Reviewing and Integrating AEC Practices into Industry 6.0: Strategies for Smart and Sustainable Future-Built Environments

Almusaed, Amjad; Yitmen, Ibrahim; Almssad, Asaad

doi:10.3390/su151813464

Cited by 23 publications

(6 citation statements)

References 116 publications

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“…To overcome interoperability issues, construction firms need to adopt open-source platforms, standardized interfaces, and middleware solutions that facilitate data exchange and communication among different systems. Additionally, collaborative approaches such as Building Information Modeling (BIM) and Integrated Project Delivery (IPD) can help streamline workflows and improve coordination among project stakeholders, leading to more efficient project delivery and better outcomes [117][118][119].…”

Section: Initial Implementation Costs Data Security and Privacy Conce...mentioning

confidence: 99%

“…Despite these significant benefits, the literature review highlighted several challenges in the application of AI in sustainable building lifecycle. These challenges include initial implementation costs [96][97][98], data security and privacy concerns [62,66,103,104], lack of standardization [105,106,111], skills gap [83,[112][113][114][115][116], interoperability issues [66,[117][118][119], ethical considerations [66,103,120,121], and regulatory compliance [116,[120][121][122][123]. The construction industry's relatively slow adoption of AI can be attributed to factors such as the complexity of construction projects, the traditional nature of the industry, and a lack of awareness or understanding of AI's potential benefits [30,66].…”

Section: Key Findingsmentioning

confidence: 99%

“…Furthermore, the review identified the potential advantages and challenges of integrating AI towards creating a sustainable building lifecycle [20,23,32,47,58,[62][63][64]66,69,70,72,73,83,86,[95][96][97][98][103][104][105][106][111][112][113][114][115][116][117][118][119][120][121][123][124][125]. While highlighting the benefits, such as energy efficiency optimization, predictive maintenance, and waste reduction, the review also addressed challenges like initial implementation costs, data security and privacy concerns, lack of standardization, skills gap, interoperability issues, ethical considerations, and regulatory compliance.…”

Section: Research Contributionsmentioning

confidence: 99%

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Application of Artificial Intelligence (AI) in Sustainable Building Lifecycle; A Systematic Literature Review

Adewale,

Ene,

Ogunbayo

et al. 2024

Preprint

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With buildings accounting for a significant portion of global energy consumption and greenhouse gas emissions, the application of artificial intelligence (AI) holds promise for enhancing sustainability in the building lifecycle. This systematic literature review addresses the current understanding of AI's potential to optimize energy efficiency and minimize environmental impact in building design, construction, and operation. A comprehensive literature review and synthesis were conducted to identify AI technologies applicable to sustainable building practices, examine their influence, and analyze the challenges of implementation. The review was guided by a meticulous search strategy utilizing keywords related to AI application in sustainable building design, construction, and operation. The findings reveal AI's capabilities in optimizing energy efficiency through intelligent control systems, enabling predictive maintenance, and aiding design simulation. Advanced machine learning algorithms facilitate data-driven analysis and prediction, while digital twins provide real-time insights for informed decision-making. Furthermore, the review identifies barriers to AI adoption, including cost concerns, data security risks, and challenges in implementation. AI presents a transformative opportunity to enhance sustainability in the built environment, offering innovative solutions for energy optimization and environmentally conscious practices. However, addressing technical and practical challenges will be crucial for the successful integration of AI in sustainable building practices.

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Section: Initial Implementation Costs Data Security and Privacy Conce...mentioning

confidence: 99%

Section: Key Findingsmentioning

confidence: 99%

Section: Research Contributionsmentioning

confidence: 99%

See 1 more Smart Citation

Application of Artificial Intelligence (AI) in Sustainable Building Lifecycle; A Systematic Literature Review

Adewale,

Ene,

Ogunbayo

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The flexible job-shop scheduling problem (FJSP) is essentially about efficiently managing a sequence of jobs using a specific number of machines [1]. Each job comprises multiple operations, and each operation can be executed on several machines [2].…”

Section: Introductionmentioning

confidence: 99%

The Green Flexible Job-Shop Scheduling Problem Considering Cost, Carbon Emissions, and Customer Satisfaction under Time-of-Use Electricity Pricing

