|
Showing 1 - 25 of
27 matches in All Departments
This book presents select proceedings of National Conference on
Advances in Sustainable Construction Materials (ASCM 2020) and
examines a range of durable, energy-efficient, and next-generation
construction materials produced from industrial wastes and
by-products. The topics covered include sustainable materials and
construction, innovations in recycling concrete, green buildings
and innovative structures, utilization of waste materials in
construction, geopolymer concrete, self-compacting concrete by
using industrial waste materials, nanotechnology and sustainability
of concrete, environmental sustainability and development,
recycling solid wastes as road construction materials, emerging
sustainable practices in highway pavements construction, plastic
roads, pavement analysis and design, application of geosynthetics
for ground improvement, sustainability in offshore geotechnics,
green tunnel construction technology and application, ground
improvement techniques and municipal solid waste landfill. Given
the scope of contents, the book will be useful for researchers and
professionals working in the field of civil engineering and
especially sustainable structures and green buildings.
This book presents select proceedings of National Conference on
Advances in Sustainable Construction Materials (ASCM 2020) and
examines a range of durable, energy-efficient, and next-generation
construction materials produced from industrial wastes and
by-products. The topics covered include sustainable materials and
construction, innovations in recycling concrete, green buildings
and innovative structures, utilization of waste materials in
construction, geopolymer concrete, self-compacting concrete by
using industrial waste materials, nanotechnology and sustainability
of concrete, environmental sustainability and development,
recycling solid wastes as road construction materials, emerging
sustainable practices in highway pavements construction, plastic
roads, pavement analysis and design, application of geosynthetics
for ground improvement, sustainability in offshore geotechnics,
green tunnel construction technology and application, ground
improvement techniques and municipal solid waste landfill. Given
the scope of contents, the book will be useful for researchers and
professionals working in the field of civil engineering and
especially sustainable structures and green buildings.
This book highlights cutting-edge applications of machine learning
techniques for disaster management by monitoring, analyzing, and
forecasting hydro-meteorological variables. Predictive modelling is
a consolidated discipline used to forewarn the possibility of
natural hazards. In this book, experts from numerical weather
forecast, meteorology, hydrology, engineering, agriculture,
economics, and disaster policy-making contribute towards an
interdisciplinary framework to construct potent models for hazard
risk mitigation. The book will help advance the state of knowledge
of artificial intelligence in decision systems to aid disaster
management and policy-making. This book can be a useful reference
for graduate student, academics, practicing scientists and
professionals of disaster management, artificial intelligence, and
environmental sciences.
This book highlights cutting-edge applications of machine learning
techniques for disaster management by monitoring, analyzing, and
forecasting hydro-meteorological variables. Predictive modelling is
a consolidated discipline used to forewarn the possibility of
natural hazards. In this book, experts from numerical weather
forecast, meteorology, hydrology, engineering, agriculture,
economics, and disaster policy-making contribute towards an
interdisciplinary framework to construct potent models for hazard
risk mitigation. The book will help advance the state of knowledge
of artificial intelligence in decision systems to aid disaster
management and policy-making. This book can be a useful reference
for graduate student, academics, practicing scientists and
professionals of disaster management, artificial intelligence, and
environmental sciences.
This book presents the current trends, technologies, and challenges
in Big Data in the diversified field of engineering and sciences.
It covers the applications of Big Data ranging from conventional
fields of mechanical engineering, civil engineering to electronics,
electrical, and computer science to areas in pharmaceutical and
biological sciences. This book consists of contributions from
various authors from all sectors of academia and industries,
demonstrating the imperative application of Big Data for the
decision-making process in sectors where the volume, variety, and
velocity of information keep increasing. The book is a useful
reference for graduate students, researchers and scientists
interested in exploring the potential of Big Data in the
application of engineering areas.
This book presents the current trends, technologies, and challenges
in Big Data in the diversified field of engineering and sciences.
