uujfe Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 2148-4155 Bursa Uludağ University 10.17482/uumfd.1649878 Building (Other) Yapı (Diğer) Yapay Zekânın Yapı Sektöründe Yaşam Döngüsü Değerlendirmesine Entegrasyonu: Bibliyometrik ve Eleştirel Bir İnceleme INTEGRATING ARTIFICIAL INTELLIGENCE INTO LIFE CYCLE ASSESSMENT IN THE BUILDING INDUSTRY: A BIBLIOMETRIC AND CRITICAL REVIEW https://orcid.org/0000-0002-3785-4195 Yardimci Yigit BURSA ULUDAĞ ÜNİVERSİTESİ, MİMARLIK FAKÜLTESİ, MİMARLIK BÖLÜMÜ https://orcid.org/0000-0002-2290-3097 Erbil Yasemin Bursa Uludağ Üniversitesi 04 10 2026 31 1 447 464 03 02 2025 02 04 2026 Copyright © 2002, Uludağ University Journal of The Faculty of Engineering 2002 Uludağ University Journal of The Faculty of Engineering

Binaların çevresel etkilerindeki artış, sürdürülebilirlik değerlendirme araçlarının kapsam ve güvenilirliğine yönelik ihtiyacı belirginleştirmiş; bu bağlamda Yaşam Döngüsü Değerlendirmesi (LCA), çevresel performansın nicel analizinde merkezi bir konuma yerleşmiştir. Yapay zekâ (YZ), LCA süreçlerinin doğruluğunu, verimliliğini ve otomasyon potansiyelini artırmada stratejik bir araç olarak öne çıkmaktadır. Bu çalışma, inşaat sektöründe YZ entegreli LCA araştırmalarına yönelik güncel ve eleştirel bir inceleme sunmaktadır. Çalışmada, Web of Science ve Scopus veri tabanlarından elde edilen 883 yayının bibliyometrik analizi ile YZ tekniklerinin LCA iş akışlarına açıkça entegre edildiği, metodolojik şeffaflığa sahip 18 seçilmiş makalenin sistematik değerlendirmesi yürütülmüştür. Bulgular, makine öğrenmesi ve yapay sinir ağlarının enerji tüketimi ve karbon emisyonu tahminlerinde yaygın kullanıldığını göstermektedir. Ancak YZ-LCA entegrasyonu; yapısal olmayan veriler, standart protokol eksikliği, düşük araç uyumluluğu ve kaliteli veriye kısıtlı erişim nedeniyle henüz bütüncül bir yapıya ulaşamamıştır. Mevcut literatür çoğunlukla operasyonel enerjiye odaklanıp, gömülü etkileri ve geniş sürdürülebilirlik göstergelerini ihmal etmektedir. Gelecek araştırmalar standart veri şemaları, gerçek zamanlı izleme ve vaka temelli doğrulamalar içeren YZ çerçevelerine öncelik vermelidir. Bu entegrasyon, yapı sektöründe veriye dayalı, şeffaf ve uyarlanabilir sürdürülebilirlik stratejileri için dönüştürücü potansiyel taşımaktadır.

Increasing environmental impacts of buildings necessitate robust sustainability assessment tools, positioning Life Cycle Assessment (LCA) as a central methodology for evaluating environmental performance. Artificial Intelligence (AI) offers strategic potential to enhance the accuracy, efficiency, and automation of LCA processes. This study critically reviews AI-integrated LCA research in the construction sector. A bibliometric analysis of 883 publications from Web of Science and Scopus was conducted, alongside a systematic review of 18 articles explicitly integrating AI into LCA workflows. Findings show Machine Learning (ML) and Artificial Neural Networks (ANN) are predominantly used to predict energy consumption and carbon emissions. However, AI-LCA integration remains fragmented due to unstructured data, lack of standardized protocols, low interoperability, and restricted access to high-quality datasets. Current literature primarily focuses on operational energy use, largely neglecting embodied impacts and broader sustainability indicators. Future research should prioritize AI frameworks incorporating standardized data schemas, real-time monitoring, and case-based validation. Integrating AI into LCA offers transformative potential for data-driven, transparent, and adaptable sustainability strategies in construction.

