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Nanotechnology in Civil Infrastructure is a state-of-the art
reference source describing the latest developments in
nano-engineering and nano-modification of construction materials to
improve the bulk properties, development of sustainable,
intelligent, and smart concrete materials through the integration
of nanotechnology based self-sensing and self-powered materials and
cyber infrastructure technologies, review of nanotechnology
applications in pavement engineering, development of novel,
cost-effective, high-performance and long-lasting concrete products
and processes through nanotechnology-based innovative processing of
cement and cement paste, and advanced nanoscience modeling,
visualization, and measurement systems for characterizing and
testing civil infrastructure materials at the nano-scale.
Researchers, practitioners, undergraduate and graduate students
engaged in nanotechnology related research will find this book very
useful.
The term "soft computing" applies to variants of and combinations
under the four broad categories of evolutionary computing, neural
networks, fuzzy logic, and Bayesian statistics. Although each one
has its separate strengths, the complem- tary nature of these
techniques when used in combination (hybrid) makes them a powerful
alternative for solving complex problems where conventional mat-
matical methods fail. The use of intelligent and soft computing
techniques in the field of geo- chanical and pavement engineering
has steadily increased over the past decade owing to their ability
to admit approximate reasoning, imprecision, uncertainty and
partial truth. Since real-life infrastructure engineering decisions
are made in ambiguous environments that require human expertise,
the application of soft computing techniques has been an attractive
option in pavement and geomecha- cal modeling. The objective of
this carefully edited book is to highlight key recent advances made
in the application of soft computing techniques in pavement and
geo- chanical systems. Soft computing techniques discussed in this
book include, but are not limited to: neural networks, evolutionary
computing, swarm intelligence, probabilistic modeling, kernel
machines, knowledge discovery and data mining, neuro-fuzzy systems
and hybrid approaches. Highlighted application areas include
infrastructure materials modeling, pavement analysis and design,
rapid interpre- tion of nondestructive testing results, porous
asphalt concrete distress modeling, model parameter identification,
pavement engineering inversion problems, s- grade soils
characterization, and backcalculation of pavement layer thickness
and moduli.
Nanotechnology is a rapidly evolving field finding newer and newer
areas of application that remained unexplored previously. In the
area of civil infrastructure systems such as buildings, roads, and
bridges, there is a drive towards understanding the behavior of
component materials and their interactions at the molecular or
nano-level to manipulate and effect macro-level changes to engineer
designer or smart materials. Nano-engineering and nano-modification
of concrete and bituminous materials have far-reaching implications
allowing the development of cost-effective, high-performance, and
long-lasting products and processes for civil infrastructure within
the ideals of sustainable development. This book focuses on the
latest advances made in the development and characterization of
nanotechnology based civil engineering materials, structures, and
systems. Specific topics discussed in this book include nanoscience
modeling to understand the atomic structure of C-S-H, the effect of
nanomaterials on cement hydration and reinforcement,
multifunctional concrete and Carbon Nanotube (CNT) reinforced
cementitious systems, nano-optimized construction materials by
nano-seeding, moisture damage characterization of asphalt materials
using Atomic Force Microscopy (AFM) and nanoindentation,
nanoclay-modified asphalt binder systems, etc.
The term "soft computing" applies to variants of and combinations
under the four broad categories of evolutionary computing, neural
networks, fuzzy logic, and Bayesian statistics. Although each one
has its separate strengths, the complem- tary nature of these
techniques when used in combination (hybrid) makes them a powerful
alternative for solving complex problems where conventional mat-
matical methods fail. The use of intelligent and soft computing
techniques in the field of geo- chanical and pavement engineering
has steadily increased over the past decade owing to their ability
to admit approximate reasoning, imprecision, uncertainty and
partial truth. Since real-life infrastructure engineering decisions
are made in ambiguous environments that require human expertise,
the application of soft computing techniques has been an attractive
option in pavement and geomecha- cal modeling. The objective of
this carefully edited book is to highlight key recent advances made
in the application of soft computing techniques in pavement and
geo- chanical systems. Soft computing techniques discussed in this
book include, but are not limited to: neural networks, evolutionary
computing, swarm intelligence, probabilistic modeling, kernel
machines, knowledge discovery and data mining, neuro-fuzzy systems
and hybrid approaches. Highlighted application areas include
infrastructure materials modeling, pavement analysis and design,
rapid interpre- tion of nondestructive testing results, porous
asphalt concrete distress modeling, model parameter identification,
pavement engineering inversion problems, s- grade soils
characterization, and backcalculation of pavement layer thickness
and moduli.
Data used to develop and confirm models suffer from several
shortcomings: the total data is too limited, the data are
non-stationary, and the data represent nonlinear processes. The
Hilbert-Huang transform (HHT) is a relatively new method that has
grown into a robust tool for data analysis and is ready for a wide
variety of applications. This text presents the first thorough
presentation of the formulation and application of the
Hilbert-Huang Transform (HHT) in engineering. After an introduction
and overview of recent advances, thirty leading international
experts explore the use of the HHT in areas such as oceanography,
nonlinear soil amplification, and non-stationary random processes.
One chapter offers a comparative analysis between HHT wavelet and
Fourier transforms, and another looks at the HHT applied to
molecular dynamic simulations. The final chapter provides
perspectives on the theory and practice of HHT and reviews
applications in disciplines ranging from biomedical, chemical, and
financial engineering to meteorology and seismology. The
Hilbert-Huang Transform in Engineering features a variety of modern
topics, and the examples presented include wide-ranging, real-life
engineering problems. While the development of the HHT is not yet
complete, this book clearly demonstrates the power and utility of
the method and will undoubtedly stimulate further interest,
theoretical advances, and innovative applications.
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