In this study, we critically examine the potential of recycled construction materials, focusing on how these materials can significantly reduce greenhouse gas (GHG) emissions and energy usage in the construction sector. By adopting an integrated approach that combines Life Cycle Assessment (LCA) and Material Flow Analysis (MFA) within the circular economy framework, we thoroughly examine the lifecycle environmental performance of these materials. Our findings reveal a promising future where incorporating recycled materials in construction can significantly lower GHG emissions and conserve energy. This underscores their crucial role in advancing sustainable construction practices. Moreover, our study emphasizes the need for robust regulatory frameworks and technological innovations to enhance the adoption of environmentally responsible practices. We encourage policymakers, industry stakeholders, and the academic community to collaborate and promote the adoption of a circular economy strategy in the building sector. Our research contributes to the ongoing discussion on sustainable construction, offering evidence-based insights that can inform future policies and initiatives to improve environmental stewardship in the construction industry. This study aligns with the European Union’s objectives of achieving climate-neutral cities by 2030 and the United Nations’ Sustainable Development Goals outlined for completion by 2030. Overall, this paper contributes to the ongoing dialogue on sustainable construction, providing a fact-driven basis for future policy and initiatives to enhance environmental stewardship in the industry. 

This study takes a unique approach by investigating the integration of Brain–Computer Interfaces (BCIs) and Building Information Modeling (BIM) within residential architecture. It explores their combined potential to foster neuro-responsive, sustainable environments within the framework of Construction 5.0. The methodological approach involves real-time BCI data and subjective evaluations of occupants’ experiences to elucidate cognitive and emotional states. These data inform BIM-driven alterations that facilitate adaptable, customized, and sustainability-oriented architectural solutions. The results highlight the ability of BCI–BIM integration to create dynamic, occupant-responsive environments that enhance well-being, promote energy efficiency, and minimize environmental impact. The primary contribution of this work is the demonstration of the viability of neuro-responsive architecture, wherein cognitive input from Brain–Computer Interfaces enables real-time modifications to architectural designs. This technique enhances built environments’ flexibility and user-centered quality by integrating occupant preferences and mental states into the design process. Furthermore, integrating BCI and BIM technologies has significant implications for advancing sustainability and facilitating the design of energy-efficient and ecologically responsible residential areas. The study offers practical insights for architects, engineers, and construction professionals, providing a method for implementing BCI–BIM systems to enhance user experience and promote sustainable design practices. The research examines ethical issues concerning privacy, data security, and informed permission, ensuring these technologies adhere to moral and legal requirements. The study underscores the transformational potential of BCI–BIM integration while acknowledging challenges related to data interoperability, integrity, and scalability. As a result, ongoing innovation and rigorous ethical supervision are crucial for effectively implementing these technologies. The findings provide practical insights for architects, engineers, and industry professionals, offering a roadmap for developing intelligent and ethically sound design practices.

This study conducts an in-depth analysis of the interplay between sustainable industrial growth and integrated industrial urban environments, proposing a novel paradigm for urban production. The aim of this study is to combine sustainable industrial growth with its integration into urban environments, to establish a new and novel way to seamlessly integrate industrial processes within urban surroundings. This research utilizes a thorough approach, incorporating several disciplines, to examine Hamadan industrial city. It includes an extensive survey of existing literature, a comparative analysis based on empirical evidence, and a detailed evaluation of a specific example. This technique aims to address a significant research gap by providing a comprehensive framework that promotes sustainable industrial practices in urban environments. The scholarly contribution of this work is to manifest in its formulation of a pragmatic framework designed to provide urban planners and policymakers with strategies to harmonize industrial growth with urban sustainability imperatives. This article tackles the considerable challenges posed by escalating urbanization and industrialization. To conceive a framework for urban planning and industrial operations that emphasize environmental stewardship, resource efficiency, and social welfare is the primary purpose of this project. The study shows how industrial cities may revitalize economies, innovate industries, and solve urban problems including housing shortages and congestion. The importance of creative, collaborative, and policy-driven initiatives to build sustainable and resilient industrial-urban ecosystems in global industrial sustainability efforts is highlighted. The findings show that synergistic urban-industrial integration is needed for economic growth, environmental protection, and social welfare.

