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Technological Advances in AEC: AI, Machine Learning, BIM, and the Future of Sustainable Building Design in a Post-Pandemic World

Yıl 2025, Cilt: 8 Sayı: 3, 757 - 774, 15.05.2025
https://doi.org/10.34248/bsengineering.1603629

Öz

The Architecture, Engineering, and Construction (AEC) industry has experienced a profound transformation, accelerated by the COVID-19 pandemic, which underscored the need for digital solutions to enhance sustainability and resilience. Despite existing technological advancements, the pandemic revealed gaps in the widespread adoption of tools like Artificial Intelligence (AI), Machine Learning (ML), Building Information Modeling (BIM), Internet of Things (IoT), and Digital Twins (DT). This study addresses the critical question of how these technologies have reshaped building design, construction, and operation processes to meet sustainability goals in the post-pandemic era. A systematic literature review of 35 peer-reviewed studies published between 2019 and 2024 was conducted to analyze the impact of key digital technologies on sustainable building practices. The research employed a thematic analysis focusing on technological advancements, sustainability applications, challenges and barriers, and emerging trends such as smart cities, renewable energy integration, and circular economy principles. The findings reveal that technologies like AI and DTs play a pivotal role in enhancing energy efficiency, enabling predictive maintenance, and improving lifecycle resource management. However, barriers such as interoperability issues, high implementation costs, and data security concerns persist, hindering widespread adoption. The study emphasizes the growing trend toward data-driven sustainability and the need to address these challenges through collaborative frameworks and technological innovation. In conclusion, this research highlights the transformative potential of digital technologies in advancing sustainability and resilience within the AEC industry. By bridging the gap between technological innovation and sustainable development goals, this study provides actionable insights for overcoming existing barriers and fostering adaptive, energy-efficient, and environmentally responsible built environments in a post-pandemic world.