It covers the applications of Big Data ranging from conventional
fields of mechanical engineering, civil engineering to electronics,
electrical, and computer science to areas in pharmaceutical and
biological sciences. This book consists of contributions from
various authors from all sectors of academia and industries,
demonstrating the imperative application of Big Data for the
decision-making process in sectors where the volume, variety, and
velocity of information keep increasing. The book is a useful
reference for graduate students, researchers and scientists
interested in exploring the potential of Big Data in the
application of engineering areas.
Integrated Disaster Science and Management: Global Case Studies in
Mitigation and Recovery bridges the gap between scientific research
on natural disasters and the practice of disaster management. It
examines natural hazards, including earthquakes, landslides and
tsunamis, and uses integrated disaster management techniques,
quantitative methods and big data analytics to create early warning
models to mitigate impacts of these hazards and reduce the risk of
disaster. It also looks at mitigation as part of the recovery
process after a disaster, as in the case of the Nepal earthquake.
Edited by global experts in disaster management and engineering,
the book offers case studies that focus on the critical phases of
disaster management.
Risk, Reliability and Sustainable Remediation in the Field of Civil
and Environmental Engineering illustrates the concepts of risk,
reliability analysis, its estimation, and the decisions leading to
sustainable development in the field of civil and environmental
engineering. The book provides key ideas on risks in performance
failure and structural failures of all processes involved in civil
and environmental systems, evaluates reliability, and discusses the
implications of measurable indicators of sustainability in
important aspects of multitude of civil engineering projects. It
will help practitioners become familiar with tolerances in design
parameters, uncertainties in the environment, and applications in
civil and environmental systems. Furthermore, the book emphasizes
the importance of risks involved in design and planning stages and
covers reliability techniques to discover and remove the potential
failures to achieve a sustainable development.
Water Engineering Modeling and Mathematic Tools provides an
informative resource for practitioners who want to learn more about
different techniques and models in water engineering and their
practical applications and case studies. The book provides
modelling theories in an easy-to-read format verified with on-site
models for specific regions and scenarios. Users will find this to
be a significant contribution to the development of mathematical
tools, experimental techniques, and data-driven models that support
modern-day water engineering applications. Civil engineers,
industrialists, and water management experts should be familiar
with advanced techniques that can be used to improve existing
systems in water engineering. This book provides key ideas on
recently developed machine learning methods and AI modelling. It
will serve as a common platform for practitioners who need to
become familiar with the latest developments of computational
techniques in water engineering.
Modeling in Geotechnical Engineering is a one stop reference for a
range of computational models, the theory explaining how they work,
and case studies describing how to apply them. Drawing on the
expertise of contributors from a range of disciplines including
geomechanics, optimization, and computational engineering, this
book provides an interdisciplinary guide to this subject which is
suitable for readers from a range of backgrounds. Before tackling
the computational approaches, a theoretical understanding of the
physical systems is provided that helps readers to fully grasp the
significance of the numerical methods. The various models are
presented in detail, and advice is provided on how to select the
correct model for your application.
Basics of Computational Geophysics provides a one-stop, collective
resource for practitioners on the different techniques and models
in geoscience, their practical applications, and case studies. The
reference provides the modeling theory in an easy-to-read format
that is verified with onsite models for specific regions and
scenarios, including the use of big data and artificial
intelligence. This book offers a platform whereby readers will
learn theory, practical applications, and the comparison of
real-world problems surrounding geomechanics, modeling and
optimizations.
Predictive Modeling for Energy Management and Power Systems
Engineering introduces readers to the cutting-edge use of big data
and large computational infrastructures in energy demand estimation
and power management systems. The book supports engineers and
scientists who seek to become familiar with advanced optimization
techniques for power systems designs, optimization techniques and
algorithms for consumer power management, and potential
applications of machine learning and artificial intelligence in
this field. The book provides modeling theory in an easy-to-read
format, verified with on-site models and case studies for specific
geographic regions and complex consumer markets.