 Buildings Life cycle assessments Artificial intelligence Bibliometric analysis Systematic analysis Yapılar Yaşam döngüsü değerlendirmesi Yapay zeka Bibliyometrik analiz Sistematik analiz Abioye, S. O., Oyedele, L. O., Akanbi, L., Ajayi, A., Delgado, J. M. D., Bilal, M., ... & Ahmed, A. (2021). Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. Journal of Building Engineering, 44, 103299. Ahmed, M., AlQadhi, S., Mallick, J., Kahla, N. B., Le, H. A., Singh, C. K., & Hang, H. T. (2022). Artificial neural networks for sustainable development of the construction industry. Sustainability, 14(22), 14738. Amankwah-Amoah, J., Khan, Z., Wood, G., & Knight, G. (2021). COVID-19 and digitalization: The great acceleration. Journal of business research, 136, 602-611. Aria, M. & Cuccurullo, C. (2017) bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. Asdrubali, F., Baldassarri, C., & Fthenakis, V. (2013). Life cycle analysis in the construction sector: Guiding the optimization of conventional Italian buildings. Energy and Buildings, 64, 73-89. Basbagill, J., Flager, F., Lepech, M., & Fischer, M. (2013). Application of life-cycle assessment to early stage building design for reduced embodied environmental impacts. Building and Environment, 60, 81-92. Bonoli, A., Zanni, S., & Serrano-Bernardo, F. (2021). Sustainability in building and construction within the framework of circular cities and european new green deal. The contribution of concrete recycling. Sustainability, 13(4), 2139. Change, C. (2022). Mitigating Climate Change. Working Group III contribution to the sixth assessment report of the intergovernmental panel on climate change. Chong, H. Y., Lee, C. Y., & Wang, X. (2017). A mixed review of the adoption of Building Information Modelling (BIM) for sustainability. Journal of Cleaner Production, 142, 4114-4126. D'Amico, A., Ciulla, G., Traverso, M., Brano, V. L., & Palumbo, E. (2019). Artificial Neural Networks to assess energy and environmental performance of buildings: An Italian case study. Journal of Cleaner Production, 239, 117993. D'Amico, B., Myers, R. J., Sykes, J., Voss, E., Cousins-Jenvey, B., Fawcett, W., ... & Pomponi, F. (2019). Machine learning for sustainable structures: a call for data. In Structures, 19, 1-4. Egwim, C. N., Alaka, H., Demir, E., Balogun, H., Olu-Ajayi, R., Sulaimon, I., ... & Muideen, A. A. (2023). Artificial intelligence in the construction industry: A systematic review of the entire construction value chain lifecycle. Energies, 17(1), 182. Eleftheriadis, S., Mumovic, D., & Greening, P. (2017). Life cycle energy efficiency in building structures: A review of current developments and future outlooks based on BIM capabilities. Renewable and Sustainable Energy Reviews, 67, 811-825. Elrefaey, O., Ahmed, S., Ahmad, I., & El-Sayegh, S. (2022). Impacts of COVID-19 on the Use of Digital Technology in Construction Projects in the UAE. Buildings, 12(4), 489. Farahzadi, L., & Kioumarsi, M. (2022). Application of machine learning initiatives and intelligent perspectives for CO2 emissions reduction in construction. Journal of Cleaner Production, 135504. Frischknecht, R., Jungbluth, N., Althaus, H. J., Doka, G., Dones, R., Heck, T., ... & Spielmann, M. (2005). The ecoinvent database: overview and methodological framework (7 pp). The International Journal of Life Cycle Assessment, 10, 3-9. Fu, C., Lu, L., & Pirabi, M. (2023). Advancing green finance: a review of sustainable development. Digital Economy and Sustainable Development, 1(1), 20. Ghoroghi, A., Rezgui, Y., Petri, I., & Beach, T. (2022). Advances in application of machine learning to life cycle assessment: a literature review. The International Journal of Life Cycle Assessment, 27(3), 433-456. Gür, M., & Karadag, I. (2024). Machine learning for pedestrian-level wind comfort analysis. Buildings, 14(6), 1845. Hawkins, D. T. (1978). Bibliometrics of the online information retrieval literature. Online Review, 2(4), 345-352. Hou, Q., Mao, G., Zhao, L., Du, H., & Zuo, J. (2015). Mapping the scientific research on