Reference framework for integration between Life Cycle Assessment and Machine Learning
DOI:
https://doi.org/10.46421/entac.v20i1.5992Keywords:
Integrative model, Prediction, Optimization, ClusteringAbstract
This research identifies studies that associate Life Cycle Assessment (LCA) and machine learning (ML) and develops an integrative reference structure between these areas. The structuralist method was applied with the following design: identification of elements for integration based on a Systematic Literature Review (SLR), conceptualization, establishment of relationships between elements, elaboration of the structural model and analysis. The integrative structure categorizes and associates characteristic elements of LCA and ML. The study confirmed the LCA stages as the categories of its elements: goal and scope definition, life cycle inventory, life cycle impact assessment and interpretation; and related to ML: algorithm. It was possible to verify that ML helps in all stages of LCA, but prediction, optimization and clustering resources vary depending on the stage and objectives. The contribution of the integrative structure is to assist LCA professionals in selecting key ML resources incorporating artificial intelligence for different environmental impact assessment scenarios.
References
D’AMICO, B. et al. Machine Learning for Sustainable Structures: A Call for Data. Structures, v. 19, p. 1–4, 1 jun. 2019.
D’AMICO, A. et al. Artificial Neural Networks to assess energy and environmental performance of buildings: An Italian case study.
Journal of Cleaner Production, v. 239, p. 117993, dez. 2019.
LI, Yunpeng et al. A Data-Driven Approach for Improving Sustainability Assessment in Advanced Manufacturing. In: IEEE
INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2017, Boston, MA. Anais... Boston, MA: IEEE, 2017.
WINSTON, Patrick Henry. Artificial Intelligence. 3rd ed. ed. USA: Addison-Wesley Publishing Company, 1993.
BILAL, Muhammad et al. Big Data in the construction industry: A review of present status, opportunities, and future trends.
Advanced Engineering Informatics, v. 30, p. 500–521, 2016.
WANG, E.; SHEN, Z. Lifecycle Energy Consumption Prediction of Residential Buildings by Incorporating Longitudinal Uncertainties.
Journal of Civil Engineering and Management, v. 19, n. SUPPL.1, p. S161–S171, 2013.
XIA, Liming; LIU, Jingjing. Research on Green Building Assessment System Based on BP neural network and Life Cycle Assessment
(LCA). Applied Mechanics and Materials, v. 357–360, p. pp 508-514, 2013.
DUPREZ, Sandrine et al. Improving life cycle-based exploration methods by coupling sensitivity analysis and metamodels. ]
Sustainable Cities and Society, v. 44, p. 70–84, 2019.
PERROTTA, Federico et al. A machine learning approach for the estimation of fuel consumption related to road pavement rolling
resistance for large fleets of trucks. out. 2018, Belgium. Anais... Belgium: [s.n.], out. 2018. Disponível em:
eprints.nottingham.ac.uk/51400/>.
ZIYADI, Mojtaba; AL-QADI, Imad L. Model uncertainty analysis using data analytics for life-cycle assessment (LCA) applications. The
International Journal of Life Cycle Assessment, v. 24, n. 5, p. 945–959, maio 2019.
MA, Jungmok; KIM, Harrison M. Predictive Usage Mining for Life Cycle Assessment. Transportation Research Part D: Transport and
Environment, v. 38, p. 125–143, 2015.
MARVUGLIA, Antonino; KANEVSKI, Mikhail; BENETTO, Enrico. Machine learning for toxicity characterization of organic chemical
emissions using USEtox database: Learning the structure of the input space. Environment International, v. 83, p. 72–85, 1 out.
AZARI, Rahman et al. Multi-Objective Optimization of Building Envelope Design for Life Cycle Environmental Performance. Energy
and Buildings, v. 126, p. 524–534, 15 ago. 2016.
