ecco un esempio complilabile
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\begin{document}
\chapter{capitolo 1}
\lipsum
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non mi allega il file della bibliografia, vi metto tutto il codice
`@inbook{Aktan_Chase_Inman_Pines_Spie_2001,
title={Monitoring and managing the health of infrastructure systems},
volume={4337},
booktitle={Health Monitoring and Management of Civil Infrastructure Systems},
publisher={SPIE},
author={Aktan, A E and Chase, S and Inman, D and Pines, D and Spie},
year={2001},
pages={xi–xxi}}
@article{Chang1999,
author = {Chang, F. K.},
title = {A summary report of the 2nd workshop on structural health monitoring held at Stanford University on September 8–10},
year = {1999},
journal = {Report AO83483}
}
@article{Rytter1993,
author = {Rytter, A.},
title = { Vibration based inspection of civil engineering structures. Ph.D. Dissertation, Department of Building Technology and Structural Engineering, Aalborg University, Denmark.},
year = {1993},
}
@article{Yin15092006,
author = {Yin, Shih-Hsun and Epureanu, Bogdan I},
title = {Structural health monitoring based on sensitivity vector fields and attractor morphing},
volume = {364},
number = {1846},
pages = {2515-2538},
year = {2006},
doi = {10.1098/rsta.2006.1838},
abstract ={The dynamic responses of a thermo-shielding panel forced by unsteady aerodynamic loads and a classical Duffing oscillator are investigated to detect structural damage. A nonlinear aeroelastic model is obtained for the panel by using third-order piston theory to model the unsteady supersonic flow, which interacts with the panel. To identify damage, we analyse the morphology (deformation and movement) of the attractor of the dynamics of the aeroelastic system and the Duffing oscillator. Damages of various locations, extents and levels are shown to be revealed by the attractor-based analysis. For the panel, the type of damage considered is a local reduction in the bending stiffness. For the Duffing oscillator, variations in the linear and nonlinear stiffnesses and damping are considered as damage. Present studies of such problems are based on linear theories. In contrast, the presented approach using nonlinear dynamics has the potential of enhancing accuracy and sensitivity of detection.},
eprint = {http://rsta.royalsocietypublishing.org/content/364/1846/2515.full.pdf+html}},
journal = {Philosophical Transactions of the Royal Society A: Mathematical,
Physical and Engineering Sciences}
}
@article{Worden08062007,
author = {Worden, Keith and Farrar, Charles R and Manson, Graeme and Park, Gyuhae},
title = {The fundamental axioms of structural health monitoring},
volume = {463},
number = {2082},
pages = {1639-1664},
year = {2007},
doi = {10.1098/rspa.2007.1834},
abstract ={Based on the extensive literature that has developed on structural health monitoring over the last 20 years, it can be argued that this field has matured to the point where several fundamental axioms, or gen eral principles, have emerged. The intention of this paper is to explicitly state and justify these axioms. In so doing, it is hoped that two subsequent goals are facilitated. First, the statement of such axioms will give new researchers in the field a starting point that alleviates the need to review the vast amounts of literature in this field. Second, the authors hope to stimulate discussion and thought within the community regarding these axioms.},
eprint = {http://rspa.royalsocietypublishing.org/content/463/2082/1639.full.pdf+html},
journal = {Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science}
}
@article{Farrar15012001,
author = {Farrar, Charles R. and Doebling, Scott W. and Nix, David A.},
title = {Vibration–based structural damage identification},
volume = {359},
number = {1778},
pages = {131-149},
year = {2001},
doi = {10.1098/rsta.2000.0717},
abstract ={Many aerospace, civil and mechanical systems continue to be used despite ageing and the associated potential for damage accumulation. Therefore, the ability to monitor the structural health of these systems is becoming increasingly important. A wide variety of highly effective local non–destructive evaluation tools is available. However, damage identification based upon changes in vibration characteristics is one of the few methods that monitor changes in the structure on a global basis. A summary of developments in the field of global structural health monitoring that have taken place over the last thirty years is first presented. Vibration–based damage detection is a primary tool that is employed for this monitoring. Next, the process of vibration based damage detection will be described as a problem in statistical pattern recognition. This process is composed of three portions: (i) data acquisition and cleansing; (ii) feature selection and data compression; and (iii) statistical model development. Current research regarding feature selection and statistical model development will be emphasized with the application of this technology to a large–scale laboratory structure.},
