Smart Technologies for Energy, Environment and Sustainable Development, Vol 1 : Select Proceedings of ICSTEESD 2020

個数:1
紙書籍版価格
¥62,287
  • 電子書籍
  • ポイントキャンペーン

Smart Technologies for Energy, Environment and Sustainable Development, Vol 1 : Select Proceedings of ICSTEESD 2020

  • 著者名:Kolhe, Mohan Lal (EDT)/Jaju, S. B. (EDT)/Diagavane, P. M. (EDT)
  • 価格 ¥48,373 (本体¥43,976)
  • Springer(2022/02/25発売)
  • 春うらら!Kinoppy 電子書籍・電子洋書 全点ポイント30倍キャンペーン(~3/15)
  • ポイント 13,170pt (実際に付与されるポイントはご注文内容確認画面でご確認下さい)
  • 言語:ENG
  • ISBN:9789811668746
  • eISBN:9789811668753

ファイル: /

Description

This book contains select proceedings of the International Conference on Smart Technologies for Energy, Environment, and Sustainable Development (ICSTEESD 2020). The book is broadly divided into the themes of energy, environment, and sustainable development; and discusses the significance and solicitations of intelligent technologies in the domain of energy and environmental systems engineering. Topics covered in this book include sustainable energy systems including renewable technologies, energy efficiency, techno-economics of energy system and policies, integrated energy system planning, environmental management, energy efficient buildings and communities, sustainable transportation, smart manufacturing processes, etc. The book will be a valuable reference for young researchers, professionals, and policy makers working in the areas of energy, environment and sustainable development.

Table of Contents


Seismic Analysis of 3D Printed Structures.- Efficiency of linear univariate programming method in estimating the parameters reflecting the behavior of R.C.C beam along the span.- Effect of Creep and Shrinkage on Construction Sequence Analysis of High Rise Building.- Experimental Investigation of Concrete used in Spent Fuel Pools of Nuclear Power Plants Subjected to Boiling Water Exposure.- Effect of Aspect Ratio on Fatigue Behaviour of Steel Shear Wall.- Extreme rainfall analysis using Extreme Value (EV-I) distribution based on L-Moment approach.- Application of Machine Learning for Accuracy Improvement of Projected Precipitation of Climate Change Data with Observed Data.

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