Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Piezoelectric wafer active sensors may be applied on aging aircraft structures to monitor the onset and progress of structural damage such as fatigue cracks and corrosion. The state of the art in piezoelectric-wafer active sensors structural health monitoring and damage detection is reviewed. Methods based on (a) elastic wave propagation and (b) the Electro–Mechanical (E/M) impedance technique are cited and briefly discussed. For health monitoring of aging aircraft structures, two main detection strategies are considered: the E/M impedance method for near field damage detection, and wave propagation methods for far-field damage detection. These methods are developed and verified on simple-geometry specimens and on realistic aging aircraft panels with seeded cracks and corrosion. The experimental methods, signal processing, and damage detection algorithms are tuned to the specific method used for structural interrogation. In the E/M impedance method approach, the high-frequency spectrum, representative of the structural resonances, is recorded. Then, overallstatistics damage metrics can be used to compare the impedance signatures and correlate the change in these signatures with the damage progression and intensity. In our experiments, the (1 � R 2 ) 3 damage metric was found to best fit the results in the 300–450 kHz band. In the wave propagation approach, the pulse-echo and acousto-ultrasonic methods can be utilized to identify the additional reflections generated from crack damage and the changes in transmission phase and velocity associated with corrosion damage. The paper ends with a conceptual design of a structural health monitoring system and suggestions for aging aircraft installation utilizing active-sensor arrays, data concentrators, wireless transmission, and a health monitoring and processing unit.

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Piezoelectric Wafer Embedded Active Sensors for Aging Aircraft Structural Health Monitoring

The review outlines modern trends in theoretical developments, novel designs and modern applications of sandwich structures. The most recent work published at the time of writing of this review is considered, older sources are listed only on as-needed basis. The review begins with the discussion on the analytical models and methods of analysis of sandwich structures as well as representative problems utilizing or comparing these models. Novel designs of sandwich structures is further elucidated concentrating on miscellaneous cores, introduction of nanotubes and smart materials in the elements of a sandwich structure as well as using functionally graded designs. Examples of problems experienced by developers and designers of sandwich structures, including typical damage, response under miscellaneous loads, environmental effects and fire are considered. Sample applications of sandwich structures included in the review concentrate on aerospace, civil and marine engineering, electronics and biomedical areas. Finally, the authors suggest a list of areas where they envision a pressing need in further research.

Review of current trends in research and applications of sandwich structures

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Formulas For Natural Frequency And Mode Shape

This paper describes the utilization of Electro-Mechanical (E/M) impedance method for structural health monitoring of thin plates. The method allows the direct identification of structural dynamics by obtaining its E/M impedance or admittance signatures. The analytical model for two-dimensions structure was developed and verified with experiments. Good matching of experimental results and calculated spectra was obtained for axial and flexural components. The ability of the method to identify the presence of damage was investigated by performing an experiment where the damage in the form of crack was simulated with An EDM slit placed at various distances from the sensor. It was found that the crack presence dramatically modifies the E/M impedance spectrum and this modification decreases as the distance between the sensor and the crack increases. Several overall-statistics damage metrics, which may be used for on-line structural heath monitoring, were investigated. Among these candidate damage metrics, the α-th power of the correlation coefficient deviation, CCD α , 3 < α < 7, used in the high frequency band 300-450 kHz, was found to be most successful. Careful selection of the high frequency band and proper choice of the appropriate damage metric were found to be essential for successful damage detection and structural health monitoring.

/pdf/electro-mechanical-impedance-method-for-crack-detection-in-1gidn3si3z.pdf

Electro-Mechanical Impedance Method for Crack Detection in Thin Plates

This paper reviews the most significant works in literature about the acoustic behaviour of sandwich panels, starting from the first examples of multi-layered structures, comprising a series of dif...

A review of the vibroacoustics of sandwich panels: Models and experiments

Similitude theory allows engineers, through a set of tools known as similitude methods, to establish the necessary conditions to design a scaled (up or down) model of a full-scale prototype structure. In recent years, to overcome the obstacles associated with full-scale testing, such as cost and setup, research on similitude methods has grown and their application has expanded into many branches of engineering. The aim of this paper is to provide as comprehensive a review as possible about similitude methods applied to structural engineering and their limitations due to size effects, rate sensitivity phenomena, etc. After a brief historical introduction and a more in-depth analysis of the main methods, the paper focuses on similitude applications classified, first, by test article, then by engineering fields.

/pdf/a-review-of-similitude-methods-for-structural-engineering-4jwsb4rcyf.pdf

A Review of Similitude Methods for Structural Engineering

Exact and numerical responses of a plate under a turbulent boundary layer excitation

http://paginas.fe.up.pt/~icnmmcs/abstracts/260.pdf

Francesco Franco

Papers

A review of the vibroacoustics of sandwich panels: Models and experiments

A Review of Similitude Methods for Structural Engineering

Exact and numerical responses of a plate under a turbulent boundary layer excitation

Behaviour of fibre-reinforced honeycomb core under low velocity impact loading

A scaling procedure for the response of an isolated system with high modal overlap factor