Kaynakça

  • Adewale BA, Ene VO, Ogunbayo BF, Aigbavboa CO. 2024. A systematic review of the applications of AI in a sustainable building’s lifecycle. Buildings, 14(7): 2137. https://doi.org/10.3390/buildings14072137.
  • Alizadehsalehi S, Hadavi A, Huang JC. 2020. From BIM to extended reality in AEC industry. Autom. Constr., 116: 103254. https://doi.org/10.1016/j.autcon.2020.103254.
  • Amoatey P, Omidvarborna H, Baawain M, Al-Mamun A. 2020. Impact of building ventilation systems and habitual indoor incense burning on SARS-CoV-2 virus transmissions in Middle Eastern countries. Sci. Total Environ., 733: 139356. https://doi.org/10.1016/j.scitotenv.2020.139356.
  • Aniekan Akpan Umoh CN, Nwasike OAT, Adekoya O O, Gidiagba JO. 2024. A review of smart green building technologies: Investigating the integration and impact of AI and IoT in sustainable building designs. Comput. Sci. IT Res. J., 5(1): 141–165. https://doi.org/10.51594/csitrj.v5i1.715.
  • Arowoiya VA, Moehler RC, Fang Y. 2024. Digital twin technology for thermal comfort and energy efficiency in buildings: A state-of-the-art and future directions. Energy Built Environ., 5(5): 641–656. https://doi.org/10.1016/j.enbenv.2023.05.004.
  • Arsecularatne B, Rodrigo N, Chang R. 2024. Digital twins for reducing energy consumption in buildings: A review. Sustainability, 16(21): 9275. https://doi.org/10.3390/su16219275.
  • Asif M, Naeem G, Khalid M. 2024. Digitalization for sustainable buildings: Technologies, applications, potential, and challenges. J. Clean. Prod., in press.
  • Badenko V, Bolshakov N, Celani A, Puglisi V. 2024. Principles for sustainable integration of BIM and digital twin technologies in industrial infrastructure. Sustainability, 16(22): 9885. https://doi.org/10.3390/su16229885.
  • Banai R. 2020. Pandemic and the planning of resilient cities and regions. Cities, 106: 102929. https://doi.org/10.1016/j.cities.2020.102929.
  • Bibri SE, Huang J, Jagatheesaperumal SK, Krogstie J. 2024. The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review. Environ. Sci. Ecotechnol., 20: 100433. https://doi.org/10.1016/j.ese.2024.100433.
  • Chen X, Chang-Richards AY, Ling FYY, Yiu TW, Pelosi A, Yang N. 2024. Digital technology-enabled AEC project management: Practical use cases, deployment patterns and emerging trends. Eng Constr Archit Manag, online publication. https://doi.org/10.1108/ECAM-09-2023-0962.
  • Cheshmehzangi A. 2021. Revisiting the built environment: 10 potential development changes and paradigm shifts due to COVID-19. J Urban Manag, 10: 166–175. https://doi.org/10.1016/j.jum.2021.01.002.
  • Coraglia UM, Simeone D, Bragadin MA. 2024. Research perspectives on buildings’ sustainability after COVID-19: Literature review and analysis of changes. Buildings, 14(2): 482. https://doi.org/10.3390/buildings14020482.
  • Darko A, Chan APC, Adabre MA, Edwards DJ, Hosseini MR, Ameyaw EE. 2020. Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities. Autom Constr, 112: 103081. https://doi.org/10.1016/j.autcon.2020.103081.
  • De Las Heras et al. 2020. Machine learning technologies for sustainability in smart cities in the post-COVID era. Sustainability, 12(22): 9320. https://doi.org/10.3390/su12229320.
  • Elavarasan R, Pugazhendhi R. 2020. Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic. Sci. Total Environ., 725: 138858. https://doi.org/10.1016/j.scitotenv.2020.138858.
  • 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. https://doi.org/10.3390/buildings12040489.
  • Ferdaus MM, Dam T, Anavatti S, Das S. 2024. Digital technologies for a net-zero energy future: A comprehensive review. Renew Sustain Energy Rev, 202: 114681. https://doi.org/10.1016/j.rser.2024.114681.
  • Gorina L, Korneeva E, Kovaleva O, Strielkowski W. 2024. Energy-saving technologies and energy efficiency in the post-COVID era. Sustain Dev, 32(5): 5294–5310. https://doi.org/10.1002/sd.2978.
  • Gurram MK, Wang MX, Wang YC, Pang J. 2022. Impact of urbanization and environmental factors on spatial distribution of COVID-19 cases during the early phase of epidemic in Singapore. Sci Rep, 12: 9758. https://doi.org/10.1038/s41598-022-12941-8.
  • Han P, Wang L, Song Y, Zheng X. 2022. Designing for the post-pandemic era: Trends, focuses, and strategies learned from architectural competitions based on a text analysis. Front Public Health, 10: 1084562. https://doi.org/10.3389/fpubh.2022.1084562.
  • Inusah Y, Kazaz A, Ulubeyli S. 2025. Barriers to e-tendering implementation in the construction industry: A comprehensive review and analysis of a decade and beyond. Sustainability, 17(5): 2052. https://doi.org/10.3390/su17052052.
  • Jogunola O, Morley C, Akpan IJ, Tsado Y, Adebisi B, Yao L. 2022. Energy consumption in commercial buildings in a post-COVID-19 world. IEEE Eng Manag Rev, 50(1): 54–64. https://doi.org/10.1109/EMR.2022.3146591.
  • Karatzas S, Papageorgiou G, Lazari V, Bersimis S, Fousteris A, Economou P, Chassiakos A. 2024. A text analytic framework for gaining insights on the integration of digital twins and machine learning for optimizing indoor building environmental performance. Dev Built Environ, 18: 100386. https://doi.org/10.1016/j.dibe.2024.100386.
  • Kazeem KO, Olawumi TO, Osunsanmi T. 2023. Roles of artificial intelligence and machine learning in enhancing construction processes and sustainable communities. Buildings, 13(8): 2061. https://doi.org/10.3390/buildings13082061.
  • Kozlovska M, Petkanic S, Vranay F, Vranay D. 2023. Enhancing energy efficiency and building performance through BEMS-BIM integration. Energies, 16(17): 6327. https://doi.org/10.3390/en16176327.
  • Lv Z, Chen D, Lv H. 2022. Smart city construction and management by digital twins and BIM big data in COVID-19 scenario. ACM Trans Internet Technol, 18(2s). https://doi.org/10.1145/3529395.
  • Martínez-Cuevas C, Torres S, Pedrote Sanz M. 2024.. A digital twin of a university campus from an urban sustainability approach: Case study in Madrid (Spain). Urban Sci, 8(4): 167. https://doi.org/10.3390/urbansci8040167.
  • Megahed NA, Ghoneim EM. 2020. Antivirus-built environment: Lessons learned from COVID-19 pandemic. Sustain Cities Soc, 61: 102350. https://doi.org/10.1016/j.scs.2020.102350.
  • Megahed NA, Ghoneim EM. 2021. Indoor air quality: Rethinking rules of building design strategies in post-pandemic architecture. Environ Res, 193: 110471. https://doi.org/10.1016/j.envres.2020.110471.
  • Megahed NA, Hassan AM. Evolution of BIM to DTs: A paradigm shift for the post-pandemic AECO industry. Urban Sci 6(4): 67. https://doi.org/10.3390/urbansci6040067.
  • Microsoft Corporation, Microsoft Power BI [Computer software]. Microsoft. https://powerbi.microsoft.com.
  • Morawska L, Tang J, Bahnfleth W, Bluyssen P, Boerstra A, Buonanno G, et al. How can airborne transmission of COVID-19 indoors be minimised? Environ Int 142: 105832. https://doi.org/10.1016/j.envint.2020.105832.
  • Mouratidis K, Yiannakou A. COVID-19 and urban planning: Built environment, health, and wellbeing in Greek cities before and during the pandemic. Cities 121: 103491. https://doi.org/10.1016/j.cities.2021.103491.
  • Naeem G, Asif M, Khalid M. Industry 4.0 digital technologies for the advancement of renewable energy: Functions, applications, potential and challenges. Energy Convers Manag X 24: 100779. https://doi.org/10.1016/j.ecmx.2024.100779.
  • Nakicenovic N, Messner D, Zimm C, Clarke G, Rockström J, Aguiar AP, Boza-Kiss B, Campagnolo L, Chabay I, Collste D, et al. The digital revolution and sustainable development: Opportunities and challenges. World in 2050 Initiative, Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria, pp:25.
  • Oguntona OA, Akinradewo OI. Hindrances to the utilisation of the metaverse for net-zero buildings in South Africa. Infrastructures 10(2): 46. https://doi.org/10.3390/infrastructures10020046.
  • Oulefki A, Kheddar H, Amira A, Kurugollu F, Himeur Y. Innovative AI strategies for enhancing smart building operations through digital twins: A survey. SSRN. https://doi.org/10.2139/ssrn.5015571.
  • Pamidimukkala A, Kermanshachi S, Adepu N. Identifying critical factors that affected construction project performance during COVID-19. Int J Build Pathol Adapt, https://doi.org/10.1108/IJBPA-09-2024-0187.
  • Parracho DFR, Nour El-Din M, Esmaeili I, Freitas SS, Rodrigues L, Poças Martins J, Corvacho H, Delgado JMPQ, Guimarães AS. Modular construction in the digital age: A systematic review on smart and sustainable innovations. Buildings 15(5): 765. https://doi.org/10.3390/buildings15050765.
  • Pham VB, Wong P, Abbasnejad B. A systematic review of criteria influencing the integration of BIM and immersive technology in building projects. ITcon 30: 243–297. https://doi.org/10.36680/j.itcon.2025.011.
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Technological Advances in AEC: AI, Machine Learning, BIM, and the Future of Sustainable Building Design in a Post-Pandemic World