Data Analytics in Biomedical Engineering and Healthcare explores
key applications using data analytics, machine learning, and deep
learning in health sciences and biomedical data. The book is useful
for those working with big data analytics in biomedical research,
medical industries, and medical research scientists. The book
covers health analytics, data science, and machine and deep
learning applications for biomedical data, covering areas such as
predictive health analysis, electronic health records, medical
image analysis, computational drug discovery, and genome structure
prediction using predictive modeling. Case studies demonstrate big
data applications in healthcare using the MapReduce and Hadoop
frameworks.
New Materials in Civil Engineering provides engineers and
scientists with the tools and methods needed to meet the challenge
of designing and constructing more resilient and sustainable
infrastructures. This book is a valuable guide to the properties,
selection criteria, products, applications, lifecycle and
recyclability of advanced materials. It presents an A-to-Z approach
to all types of materials, highlighting their key performance
properties, principal characteristics and applications. Traditional
materials covered include concrete, soil, steel, timber, fly ash,
geosynthetic, fiber-reinforced concrete, smart materials, carbon
fiber and reinforced polymers. In addition, the book covers
nanotechnology and biotechnology in the development of new
materials.
This book presents a broad range of deep-learning applications
related to vision, natural language processing, gene expression,
arbitrary object recognition, driverless cars, semantic image
segmentation, deep visual residual abstraction, brain-computer
interfaces, big data processing, hierarchical deep learning
networks as game-playing artefacts using regret matching, and
building GPU-accelerated deep learning frameworks. Deep learning,
an advanced level of machine learning technique that combines class
of learning algorithms with the use of many layers of nonlinear
units, has gained considerable attention in recent times. Unlike
other books on the market, this volume addresses the challenges of
deep learning implementation, computation time, and the complexity
of reasoning and modeling different type of data. As such, it is a
valuable and comprehensive resource for engineers, researchers,
graduate students and Ph.D. scholars.
Handbook of Neural Computation explores neural computation
applications, ranging from conventional fields of mechanical and
civil engineering, to electronics, electrical engineering and
computer science. This book covers the numerous applications of
artificial and deep neural networks and their uses in learning
machines, including image and speech recognition, natural language
processing and risk analysis. Edited by renowned authorities in
this field, this work is comprised of articles from reputable
industry and academic scholars and experts from around the world.
Each contributor presents a specific research issue with its recent
and future trends. As the demand rises in the engineering and
medical industries for neural networks and other machine learning
methods to solve different types of operations, such as data
prediction, classification of images, analysis of big data, and
intelligent decision-making, this book provides readers with the
latest, cutting-edge research in one comprehensive text.
Handbook of Probabilistic Models carefully examines the application
of advanced probabilistic models in conventional engineering
fields. In this comprehensive handbook, practitioners, researchers
and scientists will find detailed explanations of technical
concepts, applications of the proposed methods, and the respective
scientific approaches needed to solve the problem. This book
provides an interdisciplinary approach that creates advanced
probabilistic models for engineering fields, ranging from
conventional fields of mechanical engineering and civil
engineering, to electronics, electrical, earth sciences, climate,
agriculture, water resource, mathematical sciences and computer
sciences. Specific topics covered include minimax probability
machine regression, stochastic finite element method, relevance
vector machine, logistic regression, Monte Carlo simulations,
random matrix, Gaussian process regression, Kalman filter,
stochastic optimization, maximum likelihood, Bayesian inference,
Bayesian update, kriging, copula-statistical models, and more.
