with a novel encoding strategy, which is based on a combination of Chirp Spread Spectrum (CSS) and Frequency Hopping (FH) multiplexing, similar to the LoRaWan solution adopted in radio-frequency environments. The devised strategy is aimed at suppressing the inherent destructive interference due to GWs dispersion and multi-path fading

A Filtering technique based on a DLMS algorithm for ultrasonography video

It is well known that ultrasonography is a diagnostic method for visualizinginside human tissues by spreading ultrasounds and measuring their return time tothe sensor. However, the interface between the human skin and this ultrasoundtransducer attenuates the received signal and the medical image quality deterioratessignificantly. In this paper we propose a filtering technique in order to compensatethis attenuation. A finite impulse response filter (FIR) based on a Delayed LeastMean Square (DLMS) was optimized and implemented. The main contribution ofour work consists of finding the order and the coefficients of the filter that minimizethe attenuation error. We validate our method first on simulated data and later on areprogrammable FPGA device for a real time performance testing. Among others,we show that incrementing the order of the filter, not always is the best way toreduce image quality errors

Vibration Monitoring in the Compressed Domain with Energy-Efficient Sensor Networks

Structural Health Monitoring (SHM) is crucial for the development of safe infrastructures. Onboard vibration diagnostics implemented by means of smart embedded sensors is a suitable approach to achieve accurate prediction supported by low-cost systems. Networks of sensors can be installed in isolated infrastructures allowing periodic monitoring even in the absence of stable power sources and connections. To fulfill this goal, the present paper proposes an effective solution based on intelligent extreme edge nodes that can sense and compress vibration data onboard, and extract from it a reduced set of statistical descriptors that serve as input features for a machine learning classifier, hosted by a central aggregating unit. Accordingly, only a small batch of meaningful scalars needs to be outsourced in place of long time series, hence paving the way to a considerable decrement in terms of transmission time and energy expenditure. The proposed approach has been validated using a real-world SHM dataset for the task of damage identification from vibration signals. Results demonstrate that the proposed sensing scheme combining data compression and feature estimation at the sensor level can attain classification scores always above 94%, with a sensor life cycle extension up to 350x and 1510x if compared with compression-only and processing-free implementations, respectively

Augmented Reality to Support On-Field Post-Impact Maintenance Operations on Thin Structures

This paper proposes an augmented reality (AR) strategy in which a Lamb waves based impact detection methodology dynamically interacts with a head portable visualization device allowing the inspector to see the estimated impact position (with its uncertainty) and impact energy directly on the plate-like structure. The impact detection methodology uses a network of piezosensors bonded on the structure to be monitored and a signal processing algorithm (the Warped Frequency Transform) able to compensate for dispersion the acquired waveforms. The compensated waveforms yield to a robust estimation of Lamb waves difference in distance of propagation (DDOP), used to feed hyperbolic algorithms for impact location determination, and allow an estimation of the uncertainty of the impact positioning as well as of the impact energy. The outputs of the impact methodology are passed to a visualization technology that yielding their representation in Augmented Reality (AR) is meant to support the inspector during the on-field inspection/diagnosis as well as the maintenance operations. The inspector, in fact, can see interactively in real time the impact data directly on the surface of the structure. To validate the proposed approach, tests on an aluminum plate are presented. Results confirm the feasibility of the method and its exploitability in maintenance practice