eprint = {http://rsta.royalsocietypublishing.org/content/359/1778/131.full.pdf+html},
journal = {Philosophical Transactions of the Royal Society of London. Series A:
Mathematical, Physical and Engineering Sciences}
}
@article{Worden01032004,
author = {Worden, K. and Dulieu-Barton, J.M.},
title = {An Overview of Intelligent Fault Detection in Systems and Structures},
volume = {3},
number = {1},
pages = {85-98},
year = {2004},
doi = {10.1177/1475921704041866},
abstract ={This paper describes a coherent strategy for intelligent fault detection. All of the features of the strategy are discussed in detail. These encompass: (i) a taxonomy for the relevant concepts, i.e. a precise definition of what constitutes a fault etc., (ii) a specification for operational evaluation which makes use of a hierarchical damage identification scheme, (iii) an approach to sensor prescription and optimisation and (iv) a data processing methodology based on a data fusion model.},
eprint = {http://shm.sagepub.com/content/3/1/85.full.pdf+html},
journal = {Structural Health Monitoring}
}
@article{Farrar15022007.303,
author = {Farrar, Charles R and Worden, Keith},
title = {An introduction to structural health monitoring},
volume = {365},
number = {1851},
pages = {303-315},
year = {2007},
doi = {10.1098/rsta.2006.1928},
abstract ={The process of implementing a damage identification strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring (SHM). Here, damage is defined as changes to the material and/or geometric properties of these systems, including changes to the boundary conditions and system connectivity, which adversely affect the system's performance. A wide variety of highly effective local non-destructive evaluation tools are available for such monitoring. However, the majority of SHM research conducted over the last 30 years has attempted to identify damage in structures on a more global basis. The past 10 years have seen a rapid increase in the amount of research related to SHM as quantified by the significant escalation in papers published on this subject. The increased interest in SHM and its associated potential for significant life-safety and economic benefits has motivated the need for this theme issue.This introduction begins with a brief history of SHM technology development. Recent research has begun to recognize that the SHM problem is fundamentally one of the statistical pattern recognition (SPR) and a paradigm to address such a problem is described in detail herein as it forms the basis for organization of this theme issue. In the process of providing the historical overview and summarizing the SPR paradigm, the subsequent articles in this theme issue are cited in an effort to show how they fit into this overview of SHM. In conclusion, technical challenges that must be addressed if SHM is to gain wider application are discussed in a general manner.},
eprint = {http://rsta.royalsocietypublishing.org/content/365/1851/303.full.pdf+html},
journal = {Philosophical Transactions of the Royal Society A: Mathematical,
Physical and Engineering Sciences}
}
@article{Worden15022007,
author = {Worden, Keith and Manson, Graeme},
title = {The application of machine learning to structural health monitoring},
volume = {365},
number = {1851},
pages = {515-537},
year = {2007},
doi = {10.1098/rsta.2006.1938},
abstract ={In broad terms, there are two approaches to damage identification. Model-driven methods establish a high-fidelity physical model of the structure, usually by finite element analysis, and then establish a comparison metric between the model and the measured data from the real structure. If the model is for a system or structure in normal (i.e. undamaged) condition, any departures indicate that the structure has deviated from normal condition and damage is inferred. Data-driven approaches also establish a model, but this is usually a statistical representation of the system, e.g. a probability density function of the normal condition. Departures from normality are then signalled by measured data appearing in regions of very low density. The algorithms that have been developed over the years for data-driven approaches are mainly drawn from the discipline of pattern recognition, or more broadly, machine learning. The object of this paper is to illustrate the utility of the data-driven approach to damage identification by means of a number of case studies.},
eprint = {http://rsta.royalsocietypublishing.org/content/365/1851/515.full.pdf+html},
journal = {Philosophical Transactions of the Royal Society A: Mathematical,
Physical and Engineering Sciences}
}
@article{Sohn15022007,
author = {Sohn, Hoon},
title = {Effects of environmental and operational variability on structural health monitoring},
volume = {365},
number = {1851},
pages = {539-560},
year = {2007},
doi = {10.1098/rsta.2006.1935},