Yıl 2025, Cilt: 8 Sayı: 3, 757 - 774, 15.05.2025
https://doi.org/10.34248/bsengineering.1603629

Öz

The Architecture, Engineering, and Construction (AEC) industry has experienced a profound transformation, accelerated by the COVID-19 pandemic, which underscored the need for digital solutions to enhance sustainability and resilience. Despite existing technological advancements, the pandemic revealed gaps in the widespread adoption of tools like Artificial Intelligence (AI), Machine Learning (ML), Building Information Modeling (BIM), Internet of Things (IoT), and Digital Twins (DT). This study addresses the critical question of how these technologies have reshaped building design, construction, and operation processes to meet sustainability goals in the post-pandemic era. A systematic literature review of 35 peer-reviewed studies published between 2019 and 2024 was conducted to analyze the impact of key digital technologies on sustainable building practices. The research employed a thematic analysis focusing on technological advancements, sustainability applications, challenges and barriers, and emerging trends such as smart cities, renewable energy integration, and circular economy principles. The findings reveal that technologies like AI and DTs play a pivotal role in enhancing energy efficiency, enabling predictive maintenance, and improving lifecycle resource management. However, barriers such as interoperability issues, high implementation costs, and data security concerns persist, hindering widespread adoption. The study emphasizes the growing trend toward data-driven sustainability and the need to address these challenges through collaborative frameworks and technological innovation. In conclusion, this research highlights the transformative potential of digital technologies in advancing sustainability and resilience within the AEC industry. By bridging the gap between technological innovation and sustainable development goals, this study provides actionable insights for overcoming existing barriers and fostering adaptive, energy-efficient, and environmentally responsible built environments in a post-pandemic world.

