PurposeDigital twin (DT) technology holds significant promise for transforming facility management (FM) by enabling real-time asset monitoring, early fault detection and data-driven decision-making for predictive maintenance (PdM). While PdM uses historical and real-time data to anticipate equipment failures, DT offers dynamic virtual replicas of physical assets for continuous performance optimization. This study aims to examine the integration of DT and building information modeling to support PdM in building facilities, aiming to address inefficiencies inherent in traditional reactive maintenance approaches. The research explores key questions around the main challenges and benefits of deploying DT for PdM, the effectiveness of DT in enhancing FM processes and sustainability and the critical components needed for a practical DT implementation strategy.Design/methodology/approachA qualitative approach was adopted, combining a systematic literature review with semistructured interviews with industry professionals. Thematic analysis was used to synthesize the results.FindingsThe results demonstrate DT's potential to improve maintenance through enhanced decision-making, greater operational efficiency and stronger predictive capabilities. However, significant challenges were identified, including high implementation costs, data integration challenges, lack of standardization, organizational resistance and skill gaps. The findings highlight the significance of aligning people, processes and technology to enhance the impact of DT.Practical implicationsThe research offers actionable insights for facility managers and policymakers aiming to implement DT-driven PdM, ultimately supporting the development of smarter and more sustainable built environments.Originality/valueThis study proposes a structured strategy comprising seven key elements to facilitate DT adoption in FM: (1) needs assessment, (2) incremental deployment, (3) structured data management, (4) seamless system integration, (5) organizational anchoring, (6) standardization protocols and (7) a long-term vision.

Purpose

The implementation of the European Union's Level(s) framework at Level 3 – focused on in-use performance – is hindered by fragmented tools and a lack of integrated digital workflows. To bridge this gap, this study proposes and develops a prototype Collaborative Digital Platform (CDP) designed specifically to address Level 3 requirements by integrating Building Information Modeling (BIM), Internet of Things (IoT), Digital Twin and Artificial Intelligence (AI) components.

Design/methodology/approach

Employing a Design Science Research methodology, the study conceptualizes, architects and demonstrates a functional CDP prototype. The platform is built using a modular, openBIM-based architecture to enable the merging of static design data with dynamic operational data streams.

Findings

The developed prototype successfully demonstrates the technical feasibility of integrating BIM, simulated IoT sensor data and a rule-based AI interface within a unified dashboard. It provides a proof-of-concept workflow for visualizing key Level 3 indicators – specifically operational energy use, carbon emissions and indoor environmental quality – in near real time. The findings highlight the platform's potential to support data-informed monitoring and enhance transparency for sustainability reporting.

Practical implications

The study provides a replicable architectural template and a functional prototype, demonstrated via a tutorial residential building case study. This foundational work offers a concrete starting point for developers and researchers aiming to build digital tools for Level(s) compliance and establishes a basis for future pilot deployments, empirical validation and the development of automated compliance-checking features.

Originality/value

The primary contribution of this research is a novel, problem-specific digital architecture designed explicitly to operationalize the reporting requirements of the EU Level(s) framework at Level 3. While the constituent technologies are established, their purposeful synthesis into a cohesive platform blueprint addresses a distinct gap in translating policy-led sustainability assessment into practical, integrated digital workflows.

This book analyzes the transformation process of traditional cities into Sustainable Urban Smart Cities. The UN’s Sustainable Development Goals is used as the book’s over-arching framework. The overall aim of this book is to discuss the implementation of spatial planning, national and regional physical planning, good governance, water and waste management, energy efficiency, and digital innovation in the architecture, engineering, and construction (AEC) sector. The manuscript focuses on Construction 5.0 for smart, sustainable, and resilient built environments, and Digital Twin-enabled cognitive buildings and infrastructure lifecycle management. 

Smart buildings equipped with diverse control systems serve the objectives of gathering data, optimizing energy efficiency (EE), and detecting and diagnosing faults, particularly in the domain of indoor environmental quality (IEQ). Digital twins (DTs) offering an environmentally sustainable solution for managing facilities and incorporated with artificial intelligence (AI) create opportunities for maintaining IEQ and optimizing EE. The purpose of this study is to assess the impact of AI-driven DTs on enhancing IEQ and EE in smart building systems (SBS). A scoping review was performed to establish the theoretical background about DTs, AI, IEQ, and SBS, semi-structured interviews were conducted with the specialists in the industry to obtain qualitative data, and quantitative data were gathered via a computerized self-administered questionnaire (CSAQ) survey, focusing on how DTs can improve IEQ and EE in SBS. The results indicate that the AI-driven DT enhances occupants’ comfort and energy-efficiency performance and enables decision-making on automatic fault detection and maintenance conditioning to improve buildings’ serviceability and IEQ in real time, in response to the key industrial needs in building energy management systems (BEMS) and interrogative and predictive analytics for maintenance. The integration of AI with DT presents a transformative approach to improving IEQ and EE in SBS. The practical implications of this advancement span across design, construction, AI, and policy domains, offering significant opportunities and challenges that need to be carefully considered.