The disciplines of science and engineering rely heavily on the
forecasting of prospective constraints for concepts that have not
yet been proven to exist, especially in areas such as artificial
intelligence. Obtaining quality solutions to the problems presented
becomes increasingly difficult due to the number of steps required
to sift through the possible solutions, and the ability to solve
such problems relies on the recognition of patterns and the
categorization of data into specific sets. Predictive modeling and
optimization methods allow unknown events to be categorized based
on statistics and classifiers input by researchers. The Handbook of
Research on Predictive Modeling and Optimization Methods in Science
and Engineering is a critical reference source that provides
comprehensive information on the use of optimization techniques and
predictive models to solve real-life engineering and science
problems. Through discussions on techniques such as robust design
optimization, water level prediction, and the prediction of human
actions, this publication identifies solutions to developing
problems and new solutions for existing problems, making this
publication a valuable resource for engineers, researchers,
graduate students, and other professionals.
The development of new and effective analytical and numerical
models is essential to understanding the performance of a variety
of structures. As computational methods continue to advance, so too
do their applications in structural performance modeling and
analysis. Modeling and Simulation Techniques in Structural
Engineering presents emerging research on computational techniques
and applications within the field of structural engineering. This
timely publication features practical applications as well as new
research insights and is ideally designed for use by engineers, IT
professionals, researchers, and graduate-level students.
Recent developments in information processing systems have driven
the advancement of computational methods in the engineering realm.
New models and simulations enable better solutions for
problem-solving and overall process improvement. The Handbook of
Research on Advanced Computational Techniques for Simulation-Based
Engineering is an authoritative reference work representing the
latest scholarly research on the application of computational
models to improve the quality of engineering design. Featuring
extensive coverage on a range of topics from various engineering
disciplines, including, but not limited to, soft computing methods,
comparative studies, and hybrid approaches, this book is a
comprehensive reference source for students, professional
engineers, and researchers interested in the application of
computational methods for engineering design.
The rapid advance in information processing systems in recent
decades directed engineering research towards the development of
data driven models that can model natural phenomena automatically.
This book describes the different types of data driven model
(Artificial Neural Network, Support Vector Machine, Relevance
Vector Machine, Least Square Support Vector Machine, Multivariate
Adaptive Regression Spline, Genetic Programming, etc.)
The determination of Elastic Modulus (E) of normal strength
concrete is an important task in civil engineering for
infrastructure development. Experimental methods for determination
of E value of normal strength concrete are complicated and time
consuming. This article employs an Artificial Intelligence (AI)
technique for prediction of E value of normal strength concrete.
The results are compared with a widely used Artificial Neural
Network (ANN), Support Vector Machine (SVM) model and empirical
equation from the different buildings codes. Equations have been
also developed for determination of E value of normal strength
concrete based on the AI. The developed AI model also gives error
bar of predicted E value. The predicted error bar can be used to
determine model uncertainty. This study shows that the developed AI
is a robust model for prediction of E value of normal strength
concrete.
Earthquake engineers particularly in seismology and geotechnical
engineering use a lot of empirical formulas to solve various
earthquake engineering problems. These empirical formulas have lot
of uncertainty. So, the available empirical formulas are not so
reliable. This uncertainly can be minimized by using these new AI
techniques. Therefore earthquake engineers should familiar with
these new AI techniques. They can use the proposed book to know the
application of these new AI techniques for different earthquake
engineering problems. The application of these new AI techniques is
very limited in earthquake engineering. These new techniques have
been proved better than ANN in other engineering fields. So, it is
necessary to investigate the feasibility of these new AI techniques
in earthquake engineering.
The rapid advance in information processing systems in recent
decades directed engineering research towards the development of
various soft computing techniques that can model natural phenomena
automatically. The new soft computing techniques (SVM, LSSVM, RVM,
MARS) is being applied successfully to solve different engineering
problems. But, the application of these new soft computing
techniques in geotechnical engineering is very little. The proposed
book will give an idea of these new soft computing techniques to
geotechnical community. The application of these new soft computing
techniques in geotechnical engineering will also be described. The
book is aimed at researchers and practitioners who are engaged in
developing and applying soft computing tools to solving
geotechnical engineering problems. It is also suitable as wider
reading for geotechnical engineering postgraduate students.
|
|