SCHWARTZ, Yair; RASLAN, Rokia; MUMOVIC, Dejan. Implementing multi objective genetic algorithm for life cycle carbon footprint
and life cycle cost minimisation: A building refurbishment case study. Energy, v. 97, p. 58–68, 15 fev. 2016.
SHI, Qian; XU, Yilong. The Selection of Green Building Materials Using GA-BP Hybrid Algorithm. In: 2009 INTERNATIONAL
CONFERENCE ON ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, nov. 2009, Shanghai, China. Anais... Shanghai,
China: IEEE, nov. 2009. p. 40–45.
THIRY-CHERQUES, Hermano Roberto. O Primeiro Estruturalismo: Método de Pesquisa para as Ciências da Gestão. RAC, v. 10, n. 2,
p. 137–156, 2006.
DRESCH, Aline; LACERDA, Daniel Pacheco; ANTUNES JUNIOR, Jose Antonio Valle. Design Science Research: A Method for Science
and Technology Advancement. Switzerland: Springer International Publishing, 2015.
FENG, Kailun; LU, Weizhuo; WANG, Yaowu. Assessing Environmental Performance in Early Building Design Stage: An Integrated
Parametric Design and Machine Learning Method. Sustainable Cities and Society, v. 50, p. 101596, 1 out. 2019.
GOLAK, Sławomir et al. Application of Neural Network for the Prediction of Eco-Efficiency. Lecture Notes in Computer Science,
, Berlin, Heidelberg. Anais... Berlin, Heidelberg: Springer, 2011. p. 380–387.
SHARIF, Seyed Amirhosain; HAMMAD, Amin. Developing surrogate ANN for selecting near-optimal building energy renovation
methods considering energy consumption, LCC and LCA. Journal of Building Engineering, v. 25, p. 100790, set. 2019.
TEODOSIO, B. et al. Environmental, economic, and serviceability attributes of residential foundation slabs: A comparison between
waffle and stiffened rafts using multi-output deep learning. Journal of Building Engineering, v. 80, p. 107983, 1 dez. 2023.
JI, Sukwon et al. Effect of realistically estimated building lifespan on life cycle assessment: A case study in Korea. Journal of
Building Engineering, v. 75, p. 107028, 15 set. 2023.
JI, Sukwon; LEE, Bumho; YI, Mun Yong. Building life-span prediction for life cycle assessment and life cycle cost using machine
learning: A big data approach. Building and Environment, v. 205, p. 108267, 1 nov. 2021.
MARTÍNEZ-ROCAMORA, Alejandro et al. Environmental benchmarking of building typologies through BIM-based combinatorial
case studies. Automation in Construction, v. 132, p. 103980, 1 dez. 2021.
ESTEGHAMATI, Mohsen Zaker; FLINT, Madeleine M. Developing data-driven surrogate models for holistic performance-based
assessment of mid-rise RC frame buildings at early design. Engineering Structures, v. 245, p. 112971, 15 out. 2021.
BRAGANÇA, Luís; MUNIESA, María Concepción Verde. Measuring Carbon in Cities and Their Buildings through Reverse
Engineering of Life Cycle Assessment. Applied System Innovation, v. 6, n. 5, p. 76, out. 2023.
KHARBANDA, Kritika et al. LearnCarbon: 40th Conference on Education and Research in Computer Aided Architectural Design in
Europe, eCAADe 2022. In: 42ND ECAADE CONFERENCE 2022 - CO-CREATING THE FUTURE, Proceedings of the International
Conference on Education and Research in Computer Aided Architectural Design in Europe, 2022, Nicosia, Cyprus. Anais... Nicosia,
Cyprus: Education and research in Computer Aided Architectural Design in Europe, 2022. p. 601–610. Disponível em:
www.scopus.com/inward/record.url?scp=85139248291&partnerID=8YFLogxK>. Acesso em: 29 fev. 2024.
KOYAMPARAMBATH, Anish et al. Implementing Artificial Intelligence Techniques to Predict Environmental Impacts: Case of
Construction Products. Sustainability, v. 14, n. 6, p. 1–12, 2022.