Jia,

Yang,

et al. 2024

Sustainability

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Exploration of the green flexible job-shop scheduling problem is essential for enterprises aiming for sustainable practices, including energy conservation, emissions reduction, and enhanced economic and social benefits. While existing research has predominantly focused on carbon emissions or energy consumption as green scheduling objectives, this paper addresses the broader scope by incorporating the impact of variable energy prices on energy cost. Through the introduction of an energy cost model based on time-of-use electricity pricing, the study formulates a multi-objective optimization model for green flexible job-shop scheduling. The objectives include minimizing cost, reducing carbon emissions, and maximizing customer satisfaction. To prevent premature convergence and maintain population diversity, an enhanced genetic algorithm is employed for solving. The validation of the algorithm’s effectiveness is demonstrated through specific examples, providing decision results for optimal scheduling under various weight combinations. The research outcomes hold substantial practical value as they can significantly reduce energy expenses, lower carbon emissions, and elevate customer satisfaction while safeguarding production efficiency. This contributes to enhancing the market competitiveness and green brand image of businesses.

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“…Many technologies were developed to create a vast web of ramifications, such as big data, machine learning, the Internet of Things, and computer vision, among countless others. As current reality indicates, the near future will be much more revolutionary, as Industry 5.0 (Wang et al, 2024;Gladysz et al, 2023), Industry 6.0 (Almusaed et al, 2023) structures are already being developed; Chourasia et al, 2023) and even Industry 7.0 (Iordache, 2022;Chernysh et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

What is Industry 4.0: a Review of the Literature Using the Conceptual Bibliographic Method

De Oliveira,

Craveiro,

Bezerra

et al. 2023

J. Professional Business Review

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Purpose: Scientific production has tried to understand the various evolutionary intricacies of Industry 4.0, the so-called fourth industrial revolution. As discoveries increase and deepen, there must also be an effort to understand the frontiers of scientific knowledge, its most noticeable and studied aspects, and those attributes that have only now been perceived. It is, therefore, necessary to periodically take stock of what science knows about the phenomenon to understand where it is headed and which paths have already been taken. Therefore, the need to take inventory of the current state of knowledge about Industry 4.0 was the theoretical justification for this study. Theoretical framework: The theoretical architecture of this study consisted of the transformations that the industrial revolutionary stages caused in production systems, firstly, and then for the entire society. It involves aspects from various areas of scientific knowledge to understand the different perceptions contained in the scientific literature on the phenomenon of Industry 4.0. Design/Methodology/Approach: The conceptual bibliographic method was used to generate answers to three guiding questions: What are the main approaches to Industry 4.0? What are its main attributes? What is its irradiation logic? All conceptual definitions in scientific studies published in English and Portuguese available in the Google Scholar database from 2019 to 2020 were analyzed, with a total of 19 studies. The semantic analysis technique was used to identify the approaches and attributes, which were then organized by semantic proximity, constituting the semantic groupings on which the results were generated and interpreted. Findings: The results showed the existence of eight groups of approaches (procedures, evolution, time, grouping, transformation, layer, organization, and model) and twelve groups of attributes (industrialization, transformation technologies, information technologies, integrated system, processes, society, quality, knowledge, planning, market, value chain, and automation). These discoveries made it possible to understand that Industry 4.0 has a driving force: knowledge, which spreads similarly in waves until it reaches the entire society and is not limited only to the interior of factories and economic organizations. Research, Practical, and Social Implications: Two implications of these findings stand out. The first is the dynamic nature of implementing Industry 4.0 in organizations, whether economic or not, which is done more quickly if knowledge and technologies are available or more slowly when expertise and technologies are fragile or non-existent. The second is teleological since the purpose of Industry 4.0 and all industrial revolutions is to continually improve production processes, even though these benefits often generate unwanted externalities, such as environmental impacts. This means that the progress of knowledge and its developments cannot be stopped but can be directed towards other purposes. Originality/Value: The main contribution of this study to science is to point out the current stage of the evolution of Industry 4.0 about previous revolutions. Three other contributions are also significant: identifying the main approaches to the phenomenon, its most prominent characteristics, and its irradiation dynamics.

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Reviewing and Integrating AEC Practices into Industry 6.0: Strategies for Smart and Sustainable Future-Built Environments

Cited by 23 publications

References 116 publications

Application of Artificial Intelligence (AI) in Sustainable Building Lifecycle; A Systematic Literature Review

Application of Artificial Intelligence (AI) in Sustainable Building Lifecycle; A Systematic Literature Review

The Green Flexible Job-Shop Scheduling Problem Considering Cost, Carbon Emissions, and Customer Satisfaction under Time-of-Use Electricity Pricing

What is Industry 4.0: a Review of the Literature Using the Conceptual Bibliographic Method

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