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.

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.

With an eye towards environmental sustainability, this book presents a step-by-step approach to formulating the resilience of civil infrastructure and energy systems. It provides a concise explanation of resilience terminology, a general overview of theoretical models and analyses, and a clear guide to practical applications, covering critical topics such as interdependent infrastructures and geologic carbon sequestration. Additionally, it contains a general introduction to selected data science topics, so readers can acquire the tools to formulate and analyze resilience engineering problems in depth. Informed by the author's extensive practical experience and thorough academic background, this book includes examples to illustrate key computational algorithms, end-of-chapter exercises and references, and an entire chapter devoted to case studies. Intended for practitioners of resilience engineering with a background in probability, this book offers a balanced blend of background, theory, and practical examples to address one of the most important emerging topics of modern times.

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.