Sustainable Infrastructure & Digital Twins | A Lifecycle Approach to Net-Zero Construction and Operations

Vishnuvardhan S (u. a.)

ISBN 10: 6209407706 / ISBN 13: 9786209407703

Published by LAP LAMBERT Academic Publishing, 2026

About this Item

Sustainable Infrastructure & Digital Twins | A Lifecycle Approach to Net-Zero Construction and Operations | Vishnuvardhan S (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786209407703 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.

Seller Inventory # 134526646

Bibliographic Details

• Title: Sustainable Infrastructure & Digital Twins | A Lifecycle Approach to Net-Zero Construction and Operations
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2026
• Language: English
• ISBN 10: 6209407706
• ISBN 13: 9786209407703
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 8D millimeters 220 millimeters width by 150 millimeters height by 8 millimeters depth
• Weight: 197 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

As digital twins, smart infrastructure, and net-zero frameworks mature, they open vast and largely unexplored research frontiers. While this book has established conceptual foundations, technological architectures, and governance frameworks, future research must deepen, validate, and extend these ideas across disciplines, scales, and socio-economic contexts. The next generation of research must move beyond proof-of-concept demonstrations toward system-level intelligence, real-world validation, and long-term societal impact assessment.One of the most critical future research directions lies in the evolution of digital twins from descriptive to cognitive systems. Current digital twins primarily focus on monitoring and simulation, relying on predefined models and rule-based analytics. Future research should explore self-learning digital twins that continuously adapt their internal models using machine learning, reinforcement learning, and federated intelligence. Such cognitive twins would not only predict outcomes but autonomously recommend or execute optimal interventions across planning, construction, and operations.