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This article explores the possible ramifications of incorporating ideas from AEC Industry 6.0 into the design and construction of intelligent, environmentally friendly, and long-lasting structures. This statement highlights the need to shift away from the current methods seen in the AEC Industry 5.0 to effectively respond to the increasing requirement for creative and environmentally sustainable infrastructures. Modern building techniques have been made more efficient and long-lasting because of AEC Industry 6.0’s cutting-edge equipment, cutting-edge digitalization, and ecologically concerned methods. The academic community has thoroughly dissected the many benefits of AEC Industry 5.0. Examples are increased stakeholder involvement, automation, robotics for optimization, decision structures based on data, and careful resource management. However, the difficulties of implementing AEC Industry 6.0 principles are laid bare in this research. It calls for skilled experts who are current on the latest technologies, coordinate the technical expertise of many stakeholders, orchestrate interoperable standards, and strengthen cybersecurity procedures. This study evaluates how well the principles of Industry 6.0 can create smart, long-lasting, and ecologically sound structures. The goal is to specify how these ideas may revolutionize the building industry. In addition, this research provides an in-depth analysis of how the AEC industry might best adopt AEC Industry 6.0, underscoring the sector-wide significance of this paradigm change. This study thoroughly analyzes AEC Industry 6.0 about big data analytics, the IoT, and collaborative robotics. To better understand the potential and potential pitfalls of incorporating AEC Industry 6.0 principles into the construction of buildings, this study examines the interaction between organizational dynamics, human actors, and robotic systems.

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.

The process of combining various parts to create a structure is called building. The most effective and significant component of any construction is masonry. The Colosseum, buildings from ancient Greece and Rome, Central American buildings, and Mycenaean structures all used this material as one of their primary building elements. The oldest form is dry masonry of irregularly shaped stones. The ecological qualities of masonry, as a restorative material with a low impact on the environment, as well as the environmental control capacity of the massive wall, bring masonry back to attention as a suitable material for sustainable building in the context of current concerns for sustainable architecture. This article takes the form of a review of the journey of masonry as the primary construction material—from prehistoric structures to modern-day edifices. This article will go through the fundamentals of masonry construction to support its usage in structures throughout history and in many architectural styles, as a crucial representation of human construction in architectural history. This article aims to create a historical review, presenting masonry as an essential building material and assessing its role in the history of building materials.

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.

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.

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.

Cement production is a significant global source of CO2 emissions, leading to a demand for sustainable concrete alternatives. This study investigates the use of various additives to partially replace cement and assesses their effects on compressive strength and fire resistance, particularly spalling. Seven concrete mixes were tested for their initial and post-fire compressive strength, mass loss, and cracking. The cement-only reference mix (R1) achieved the highest initial strength (53.3 MPa) but experienced severe explosive spalling. In contrast, the mix with slag and polypropylene (PP) fibers (R4) offered the best balance, maintaining substantial strength after fire while completely preventing spalling. Biochar additions consistently lowered strength and increased spalling risk, whereas hydrochar notably enhanced spalling resistance, especially at higher replacement levels. The results demonstrate that sustainable additives, such as slag with PP fibers or high-dose hydrochar, can effectively improve fire safety and reduce cement use, though there is an initial trade-off in mechanical performance. Ultimately, choosing the optimal mix depends on whether environmental benefits, fire resistance, or structural strength is the highest priority.

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.

Using artificial neural networks (ANN), numerous models were developed for predicting the ultimate shear strength of reinforced concrete deep beams. Many experimental result databases from earlier research were carefully gathered for this study. Two hundred fifty-three findings from experiments were included in this database. The ultimate shear strength was the output parameter, while ten factors were determined as input parameters for the ANN model based on the completed literature research. The required model was constructed using a back propagation neural network. The model of the neural networks was determined using the trial-and-error method. It was discovered that, inside the range of the input boundaries considered, the ANN model could accurately estimate the ultimate shear strength of deep beams. The measured shear strength and the shear strength predicted by the ANN model have a high correlation coefficient of 0.97, indicating a strong relationship between the predicted and actual values. The results show that, given the range of input parameters, ANN offers an excellent agreement of interest as a practical technique for estimating the ultimate shear strength. A parametric investigation was performed using the trained neural network model to assess how the input parameters affected the shear strength capacity of deep beams.