Purpose

The concept of Construction 5.0 has emerged as the next frontier in construction practices and is characterized by the integration of advanced technologies with human-centered approaches, sustainable practices and resilience considerations to build smart and future-ready buildings. However, there is currently a gap in research that provides a comprehensive perspective on the opportunities and challenges of facilitating Construction 5.0. This study aims to explore the opportunities and challenges in facilitating Construction 5.0 and its potential to implement smart, sustainable and resilient buildings.

Design/methodology/approach

The structural equation modeling (SEM) method was used to evaluate the research model and investigate the opportunities and challenges related to Construction 5.0 in its implementation for smart, sustainable and resilient buildings.

Findings

The results show that adopting human-centric technology, sustaining resilience and maintaining sustainability in the architecture, engineering and construction (AEC) industry seizes the opportunities to overcome the challenges for facilitating Construction 5.0 in the implementation of smart, sustainable and resilient buildings.

Practical implications

The AEC industry facilitating Construction 5.0 has the potential to redefine the future of construction, creating a built environment that is not only intelligent, sustainable and resilient but also deeply connected with the well-being and values of the communities it serves.

Originality/value

The research illuminates the path forward for a holistic understanding of Construction 5.0, envisioning a future where smart, sustainable and resilient buildings stand as testaments to the harmonious collaboration between humans and technology.

Artificial Intelligence (AI) simulation models and digital twins (DT) are used in designingand treating the activities, layout, and functions for the new generation of buildings to enhanceuser experience and optimize building performance. These models use data about a building’s use,configuration, functions, and environment to simulate different design options and predict theireffects on house function efficiency, comfort, and safety. On the one hand, AI algorithms are usedto analyze this data and find patterns and trends that can guide the design process. On the otherhand, DTs are digital recreations of actual structures that can replicate building performance in realtime. These models would evaluate alternative design options, the performance of the building, andways to improve user comfort and building efficiency. This study examined the important role ofintelligent building design aspects, such as activities using multi-layout and the creation of particularfunctions based on AI simulation models, in developing DT-based smart building systems. Theempirical data came from a study of architecture and engineering firms throughout the globe usinga CSAQ (computer-administered, self-completed survey). For this purpose, the study employedstructural equation modeling (SEM) to examine the hypotheses and build the relationship model. Theresearch verifies the relevance of AI-based simulation models supporting the creation of intelligentbuilding design features (activities, layout, functionalities), enabling the construction of DT-basedsmart building systems. Furthermore, this study highlights the need for further exploration ofAI-based simulation models’ role and integration with DT in smart building design.

The pressing concern of climate change and the imperative to mitigate CO2 emissions have significantly influenced the selection of outdoor plant species. Consequently, evaluating CO2's environmental effects on plants has become integral to the decision-making process. Notably, reducing greenhouse gas (GHG) emissions from buildings is significant in tackling the consequences of climate change and addressing energy deficiencies. This article presents a novel approach by introducing plant panels as an integral component in future building designs, epitomizing the next generation of sustainable structures and offering a new and sustainable building solution. The integration of environmentally friendly building materials enhances buildings' indoor environments. Consequently, it becomes crucial to analyze manufacturing processes in order to reduce energy consumption, minimize waste generation, and incorporate green technologies. In this context, experimentation was conducted on six distinct plant species, revealing that the energy-saving potential of different plant types on buildings varies significantly. This finding contributes to the economy's improvement and fosters enhanced health-related and environmental responsibility. The proposed plant panels harmonize various building components and embody a strategic approach to promote health and well-being through bio-innovation. Furthermore, this innovative solution seeks to provide a sustainable alternative by addressing the challenges of unsustainable practices, outdated standards, limited implementation of new technologies, and excessive administrative barriers in the construction industry. The obtained outcomes will provide stakeholders within the building sector with pertinent data concerning performance and durability. Furthermore, these results will enable producers to acquire essential information, facilitating product improvement.