life cycle assessment: a bibliometric analysis. The International Journal of Life Cycle Assessment, 20, 541-555. Ilhan, B., & Yaman, H. (2016). Green building assessment tool (GBAT) for integrated BIM-based design decisions. Automation in Construction, 70, 26-37. International Standard Organization, (1997). ISO 14040: Environmental Management-Life Cycle Assessment-Principles and Framework. Ji, S., Lee, B., & Yi, M. Y. (2021). Building life-span prediction for life cycle assessment and life cycle cost using machine learning: A big data approach. Building and Environment, 205, 108267. Karadag, I., & Gür, M. (2025). Machine Learning for Wind Speed Estimation. Buildings, 15(9), 1541. Li, D., Cui, P., & Lu, Y. (2016). Development of an automated estimator of life-cycle carbon emissions for residential buildings: A case study in Nanjing, China. Habitat international, 57, 154-163. Liao, M., Kelley, S., & Yao, Y. (2019). Generating energy and greenhouse gas inventory data of activated carbon production using machine learning and kinetic based process simulation. ACS Sustainable Chemistry & Engineering, 8(2), 1252-1261. Ligozat, A. L., Lefevre, J., Bugeau, A., & Combaz, J. (2022). Unraveling the hidden environmental impacts of AI solutions for environment life cycle assessment of AI solutions. Sustainability, 14(9), 5172. Long, Y., Xin, J., Huang, B., Wang, Y., He, X., & Song, Q. (2025). Status and challenges of building carbon-neutral pathways: Comparative analysis in major world economies. Environmental Impact Assessment Review, 112, 107825. Lu, Y., Wu, Z., Chang, R., & Li, Y. (2017). Building Information Modeling (BIM) for green buildings: A critical review and future directions. Automation in Construction, 83, 134-148. Meex, E., Hollberg, A., Knapen, E., Hildebrand, L., & Verbeeck, G. (2018). Requirements for applying LCA-based environmental impact assessment tools in the early stages of building design. Building and Environment, 133, 228-236. Mohammed, A. B. (2023). Process Map for Accessing Automatization of Life Cycle Assessment Utilizing Building Information Modeling. Journal of Architectural Engineering, 29(3), 04023012. Moutik, B., Summerscales, J., Graham-Jones, J., & Pemberton, R. (2023). Life Cycle Assessment Research Trends and Implications: A Bibliometric Analysis. Sustainability, 15(18), 13408. Najjar, M., Figueiredo, K., Palumbo, M. & Haddad, A. (2017). Integration of BIM and LCA: evaluating the environmental impacts of building materials at an early stage of designing a typical office building. Journal of Building Engineering, 14, 115-126. Neupane, B., Belkadi, F., Formentini, M., Rozière, E., Hilloulin, B., Abdolmaleki, S. F., & Mensah, M. (2025). Machine learning algorithms for supporting life cycle assessment studies: An analytical review. Sustainable Production and Consumption, 56, 37-53. Organization of the Petroleum Exporting Countries (OPEC). (2025). World oil outlook 2025. OPEC. https://www.opec.org/assets/assetdb/woo-2025-1.pdf (Accessed date: 11.08.2025). Prabhakar, A. (2025). A Sustainable and Inclusive Economic Development: A Global Imperative: A Global Imperative. Journal of Recycling Economy & Sustainability Policy, 4(1), 1-16. Peng, C. (2016). Calculation of a building's life cycle carbon emissions based on Ecotect and building information modeling. Journal of Cleaner Production, 112, 453-465. Popowicz, M., Katzer, N. J., Kettele, M., Schöggl, J. P., & Baumgartner, R. J. (2025). Digital technologies for life cycle assessment: a review and integrated combination framework. The International Journal of Life Cycle Assessment, 30(3), 405-428. Röck, M., Hollberg, A., Habert, G., & Passer, A. (2018). LCA and BIM: Visualization of environmental potentials in building construction at early design stages. Building and Environment, 140, 153-161. Růžička, J., Veselka, J., Rudovský, Z., Vitásek, S., & Hájek, P. (2022). BIM and automation in complex building assessment. Sustainability, 14(4), 2237. Sala, S., Amadei, A. M., Beylot, A., & Ardente, F. (2021). The evolution of