Structural health evaluation by optimization techinique and artificial neural network

This paper presents two different approaches to detect, locate, and characterize structural damage. Both techniques utilize electrical impedance in a first stage to locate the damaged area. In the second stage, to quantify the damage severity, one can use neural network, or optimization technique. The electrical impedance-based, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations, this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors, and therefore, it is able to detect the damage in its early stage. Optimization approaches must be used for the case where a good condensed model is known, while neural network can be also used to estimate the nature of damage without prior knowledge of the model of the structure. The paper concludes with an experimental example in a welded cubic aluminum structure, in order to verify the performance of these two proposed methodologies.

Nondestructive evaluation of concrete compression strength by means of Artificial Neural Network (ANN)

The evaluation of structural performance of existing concrete buildings, built according to standards and materials quite different to those available today, requires procedures and methods able to cover lack of data about mechanical material properties and reinforcement detailing. To this end detailed inspections and test on materials are required. As a consequence tests on drilled cores are required; on the other end, it is stated that non-destructive testing (NDT) cannot be used as the only mean to get structural information, but can be used in conjunction with destructive testing (DT) by a representative correlation between DT and NDT. The aim of this study is to verify the accuracy of some formulas of correlation available in literature between measured parameters, i.e. rebound index, ultrasonic pulse velocity and compressive strength (SonReb Method). To this end a relevant number of DT and NDT tests has been performed on many school buildings located in Cesena (Italy). The above relationships have been assessed on site correlating NDT results to strength of core drilled in adjacent locations. Nevertheless, concrete compressive strength assessed by means of NDT methods and evaluated with correlation formulas has the advantage of being able to be implemented and used for future applications in a much more simple way than other methods, even if its accuracy is strictly limited to the analysis of concretes having the same characteristics as those used for their calibration. This limitation warranted a search for a different evaluation method for the non-destructive parameters obtained on site. To this aim, the methodology of neural identification of compressive strength is presented. Artificial Neural Network (ANN) suitable for the specific analysis were chosen taking into account the development presented in the literature in this field. The networks were trained and tested in order to detect a more reliable strength identification methodology.

Embedded systems and advanced signal processing for Acousto- Ultrasonic Inspections

Non Destructive Testing (NDT) and Structural Health Monitoring (SHM) are becoming essential in many application contexts, e.g. civil, industrial, aerospace etc., to reduce structures maintenance costs and improve safety. Conventional inspection methods typically exploit bulky and expensive instruments and rely on highly demanding signal processing techniques. The pressing need to overcome these limitations is the common thread that guided the work presented in this Thesis. In the first part, a scalable, low-cost and multi-sensors smart sensor network is introduced. The capability of this technology to carry out accurate modal analysis on structures undergoing flexural vibrations has been validated by means of two experimental campaigns. Then, the suitability of low-cost piezoelectric disks in modal analysis has been demonstrated. To enable the use of this kind of sensing technology in such non conventional applications, ad hoc data merging algorithms have been developed. In the second part, instead, imaging algorithms for Lamb waves inspection (namely DMAS and DS-DMAS) have been implemented and validated. Results show that DMAS outperforms the canonical Delay and Sum (DAS) approach in terms of image resolution and contrast. Similarly, DS-DMAS can achieve better results than both DMAS and DAS by suppressing artefacts and noise. To exploit the full potential of these procedures, accurate group velocity estimations are required. Thus, novel wavefield analysis tools that can address the estimation of the dispersion curves from SLDV acquisitions have been investigated. An image segmentation technique (called DRLSE) was exploited in the k-space to draw out the wavenumber profile. The DRLSE method was compared with compressive sensing methods to extract the group and phase velocity information. The validation, performed on three different carbon fibre plates, showed that the proposed solutions can accurately determine the wavenumber and velocities in polar coordinates at multiple excitation frequencies.

Comprehensive Bridge Deck Deterioration Mapping of Nine Bridges by Nondestructive Evaluation Technologies Final Report, January 2011

The primary objective of this research was to demonstrate the benefits of NDT technologies for effectively detecting and characterizing deterioration in bridge decks. In particular, the objectives were to demonstrate the capabilities of ground-penetrating radar (GPR) and impact echo (IE), and to evaluate and describe the condition of nine bridge decks proposed by Iowa DOT. The first part of the report provides a detailed review of the most important deterioration processes in concrete decks, followed by a discussion of the five NDT technologies utilized in this project. In addition to GPR and IE methods, three other technologies were utilized, namely: half-cell (HC) potential, electrical resistivity (ER), and ultrasonic surface waves (USW) method. The review includes a description of the principles of operation, field implementation, data analysis, and interpretation; information regarding their advantages and limitations in bridge deck evaluations and condition monitoring are also implicitly provided.. The second part of the report provides descriptions and bridge deck evaluation results from the nine bridges. The results of the NDT surveys are described in terms of condition assessment maps and are compared with the observations obtained from the recovered cores or conducted bridge deck rehabilitation. Results from this study confirm that the used technologies can provide detailed and accurate information about a certain type of deterioration, electrochemical environment, or defect. However, they also show that a comprehensive condition assessment of bridge decks can be achieved only through a complementary use of multiple technologies at this stage,. Recommendations are provided for the optimum implementation of NDT technologies for the condition assessment and monitoring of bridge decks.

