Machine Learning for Whole-Building Life Cycle Assessment: A Systematic Literature Review

Barros, Natália Nakamura; Ruschel, Regina Coeli

doi:10.1007/978-3-030-51295-8_10

Cited by 9 publications

(17 citation statements)

References 14 publications

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“…At the buildings level, through the combined use of ML in LCA, it may be possible to significantly reduce environmental impacts (Barros and Ruschel 2021). D'Amico et al employed ML methods in civil and structural engineering in order to reduce building impacts (D'Amico et al 2019b).…”

Section: Methods In Lcamentioning

confidence: 99%

“…D'Amico et al employed ML methods in civil and structural engineering in order to reduce building impacts (D'Amico et al 2019b). Barros and Ruschel performed a systematic literature review of the scientific research conducted for architecture, engineering and construction industries in the context of LCA and ML (Barros and Ruschel 2021). They show that the most investigated environmental indicators were energy consumption and Global Warming Potential (GWP).…”

Section: Methods In Lcamentioning

confidence: 99%

“…Furthermore, the implementation of ANN in software can accommodate the development of an appropriate decision support tool. Thus, ML algorithms and techniques may be capable of increasing accuracy in LCA and reducing the simulation time (Sharif and Hammad 2019;Barros and Ruschel 2021;D'Amico et al 2019a). However, the validity of the NN solution is directly and powerfully proportional to the reliability of the database, which tends to be the most difficult to implement.…”

Section: Neural Networkmentioning

confidence: 99%

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Advances in application of machine learning to life cycle assessment: a literature review

Ghoroghi

Rezgui

Petri

et al. 2022

Int J Life Cycle Assess

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Purpose Life Cycle Assessment (LCA) is the process of systematically assessing impacts when there is an interaction between the environment and human activity. Machine learning (ML) with LCA methods can help contribute greatly to reducing impacts. The sheer number of input parameters and their uncertainties that contribute to the full life cycle make a broader application of ML complex and difficult to achieve. Hence a systems engineering approach should be taken to apply ML in isolation to aspects of the LCA. This study addresses the challenge of leveraging ML methods to deliver LCA solutions. The overarching hypothesis is that: LCA underpinned by ML methods and informed by dynamic data paves the way to more accurate LCA while supporting life cycle decision making. Methods In this study, previous research on ML for LCA were considered, and a literature review was undertaken. Results The results showed that ML can be a useful tool in certain aspects of the LCA. ML methods were shown to be applied efficiently in optimization scenarios in LCA. Finally, ML methods were integrated as part of existing inventory databases to streamline the LCA across many use cases. Conclusions The conclusions of this article summarise the characteristics of existing literature and provide suggestions for future work in limitations and gaps which were found in the literature.

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Section: Methods In Lcamentioning

confidence: 99%

Section: Methods In Lcamentioning

confidence: 99%

Section: Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

Advances in application of machine learning to life cycle assessment: a literature review

Ghoroghi

Rezgui

Petri

et al. 2022

Int J Life Cycle Assess

View full text Add to dashboard Cite

show abstract

“…Again, recent developments incorporating technologies in the IoT spectrum will boost and consolidate trends in the not too far-away horizon. Although that applications emerge along with the AEC industry, there is more to be made in specific areas, including: Energy, Thermal comfort, Life cycle, costs, Optimum design, Life cycle assessment, Sustainability, and Quality [19], [22].What to expect for tomorrow, between the most notable projection the arrival of Robotic Automation. Developing advanced, usable, humanfriendly, and smart robots may be around already.…”

Section: ) Applications and Areas Of Developmentsmentioning

confidence: 99%

“…Another area where BIM can be potentiated because of the awareness of the increasingly important impacts of the construction process is sustainability. BIM integration with life cycle assessment (LCA) improves the whole-building life cycle [22]. There are advantages and disadvantages to BIM use.…”

Section: ) Sustainabilitymentioning

confidence: 99%

Towards an AEC-AI Industry Optimization Algorithmic Knowledge Mapping: An Adaptive Methodology for Macroscopic Conceptual Analysis

Maureira¹,

Pinto

Yepes³

et al. 2021

IEEE Access

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The Architecture, Engineering, and Construction (AEC) Industry is one of the most important productive sectors, hence also produce a high impact on the economic balances, societal stability, and global challenges in climate change. Regarding its adoption of technologies, applications and processes is also recognized by its status-quo, its slow innovation pace, and the conservative approaches. However, a new technological era -Industry 4.0 fueled by AI-is driving productive sectors in a highly pressurized global technological competition and sociopolitical landscape. In this paper, we develop an adaptive approach to mining text content in the literature research corpus related to the AEC and AI (AEC-AI) industries, in particular on its relation to technological processes and applications. We present a first stage approach to an adaptive assessment of AI algorithms, to form an integrative AI platform in the AEC industry, the AEC-AI industry 4.0. At this stage, a macroscopic adaptive method is deployed to characterize "Optimization", a key term in AEC-AI industry, using a mixed methodology incorporating machine learning and classical evaluation process. Our results show that effective use of metadata, constrained search queries, and domain knowledge allows getting a macroscopic assessment of the target concept. This allows the extraction of a high-level mapping and conceptual structure characterization of the literature corpus. The results are comparable, at this level, to classical methodologies for the literature review. In addition, our method is designed for an adaptive assessment to incorporate further stages.

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Scientometric analysis and panoramic review on life cycle assessment in the construction industry

Aparna,

Baskar

2024

Innov. Infrastruct. Solut.

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Machine Learning for Whole-Building Life Cycle Assessment: A Systematic Literature Review

Cited by 9 publications

References 14 publications

Advances in application of machine learning to life cycle assessment: a literature review

Advances in application of machine learning to life cycle assessment: a literature review

Towards an AEC-AI Industry Optimization Algorithmic Knowledge Mapping: An Adaptive Methodology for Macroscopic Conceptual Analysis

Scientometric analysis and panoramic review on life cycle assessment in the construction industry

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