abstract ={Stated in its most basic form, the objective of structural health monitoring is to ascertain if damage is present or not based on measured dynamic or static characteristics of a system to be monitored. In reality, structures are subject to changing environmental and operational conditions that affect measured signals, and these ambient variations of the system can often mask subtle changes in the system's vibration signal caused by damage. Data normalization is a procedure to normalize datasets, so that signal changes caused by operational and environmental variations of the system can be separated from structural changes of interest, such as structural deterioration or degradation. This paper first reviews the effects of environmental and operational variations on real structures as reported in the literature. Then, this paper presents research progresses that have been made in the area of data normalization.},
eprint = {http://rsta.royalsocietypublishing.org/content/365/1851/539.full.pdf+html},
journal = {Philosophical Transactions of the Royal Society A: Mathematical,
Physical and Engineering Sciences}
}
@article{Hayton15022007,
author = {Hayton, Paul and Utete, Simukai and King, Dennis and King, Steve and Anuzis, Paul and Tarassenko, Lionel},
title = {Static and dynamic novelty detection methods for jet engine health monitoring},
volume = {365},
number = {1851},
pages = {493-514},
year = {2007},
doi = {10.1098/rsta.2006.1931},
abstract ={Novelty detection requires models of normality to be learnt from training data known to be normal. The first model considered in this paper is a static model trained to detect novel events associated with changes in the vibration spectra recorded from a jet engine. We describe how the distribution of energy across the harmonics of a rotating shaft can be learnt by a support vector machine model of normality. The second model is a dynamic model partially learnt from data using an expectation–maximization-based method. This model uses a Kalman filter to fuse performance data in order to characterize normal engine behaviour. Deviations from normal operation are detected using the normalized innovations squared from the Kalman filter.},
eprint = {http://rsta.royalsocietypublishing.org/content/365/1851/493.full.pdf+html},
journal = {Philosophical Transactions of the Royal Society A: Mathematical,
Physical and Engineering Sciences}
}
@inproceedings{Kim2007,
author = {Kim, Sukun and Pakzad, Shamim and Culler, David and Demmel, James and Fenves, Gregory and Glaser, Steven and Turon, Martin},
title = {Health monitoring of civil infrastructures using wireless sensor networks},
booktitle = {Proceedings of the 6th international conference on Information processing in sensor networks},
series = {IPSN '07},
year = {2007},
isbn = {978-1-59593-638-7},
location = {Cambridge, Massachusetts, USA},
pages = {254–263},
numpages = {10},
doi = {http://doi.acm.org/10.1145/1236360.1236395},
acmid = {1236395},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {deployment, large-scale, structural health monitoring, wireless sensor networks},
}
@article{ross1995a,
author = {Ross, R.M. and Matthews, S.L.},
title = {In-service structural monitoring—a state of the art review. Struct. Eng. 73},
number = {73},
pages = {23–31},
year = {1995},
}
@article{mita1999,
author = {Mita, A.},
title = {Emerging needs in Japan for health monitoring technologies in civil and building structures.In Proc. 2nd Int. workshop on structural health monitoring, Stanford University. },
year = {1999},
}
@article{Brownjohn15022007,
author = {Brownjohn, J.M.W},
title = {Structural health monitoring of civil infrastructure},
volume = {365},
number = {1851},
pages = {589-622},
year = {2007},
doi = {10.1098/rsta.2006.1925},
abstract ={Structural health monitoring (SHM) is a term increasingly used in the last decade to describe a range of systems implemented on full-scale civil infrastructures and whose purposes are to assist and inform operators about continued ‘fitness for purpose’ of structures under gradual or sudden changes to their state, to learn about either or both of the load and response mechanisms. Arguably, various forms of SHM have been employed in civil infrastructure for at least half a century, but it is only in the last decade or two that computer-based systems are being designed for the purpose of assisting owners/operators of ageing infrastructure with timely information for their continued safe and economic operation.This paper describes the motivations for and recent history of SHM applications to various forms of civil infrastructure and provides case studies on specific types of structure. It ends with a discussion of the present state-of-the-art and future developments in terms of instrumentation, data acquisition, communication systems and data mining and presentation procedures for diagnosis of infrastructural ‘health’.},
eprint = {http://rsta.royalsocietypublishing.org/content/365/1851/589.full.pdf+html},
journal = {Philosophical Transactions of the Royal Society A: Mathematical,
Physical and Engineering Sciences}
}
`