Remote Continuous Evaluation of a Bridge Constructed Using High-Performance Steel, 2006

Of the approximately 25,000 bridges in Iowa, 28% are classified as structurally deficient, functionally obsolete, or both. The state of Iowa thus follows the national trend of an aging infrastructure in dire need of repair or replacement with a relatively limited funding base. Therefore, there is a need to develop new materials with properties that may lead to longer life spans and reduced life-cycle costs. In addition, new methods for determining the condition of structures are needed to monitor the structures effectively and identify when the useful life of the structure has expired or other maintenance is needed. High-performance steel (HPS) has emerged as a material with enhanced weldability, weathering capabilities, and fracture toughness compared to conventional structural steels. In 2004, the Iowa Department of Transportation opened Iowa's first HPS girder bridge, the East 12th Street Bridge over I-235 in Des Moines, Iowa. The objective of this project was to evaluate HPS as a viable option for use in Iowa bridges with a continuous structural health monitoring (SHM) system. The scope of the project included documenting the construction of the East 12th Street Bridge and concurrently developing a remote, continuous SHM system using fiber-optic sensing technology to evaluate the structural performance of the bridge. The SHM system included bridge evaluation parameters, similar to design parameters used by bridge engineers, for evaluating the structure. Through the successful completion of this project, a baseline of bridge performance was established that can be used for continued long-term monitoring of the structure. In general, the structural performance of the HPS bridge exceeded the design parameters and is performing well. Although some problems were encountered with the SHM system, the system functions well and recommendations for improving the system have been made.

Numerical methods for the dispersion analysis of Guided Waves

The use of guided ultrasonic waves (GUW) has increased considerably in the fields of non-destructive (NDE) testing and structural health monitoring (SHM) due to their ability to perform long range inspections, to probe hidden areas as well as to provide a complete monitoring of the entire waveguide. Guided waves can be fully exploited only once their dispersive properties are known for the given waveguide. In this context, well stated analytical and numerical methods are represented by the Matrix family methods and the Semi Analytical Finite Element (SAFE) methods. However, while the former are limited to simple geometries of finite or infinite extent, the latter can model arbitrary cross-section waveguides of finite domain only. This thesis is aimed at developing three different numerical methods for modelling wave propagation in complex translational invariant systems. First, a classical SAFE formulation for viscoelastic waveguides is extended to account for a three dimensional translational invariant static prestress state. The effect of prestress, residual stress and applied loads on the dispersion properties of the guided waves is shown. Next, a two-and-a-half Boundary Element Method (2.5D BEM) for the dispersion analysis of damped guided waves in waveguides and cavities of arbitrary cross-section is proposed. The attenuation dispersive spectrum due to material damping and geometrical spreading of cavities with arbitrary shape is shown for the first time. Finally, a coupled SAFE-2.5D BEM framework is developed to study the dispersion characteristics of waves in viscoelastic waveguides of arbitrary geometry embedded in infinite solid or liquid media. Dispersion of leaky and non-leaky guided waves in terms of speed and attenuation, as well as the radiated wavefields, can be computed. The results obtained in this thesis can be helpful for the design of both actuation and sensing systems in practical application, as well as to tune experimental setup.