life cycle assessment in European policies over three decades. The International Journal of Life Cycle Assessment, 26(12), 2295-2314. Santos, R., Costa, A. A., & Grilo, A. (2017). Bibliometric analysis and review of Building Information Modelling literature published between 2005 and 2015. Automation in Construction, 80, 118-136. Santos, R., Costa, A. A., Silvestre, J. D., & Pyl, L. (2019). Integration of LCA and LCC analysis within a BIM-based environment. Automation in Construction, 103, 127-149. Savino, P., & Tondolo, F. (2021). Automated classification of civil structure defects based on convolutional neural network. Frontiers of Structural and Civil Engineering, 15(2), 305-317. Serrano-Baena, M. M., Ruiz-Díaz, C., Boronat, P. G., & Mercader-Moyano, P. (2023). Optimising LCA in complex buildings with MLCAQ: a BIM-based methodology for automated multi-criteria materials selection. Energy and Buildings, 294, 113219. Shadram, F., Johansson, T. D., Lu, W., Schade, J., & Olofsson, T. (2016). An integrated BIM-based framework for minimizing embodied energy during building design. Energy and Buildings, 128, 592-604. Soust-Verdaguer, B., Llatas, C. & García-Martínez, A. (2017). Critical review of bim-based LCA method to buildings. Energy and Buildings, 136, 110–120, https://doi.org/10.1016/j.enbuild.2016.12.009. Toosi, H. A., Lavagna, M., Leonforte, F., Del Pero, C., & Aste, N. (2022). A novel LCSA-Machine learning based optimization model for sustainable building design-A case study of energy storage systems. Building and Environment, 209, 108656. Qian, G. (2014). Scientometric sorting by importance for literatures on life cycle assessments and some related methodological discussions. The International Journal of Life Cycle Assessment, 19, 1462–1467. UNEP (2020). 2020 Global Status Report for Buildings and Construction. https://globalabc.org/resources/publications/2020-global-status-report-buildings-and-construction. (Accessed date: 11.08.2025). UNEP Life Cycle Initiative (2025). Paris Agreement, Sustainable Development Goals and Life Cycle Thinking. https://www.lifecycleinitiative.org/paris-agreement-sustainable-development-goals-life-cycle-thinking/. (Accessed date: 11.08.2025). Venkatraj, V., Dixit, M. K., Yan, W., Caffey, S., Sideris, P., & Aryal, A. (2023). Toward the application of a machine learning framework for building life cycle energy assessment. Energy and Buildings, 297, 113444. Yan, S., Zhang, Y., Sun, H., & Wang, A. (2023). A real-time operational carbon emission prediction method for the early design stage of residential units based on a convolutional neural network: A case study in Beijing, China. Journal of Building Engineering, 75, 106994. Yardımcı, Y., Erbil, Y., Ediz, Ö., & Karadağ, İ. (2025). Çok Katlı Konut Yapılarına Yönelik BIM-LCA Tabanlı Çevresel Değerlendirme ve Tasarım İyileştirme Stratejileri: Bir Vaka Çalışması. 19. Mimarlıkta Sayısal Tasarım Ulusal Sempozyumu, Antalya Bilim Üniversitesi, Antalya, Türkiye,184-194 Yardımcı, Y., Colak Demirel, B. B., & Ertosun Yıldız, M. (2024). Climatic Influences on the Environmental Performances of Residential Buildings: A Comparative Case Study in Turkey. Buildings, 14(12), 4015. Yardimci, Y., & Kurucay, E. (2024). LCA-TOPSIS Integration for Minimizing Material Waste in the Construction Sector: A BIM-Based Decision-Making. Buildings, 14(12), 3919. Yılmaz, Y., & Seyis, S. (2021). Mapping the scientific research of the life cycle assessment in the construction industry: A scientometric analysis. Building and Environment, 204, 108086. Yung, P., & Wang, X. (2014). A 6D CAD model for the automatic assessment of building sustainability. International Journal of Advanced Robotic Systems, 11(8), 131. Żarczyńska, A. & Nicola De Bellis (2012). Bibliometrics and Citation Analysis, from the Science Citation Index to Cybermetrics, Lanham, Toronto, Plymouth 2009. Toruńskie Stud. Bibliol. , 5, 155–157. Zhu, J. & Liu, W. (2020) A tale of two databases: The use of Web of Science and Scopus in academic papers. Scientometrics, 123, 321–335.