Civil Engineer's Reference Book


Book Description

After an examination of fundamental theories as applied to civil engineering, authoritative coverage is included on design practice for certain materials and specific structures and applications. A particular feature is the incorporation of chapters on construction and site practice, including contract management and control.







Civil Engineer's Illustrated Sourcebook


Book Description

PUT A WEALTH OF INFORMATIVE ENGINEERING INFO RIGHT AT YOUR FINGERTIPS—ALL IN A SINGLE, HANDY VOLUME! When it comes to civil engineering, handy access to the right schematics and plans can mean the difference between a winning idea—and a concept that dies on the drawing board. That’s why if adding efficiencies to your work as an engineer is essential, McGraw-Hill’s Civil Engineer’s Illustrated Sourcebook is the one volume you shouldn't be without. Written by a noted engineering expert with lengthy consultative experience, Civil Engineer’s Illustrated Sourcebook provides practical, step-by-step information on a broad array of engineering processes. From planning, materials, and design to bidding, construction, and more, this book will show how using a consistent organizational methodology will add power and quality to your work. Plus, the book also delivers: * Practical charts, tables, plans, and other data encountered in everyday practice * Plan layouts from actual engineering projects * Source material from a wide variety of engineering projects * And much, much more! Robust enough for civil engineers, contractors, technicians, and architects—and still relevant for students pursing engineering degrees and certifications—Civil Engineer’s Illustrated Sourcebook will add a world of invaluable insight to how you do your work! Packed with 900 informative illustrations!: PLANNING Technical Reports Project Scheduling Field Reconnaissance Surveying and Mapping Public Meetings Regulatory Approvals Cost Estimating DESIGN Title Sheet organization Buildings Water Supply and Distribution Fire Protection Wastewater Collection and Treatment Storm Water Systems Dams and Reservoirs Streets, Roads, and Highways Bridges Airports Athletic Facilities Trailer Courts and Campgrounds Retrofitting and Rehabilitation Specialized Projects Standard Details and Specifications BIDDING PROCESS Bidding Documents Advertising and Bid Openings Construction Contracts CONSTRUCTION Preconstruction Conferences Shop Drawings Safety, Inspection, and Testing Construction Staking Close-Out SUPPLEMENTAL Technical Reference







Probability, Statistics, and Decision for Civil Engineers


Book Description

"This text covers the development of decision theory and related applications of probability. Extensive examples and illustrations cultivate students' appreciation for applications, including strength of materials, soil mechanics, construction planning, and water-resource design. Emphasis on fundamentals makes the material accessible to students trained in classical statistics and provides a brief introduction to probability. 1970 edition"--




Civil Engineering Body of Knowledge


Book Description

This report outlines 21 foundational, technical, and professional practice learning outcomes for individuals entering the professional practice of civil engineering.




Probabilistic Machine Learning for Civil Engineers


Book Description

An introduction to key concepts and techniques in probabilistic machine learning for civil engineering students and professionals; with many step-by-step examples, illustrations, and exercises. This book introduces probabilistic machine learning concepts to civil engineering students and professionals, presenting key approaches and techniques in a way that is accessible to readers without a specialized background in statistics or computer science. It presents different methods clearly and directly, through step-by-step examples, illustrations, and exercises. Having mastered the material, readers will be able to understand the more advanced machine learning literature from which this book draws. The book presents key approaches in the three subfields of probabilistic machine learning: supervised learning, unsupervised learning, and reinforcement learning. It first covers the background knowledge required to understand machine learning, including linear algebra and probability theory. It goes on to present Bayesian estimation, which is behind the formulation of both supervised and unsupervised learning methods, and Markov chain Monte Carlo methods, which enable Bayesian estimation in certain complex cases. The book then covers approaches associated with supervised learning, including regression methods and classification methods, and notions associated with unsupervised learning, including clustering, dimensionality reduction, Bayesian networks, state-space models, and model calibration. Finally, the book introduces fundamental concepts of rational decisions in uncertain contexts and rational decision-making in uncertain and sequential contexts. Building on this, the book describes the basics of reinforcement learning, whereby a virtual agent learns how to make optimal decisions through trial and error while interacting with its environment.