Behaviour and applications of elastic waves in structures and metamaterials

The present thesis focuses on elastic waves behaviour in ordinary structures as well as in acousto-elastic metamaterials via numerical and experimental applications. After a brief introduction on the behaviour of elastic guided waves in the framework of non-destructive evaluation (NDE) and structural health monitoring (SHM) and on the study of elastic waves propagation in acousto-elastic metamaterials, dispersion curves for thin-walled beams and arbitrary cross-section waveguides are extracted via Semi-Analytical Finite Element (SAFE) methods. Thus, a novel strategy tackling signal dispersion to locate defects in irregular waveguides is proposed and numerically validated. Finally, a time-reversal and laser-vibrometry based procedure for impact location is numerically and experimentally tested. In the second part, an introduction and a brief review of the basic definitions necessary to describe acousto-elastic metamaterials is provided. A numerical approach to extract dispersion properties in such structures is highlighted. Afterwards, solid-solid and solid-fluid phononic systems are discussed via numerical applications. In particular, band structures and transmission power spectra are predicted for 1P-2D, 2P-2D and 2P-3D phononic systems. In addition, attenuation bands in the ultrasonic as well as in the sonic frequency regimes are experimentally investigated. In the experimental validation, PZTs in a pitch-catch configuration and laser vibrometric measurements are performed on a PVC phononic plate in the ultrasonic frequency range and sound insulation index is computed for a 2P-3D phononic barrier in the sonic frequency range. In both cases the numerical-experimental results comparison confirms the existence of the numerical predicted band-gaps. Finally, the feasibility of an innovative passive isolation strategy based on giant elastic metamaterials is numerically proved to be practical for civil structures. In particular, attenuation of seismic waves is demonstrated via finite elements analyses. Further, a parametric study shows that depending on the soil properties, such an earthquake-proof barrier could lead to significant reduction of the superstructure displacement.

Inductive-Capacitive Resonant Circuit Sensors for Structural Health and Environmental Monitoring

Inductive-capacitive (LC) resonant circuit sensors are low-cost, wireless, durable, simple to fabricate and battery-less. Consequently, they are well suited to sensing applications in harsh environments or in situations where large numbers of sensors are needed. They are also advantageous in applications where access to the sensor is limited or impossible or when sensors are needed on a disposable basis. Due to their many advantages, LC sensors have been used for sensing a variety of parameters including humidity, temperature, chemical concentrations, pH, stress/pressure, strain, food quality and even biological growth. However, current versions of the LC sensor technology are limited to sensing only one parameter. The purpose of this work is to develop new types of LC sensor systems that are simpler to fabricate (hence lower cost) or capable of monitoring multiple parameters simultaneously. One design presented in this work, referred to as the multi-element LC sensor, is able to measure multiple parameters simultaneously using a second capacitive element. Compared to conventional LC sensors, this design can sense multiple parameters with a higher detection range than two independent sensors while maintaining the same overall sensor footprint. In addition, the two-element sensor does not suffer from interference issues normally encountered while implementing two LC sensors in close proximity. Another design, the single-spiral inductive-capacitive sensor, utilizes the parasitic capacitance of a coil or spring structure to form a single layer LC resonant circuit. Unlike conventional LC sensors, this design is truly planar, thus simplifying its fabrication process and reducing sensor cost. Due to the simplicity of this sensor layout it will be easier and more cost-effective for embedding in common building or packaging materials during manufacturing processes, thereby adding functionality to current products (such as drywall sheets) while having a minor impact on overall unit cost. These modifications to the LC sensor design significantly improve the functionality and commercial feasibility of this technology, especially for applications where a large array of sensors or multiple sensing parameters are required.

Piezoelectric sensing and non-parametric statistical signal processing for health monitoring of hysteretic dampers used in seismic-resistant structures

The paper proposes a new application of non-parametric statistical processing of signals recorded from vibration tests for damage detection and evaluation on I-section steel segments. The steel segments investigated constitute the energy dissipating part of a new type of hysteretic damper that is used for passive control of buildings and civil engineering structures subjected to earthquake-type dynamic loadings. Two I-section steel segments with different levels of damage were instrumented with piezoceramic sensors and subjected to controlled white noise random vibrations. The signals recorded during the tests were processed using two non-parametric methods (the power spectral density method and the frequency response function method) that had never previously been applied to hysteretic dampers. The appropriateness of these methods for quantifying the level of damage on the I-shape steel segments is validated experimentally. Based on the results of the random vibrations, the paper proposes a new index that predicts the level of damage and the proximity of failure of the hysteretic damper

Material properties and nondestructive evaluation of laminated veneer lumber (LVL) made from Pinus oocarpa and P. kesiya

