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The use of intelligent and soft computing techniques in the field of geomechanical 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 geomechanical 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 geomechanical 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 interpretation of nondestructive testing results, porous asphalt concrete distress modeling, model parameter identification, pavement engineering inversion problems, subgrade soils characterization, and backcalculation of pavement layer thickness and moduli. Researchers and practitioners engaged in developing and applying soft computing and intelligent systems principles to solving real-world infrastructure engineering problems will find this book very useful. This book will also serve as an excellent state-of-the-art reference material for graduate and postgraduate students in transportation infrastructure engineering.

Soft computing --- Pavements --- Systems engineering --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Computer Science --- Civil Engineering --- Applied Mathematics --- Design and construction --- Production engineering. --- Information technology. --- IT (Information technology) --- Manufacturing engineering --- Process engineering --- Computer science. --- Artificial intelligence. --- Applied mathematics. --- Engineering mathematics. --- Engineering economics. --- Engineering economy. --- Buildings --- Building. --- Construction. --- Engineering, Architectural. --- Engineering geology. --- Engineering --- Foundations. --- Hydraulics. --- Computer Science. --- Artificial Intelligence (incl. Robotics). --- Building Construction. --- Geoengineering, Foundations, Hydraulics. --- Appl.Mathematics/Computational Methods of Engineering. --- Engineering Economics, Organization, Logistics, Marketing. --- Design and construction. --- Geology. --- Flow of water --- Water --- Fluid mechanics --- Hydraulic engineering --- Jets --- Architecture --- Building --- Structural engineering --- Underground construction --- Caissons --- Earthwork --- Masonry --- Soil consolidation --- Soil mechanics --- Walls --- Civil engineering --- Geology, Economic --- Economy, Engineering --- Engineering economics --- Industrial engineering --- Architectural engineering --- Construction --- Construction science --- Engineering, Architectural --- Structural design --- Construction industry --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Engineering analysis --- Mathematical analysis --- Informatics --- Science --- Flow --- Distribution --- Details --- Geology --- Mathematics --- Technology --- Telematics --- Information superhighway --- Knowledge management --- Mechanical engineering --- Hydraulic engineering. --- Artificial Intelligence. --- Building Construction and Design. --- Mathematical and Computational Engineering. --- Engineering, Hydraulic --- Hydraulics --- Shore protection --- Buildings—Design and construction. --- Engineering—Geology.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The use of intelligent and soft computing techniques in the field of geomechanical 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 geomechanical 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 geomechanical 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 interpretation of nondestructive testing results, porous asphalt concrete distress modeling, model parameter identification, pavement engineering inversion problems, subgrade soils characterization, and backcalculation of pavement layer thickness and moduli. Researchers and practitioners engaged in developing and applying soft computing and intelligent systems principles to solving real-world infrastructure engineering problems will find this book very useful. This book will also serve as an excellent state-of-the-art reference material for graduate and postgraduate students in transportation infrastructure engineering.

Industrial economics --- Engineering sciences. Technology --- Artificial intelligence. Robotics. Simulation. Graphics --- analyse (wiskunde) --- KI (kunstmatige intelligentie) --- industriële marketing --- ingenieurswetenschappen --- robots