Purpose: The purpose of this research is to develop a framework of an ontology-based Asset Information Model (AIM) for a Digital Twin (DT) platform and enhance predictive maintenance practices in building facilities that could enable proactive and data-driven decision-making during the Operation and Maintenance (O&M) process. Design/methodology/approach: A scoping literature review was accomplished to establish the theoretical foundation for the current investigation. A study on developing an ontology-based AIM for predictive maintenance in building facilities was conducted. Semi-structured interviews were conducted with industry professionals to gather qualitative data for ontology-based AIM framework validation and insights. Findings: The research findings indicate that while the development of ontology faced challenges in defining missing entities and relations in the context of predictive maintenance, insights gained from the interviews enabled the establishment of a comprehensive framework for ontology-based AIM adoption in the Facility Management (FM) sector. Practical implications: The proposed ontology-based AIM has the potential to enable proactive and data-driven decision-making during the process, optimizing predictive maintenance practices and ultimately enhancing energy efficiency and sustainability in the building industry. Originality/value: The research contributes to a practical guide for ontology development processes and presents a framework of an Ontology-based AIM for a Digital Twin platform.

In construction projects, frequent changes to time planning result from unforeseen events, impacting both timing and scope of information exchanges (IE). Currently, IE planning and production planning are performed independently with limited integration between the two, leading to inefficiencies when changes are needed. This study proposes a unified approach facilitating Level of Information Need (LOIN) to systematically identify and link to required IEs for the production phase. The mapping was co-developed with and evaluated by industry professionals to ensure practical relevance. Integrating LOIN-driven IE mapping into production planning enhances responsiveness to schedule changes by providing structured guidelines for information delivery.

This book combines Construction 6.0 with AEC principles for designing sustainable, health-focused Martian habitats. It unveils innovative architectural designs ideal for Mars, utilizing 3D printing, autonomous robotics, and regolith, alongside renewable energy and life support systems. With an emphasis on well-being, it integrates biophilic design and digital technologies to enhance operational efficiency. Exploring various habitat models, it advocates a multidisciplinary approach to extraterrestrial colonization that balances technological advancement with environmental and ethical stewardship, aiming to make human life on Mars a healthy and sustainable reality.

This book offers a comprehensive exploration of research methodology, providing invaluable insights for students and researchers in the AEC industry and engineering fields. Through this book, readers learn the theoretical and practical aspects of research design and academic writing, making it an indispensable resource for anyone engaged in scholarly investigation.This book covers a wide range of topics essential to robust research practices. Readers explore identifying research gaps, formulating problem statements, crafting precise research questions, objectives, and aims. This book delves into the philosophical foundations of different research paradigms—ontological and epistemological factors that impact methodological decisions. It also provides an in-depth examination of qualitative methods such as ethnography, grounded theory, narrative analysis; quantitative methods including experimental design, survey techniques; as well as mixed-methods approaches that combine both to yield comprehensive results.In addition to theoretical content, this book offers practical tools like templates for research proposals and checklists for data collection. These resources are designed to aid researchers in efficiently applying their knowledge in real-world settings. The authors emphasize clarity, coherence, and scholarly rigor in academic writing while providing practical techniques to overcome common obstacles faced by researchers.This book is essential for researchers, scholars, students at all levels who seek to enhance their understanding of research methodology. It provides clear guidance on structuring and organizing research articles, performing literature reviews, presenting findings with scholarly rigor. Whether you are a novice researcher or an experienced academician looking to refine your skills or gain new perspectives on methodological practices—this book is tailored to meet your needs.Researchers in fields such as architecture, engineering construction (AEC), environmental design find this work particularly beneficial. Its comprehensive scope guarantees its authenticity usefulness rendering it indispensable literary essential study academic writing methodological investigation.

Smart Cities – Designing the Future of Urban Living presents a scholarly synthesis of modern smart urbanism, promoting a coherent, standards-compliant lexicon for digital public infrastructure, AI governance, and human-centered design. The volume connects theory and practice by turning normative frameworks such as equity, resilience, sustainability, and democratic legitimacy into practical advice on interoperability, outcome-based metrics, algorithmic transparency, rights-preserving security, energy systems transition, and circular economy practices. Readers will find helpful tools, such as governance patterns and procurement agreements, indicators and assessment rubrics, and finance concepts that turn pilots into long-lasting public value. Some of its unique strengths are its ability to compare different urban settings, its focus on multi-level governance and institutional capacity, and its insistence that technological "smartness" be shown through measurable improvements in well-being, environmental performance, and public trust. Written for city leaders and planners, engineers and educators, researchers and policy professionals, the book shows how to turn ideas into actions that can be held accountable. It connects data architectures to service quality, participatory co-production to legitimacy, and open standards to learning and scalability. This book provides readers with a solid foundation for planning the future of city living through a short, authoritative, methodologically sound, policy-relevant, and implementation-ready roadmap.

This chapter presents the idea of “Smart Cities That Think” as a new way of looking at traditional innovative city models. It redefines urban intelligence as a mix of cognitive infrastructures, AI-driven governance, and citizen participation. The scope encompasses emerging technologies, like digital twins, context-aware systems, and predictive analytics, while also addressing participatory platforms and sustainability frameworks. The methodological approach is mainly conceptual and integrative, merging critical literature research with illustrative case studies from European and Asian cities. Three main discoveries come to light. First, digital twins and other predictive and adaptive technologies show measurable improvements in efficiency, such as less traffic and energy use in experimental programs. Second, citizen-centered models like participatory budgeting and open data projects make democracy more legitimate and build trust in society. Third, initiatives that focus on sustainability demonstrate that AI-driven systems can enhance resilience when used ethically. At the same time, ongoing problems like data privacy, algorithmic prejudice, and unequal governance show how dangerous it may be to depend too much on technology. The chapter concludes that thinking cities must balance technological innovation with ethical governance and citizen cooperation, creating a holistic pathway for resilient and inclusive urban futures.