This study aimed at evaluating the mechanical, physical and biological properties of laminated veneer lumber (LVL) made from Pinus oocarpa Schiede ex Schltdl (PO) and Pinus kesiya Royle ex Gordon (PK) and at providing a nondestructive characterization thereof. Four PO and four PK LVL boards from 22 randomly selected 2-mm thickness veneers were produced according to the following characteristics: phenol-formaldehyde (190 g/m(2)), hot-pressing at 150A degrees C for 45 min and 2.8 N/mm(2) of specific pressure. After board production, nondestructive evaluation was conducted, and stress wave velocity (v (0)) and dynamic modulus of elasticity (E (Md) ) were determined. The following mechanical and physical properties were then evaluated: static bending modulus of elasticity (E (M) ), modulus of rupture (f (M) ), compression strength parallel to grain (f (c,0)), shear strength parallel to glue-line (f (v,0)), shear strength perpendicular to glue-line (f (v,90)), thickness swelling (TS), water absorption (WA), and permanent thickness swelling (PTS) for 2, 24, and 96-hour of water immersion. Biological property was also evaluated by measuring the weight loss by Trametes versicolor (Linnaeus ex Fries) Pilat (white-rot) and Gloeophyllum trabeum (Persoon ex Fries.) Murrill (brown-rot). After hot-pressing, no bubbles, delamination nor warping were observed for both species. In general, PK boards presented higher mechanical properties: E (M) , E (Md) , f (M) , f (c,0) whereas PO boards were dimensionally more stable, with lower values of WA, TS and PTS in the 2, 24, and 96-hour immersion periods. Board density, f (v,0), f (v,90) and rot weight loss were statistically equal for PO and PK LVL. The prediction of flexural properties of consolidated LVL by the nondestructive method used was not very efficient, and the fitted models presented lower predictability.

An evaluation approach for a new paradigm - health care integration

This paper explores an approach to the implementation and evaluation of integrated health service delivery. It identifies the key issues involved in integration evaluation, provides a framework for assessment and identifies areas for the development of new tools and measures. A proactive role for evaluators in responding to health service reform is advocated.

GPU implementation of a hyperspectral coded aperture algorithm for compressive sensing

This paper presents a new parallel implementation of a previously hyperspectral coded aperture (HYCA) algorithm for compressive sensing on graphics processing units (GPUs). HYCA method combines the ideas of spectral unmixing and compressive sensing exploiting the high spatial correlation that can be observed in the data and the generally low number of endmembers needed in order to explain the data. The proposed implementation exploits the GPU architecture at low level, thus taking full advantage of the computational power of GPUs using shared memory and coalesced accesses to memory. The proposed algorithm is evaluated not only in terms of reconstruction error but also in terms of computational performance using two different GPU architectures by NVIDIA: GeForce GTX 590 and GeForce GTX TITAN. Experimental results using real data reveals signficant speedups up with regards to serial implementation.

Parallel hyperspectral compressive sensing method on GPU

Remote hyperspectral sensors collect large amounts of data per flight usually with low spatial resolution. It is known that the bandwidth connection between the satellite/airborne platform and the ground station is reduced, thus a compression onboard method is desirable to reduce the amount of data to be transmitted. This paper presents a parallel implementation of an compressive sensing method, called parallel hyperspectral coded aperture (P-HYCA), for graphics processing units (GPU) using the compute unified device architecture (CUDA). This method takes into account two main properties of hyperspectral dataset, namely the high correlation existing among the spectral bands and the generally low number of endmembers needed to explain the data, which largely reduces the number of measurements necessary to correctly reconstruct the original data. Experimental results conducted using synthetic and real hyperspectral datasets on two different GPU architectures by NVIDIA: GeForce GTX 590 and GeForce GTX TITAN, reveal that the use of GPUs can provide real-time compressive sensing performance. The achieved speedup is up to 20 times when compared with the processing time of HYCA running on one core of the Intel i7-2600 CPU (3.4GHz), with 16 Gbyte memory.

Parallel hyperspectral coded aperture for compressive sensing on GPUs

The application of compressive sensing (CS) to hyperspectral images is an active area of research over the past few years, both in terms of the hardware and the signal processing algorithms. However, CS algorithms can be computationally very expensive due to the extremely large volumes of data collected by imaging spectrometers, a fact that compromises their use in applications under real-time constraints. This paper proposes four efficient implementations of hyperspectral coded aperture (HYCA) for CS, two of them termed P-HYCA and P-HYCA-FAST and two additional implementations for its constrained version (CHYCA), termed P-CHYCA and P-CHYCA-FAST on commodity graphics processing units (GPUs). HYCA algorithm exploits the high correlation existing among the spectral bands of the hyperspectral data sets and the generally low number of endmembers needed to explain the data, which largely reduces the number of measurements necessary to correctly reconstruct the original data. The proposed P-HYCA and P-CHYCA implementations have been developed using the compute unified device architecture (CUDA) and the cuFFT library. Moreover, this library has been replaced by a fast iterative method in the P-HYCA-FAST and P-CHYCA-FAST implementations that leads to very significant speedup factors in order to achieve real-time requirements. The proposed algorithms are evaluated not only in terms of reconstruction error for different compressions ratios but also in terms of computational performance using two different GPU architectures by NVIDIA: 1) GeForce GTX 590; and 2) GeForce GTX TITAN. Experiments are conducted using both simulated and real data revealing considerable acceleration factors and obtaining good results in the task of compressing remotely sensed hyperspectral data sets.