Since the behavior of a complex dynamic system for a large oil field in Iraq is significantly influenced by many nonlinearities, its dependent parameters exhibit non-stationary with a very high delay time. Developing white-box modelling approaches for such dynamic oil well production cannot handle these large data sets with all dependent dimensions and their non-linear effects. Therefore, this study adopts the hybrid model that combines white-box and black-box to address such problems because the model outputs require various variable types to achieve optimal fitness to measured values. The hybrid model structure needs to evolve with changes in the physical parameters (white-box part) and Neural Networks' Weights (black-box part). The model structure of the proposed hybrid network relied on converting fuzzy rules in a Takagi–Sugeno–Kang Fuzzy System (TSK-FS) into a multilayer perceptron network (MLP). The hybrid parameters are formulated concerning six-dimensional dependent variables to describe them in matrix form or layer and by which can quantify total model outputs. After mapping categorical variables to tuples of MLP, the Gauss-Newton regression (GNR) provides an optimal update of the hybrid parameters to get the best fitting of the model outputs with the target of the dataset. The clustering technique and GNR promote predictive performance due to reducing uncertainties in the hybrid parameters. Due to time being the most effective of the independent variables for predicting oil production, datasets are classified into different clusters based on time. The actual field dataset for training and validation is collected from Zubair Oil Field (9 oil wells), which is implemented to build the proposed model. The results of the hybrid model indicate that the development of the proposed structure has achieved the high capability to represent such big data which is the most imperative feature of the proposed model. Furthermore, obtained results show its accuracy far outpacing competitors and achieving a significant improvement in predictive performance.

Chillers are responsible for almost half of the total energy demand in buildings. Hence, the obligation of control systems of multi-chiller due to changes indoor environments is one of the most significant parts of a smart building. Such a controller is described as a nonlinear and multi-objective algorithm, and its fabrication is crucial to achieving the optimal balance between indoor thermal comfort and running a minimum number of chillers. This work proposes deep clustering of cooperative multi-agent reinforcement learning (DCCMARL) as well-suited to such system control, which supports centralized control by learning of agents. In MARL, since the learning of agents is based on discrete sets of actions and stats, this drawback significantly affects the model of agents for representing their actions with efficient performance. This drawback becomes considerably worse when increasing the number of agents, due to the increased complexity of solving MARL, which makes modeling policy very challenging. Therefore, the DCCMARL of multi-objective reinforcement learning is leveraging powerful frameworks of a hybrid clustering algorithm to deal with complexity and uncertainty, which is a critical factor that influences to the achievement of high levels of a performance action. The results showed that the ability of agents to manipulate the behavior of the smart building could improve indoor thermal conditions, as well as save energy up to 44.5% compared to conventional methods. It seems reasonable to conclude that agents' performance is influenced by what type of model structure.

The normal development of “smart buildings,” which calls for integrating sensors, rich data, and artificial intelligence (AI) simulation models, promises to usher in a new era of architectural concepts. AI simulation models can improve home functions and users’ comfort and significantly cut energy consumption through better control, increased reliability, and automation. This article highlights the potential of using artificial intelligence (AI) models to improve the design and functionality of smart houses, especially in implementing living spaces. This case study provides examples of how artificial intelligence can be embedded in smart homes to improve user experience and optimize energy efficiency. Next, the article will explore and thoroughly analyze the thorough analysis of current research on the use of artificial intelligence (AI) technology in smart homes using a variety of innovative ideas, including smart interior design and a Smart Building System Framework based on digital twins (DT). Finally, the article explores the advantages of using AI models in smart homes, emphasizing living spaces. Through the case study, the theme seeks to provide ideas on how AI can be effectively embedded in smart homes to improve functionality, convenience, and energy efficiency. The overarching goal is to harness the potential of artificial intelligence by transforming how we live in our homes and improving our quality of life. The article concludes by discussing the unresolved issues and potential future research areas on the usage of AI in smart houses. Incorporating AI technology into smart homes benefits homeowners, providing excellent safety and convenience and increased energy efficiency.

Hybrid learning is a complex combination of face-to-face and online learning. This model combines the use of multimedia materials with traditional classroom work. Virtual hybrid learning is employed alongside face-to-face methods. That aims to investigate using Artificial Intelligence (AI) to increase student engagement in hybrid learning settings. Educators are confronted with contemporary issues in maintaining their students' interest and motivation as the popularity of online and hybrid education continues to grow, where many educational institutions are adopting this model due to its flexibility, student-teacher engagement, and peer-to-peer interaction. AI will help students communicate, collaborate, and receive real-time feedback, all of which are challenges in education. This article examines the advantages and disadvantages of hybrid education and the optimal approaches for incorporating Artificial Intelligence (AI) in educational settings. The research findings suggest that using AI can revolutionize hybrid education, as it enhances both student and instructor autonomy while fostering a more engaging and interactive learning environment.