SU, Shu et al. Temporal dynamic assessment of household energy consumption and carbon emissions in China: From the
perspective of occupants. Sustainable Production and Consumption, v. 37, p. 142–155, 1 maio 2023.
TOOSI, Hashem Amini et al. A novel LCSA-Machine learning based optimization model for sustainable building design-A case study
of energy storage systems. Building and Environment, v. 209, p. 108656, 1 fev. 2022.
COLLETO, Giseli Mary; GOMES, Vanessa. Exploring Machine Learning-Based Archetypes for Urban Life Cycle Modeling (UBiM). In:
CENTRAL EUROPE TOWARDS SUSTAINABLE BUILDING 2022 (CESB22), 1., 21 dez. 2022, Prague. Anais... Prague: CTU, 21 dez. 2022.
p. 169–175. Disponível em: <https://ojs.cvut.cz/ojs/index.php/APP/article/view/8295>. Acesso em: 22 fev. 2024.
ABDOU, N. et al. Prediction and optimization of heating and cooling loads for low energy buildings in Morocco: An application of
hybrid machine learning methods. Journal of Building Engineering, v. 61, p. 105332, 1 dez. 2022.
ZHOU, Yijun; TAM, Vivian WY.; LE, Khoa N. Trade-off between embodied and operational carbon emissions of residential buildings
in early design stage. In: 36TH INTERNATIONAL CONFERENCE ON EFFICIENCY, COST, OPTIMIZATION, SIMULATION AND
ENVIRONMENTAL IMPACT OF ENERGY SYSTEMS (ECOS 2023), 2023, Las Palmas de Gran Canaria, Spain. Anais... Las Palmas de Gran
Canaria, Spain: ECOS 2023, 2023.
ABOKERSH, Mohamed Hany et al. Sustainability insights on emerging solar district heating technologies to boost the nearly zero
energy building concept. Renewable Energy, v. 180, p. 893–913, 1 dez. 2021.
APELLÁNIZ, Diego; PETTERSSON, Björn; GENGNAGEL, Christoph. A Flexible Reinforcement Learning Framework to Implement
Cradle-to-Cradle in Early Design Stages. 2023, Cham. Anais... Cham: Springer International Publishing, 2023. p. 3–12.
SONG, Junkang et al. Framework on Low-Carbon Retrofit of Rural Residential Buildings in Arid Areas of Northwest China: A Case
Study of Turpan Residential Buildings. Building Simulation, v. 16, n. 2, p. 279–297, 1 fev. 2023.
FARAHZADI, Leila; KIOUMARSI, Mahdi. Application of machine learning initiatives and intelligent perspectives for CO2 emissions
reduction in construction. Journal of Cleaner Production, v. 384, p. 135504, 15 jan. 2023.
GHOROGHI, Ali et al. Advances in Application of Machine Learning to Life Cycle Assessment: A Literature Review. The International
Journal of Life Cycle Assessment, v. 27, n. 3, p. 433–456, 1 mar. 2022.
BARROS, Natalia Nakamura; RUSCHEL, Regina Coeli. Machine Learning for Whole-Building Life Cycle Assessment: A Systematic
Literature Review. In: 18TH INTERNATIONAL CONFERENCE ON COMPUTING IN CIVIL AND BUILDING ENGINEERING, 2021, São
Paulo. Anais... São Paulo: Springer Nature Switzerland, 2021. p. 109–122.
ALGREN, Mikaela; FISHER, Wendy; LANDIS, Amy E. Chapter 8 - Machine learning in life cycle assessment. In: DUNN, Jennifer;
BALAPRAKASH, Prasanna (Org.). . Data Science Applied to Sustainability Analysis. [S.l.]: Elsevier, 2021. p. 167–190. Disponível em:
<https://www.sciencedirect.com/science/article/pii/B9780128179765000097>. Acesso em: 29 fev. 2024.