Kaynakça

  • Adewale BA, Ene VO, Ogunbayo BF, Aigbavboa CO. 2024. A systematic review of the applications of AI in a sustainable building’s lifecycle. Buildings, 14(7): 2137. https://doi.org/10.3390/buildings14072137.
  • Alizadehsalehi S, Hadavi A, Huang JC. 2020. From BIM to extended reality in AEC industry. Autom. Constr., 116: 103254. https://doi.org/10.1016/j.autcon.2020.103254.
  • Amoatey P, Omidvarborna H, Baawain M, Al-Mamun A. 2020. Impact of building ventilation systems and habitual indoor incense burning on SARS-CoV-2 virus transmissions in Middle Eastern countries. Sci. Total Environ., 733: 139356. https://doi.org/10.1016/j.scitotenv.2020.139356.
  • Aniekan Akpan Umoh CN, Nwasike OAT, Adekoya O O, Gidiagba JO. 2024. A review of smart green building technologies: Investigating the integration and impact of AI and IoT in sustainable building designs. Comput. Sci. IT Res. J., 5(1): 141–165. https://doi.org/10.51594/csitrj.v5i1.715.
  • Arowoiya VA, Moehler RC, Fang Y. 2024. Digital twin technology for thermal comfort and energy efficiency in buildings: A state-of-the-art and future directions. Energy Built Environ., 5(5): 641–656. https://doi.org/10.1016/j.enbenv.2023.05.004.
  • Arsecularatne B, Rodrigo N, Chang R. 2024. Digital twins for reducing energy consumption in buildings: A review. Sustainability, 16(21): 9275. https://doi.org/10.3390/su16219275.
  • Asif M, Naeem G, Khalid M. 2024. Digitalization for sustainable buildings: Technologies, applications, potential, and challenges. J. Clean. Prod., in press.
  • Badenko V, Bolshakov N, Celani A, Puglisi V. 2024. Principles for sustainable integration of BIM and digital twin technologies in industrial infrastructure. Sustainability, 16(22): 9885. https://doi.org/10.3390/su16229885.
  • Banai R. 2020. Pandemic and the planning of resilient cities and regions. Cities, 106: 102929. https://doi.org/10.1016/j.cities.2020.102929.
  • Bibri SE, Huang J, Jagatheesaperumal SK, Krogstie J. 2024. The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review. Environ. Sci. Ecotechnol., 20: 100433. https://doi.org/10.1016/j.ese.2024.100433.
  • Chen X, Chang-Richards AY, Ling FYY, Yiu TW, Pelosi A, Yang N. 2024. Digital technology-enabled AEC project management: Practical use cases, deployment patterns and emerging trends. Eng Constr Archit Manag, online publication. https://doi.org/10.1108/ECAM-09-2023-0962.
  • Cheshmehzangi A. 2021. Revisiting the built environment: 10 potential development changes and paradigm shifts due to COVID-19. J Urban Manag, 10: 166–175. https://doi.org/10.1016/j.jum.2021.01.002.
  • Coraglia UM, Simeone D, Bragadin MA. 2024. Research perspectives on buildings’ sustainability after COVID-19: Literature review and analysis of changes. Buildings, 14(2): 482. https://doi.org/10.3390/buildings14020482.
  • Darko A, Chan APC, Adabre MA, Edwards DJ, Hosseini MR, Ameyaw EE. 2020. Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities. Autom Constr, 112: 103081. https://doi.org/10.1016/j.autcon.2020.103081.
  • De Las Heras et al. 2020. Machine learning technologies for sustainability in smart cities in the post-COVID era. Sustainability, 12(22): 9320. https://doi.org/10.3390/su12229320.
  • Elavarasan R, Pugazhendhi R. 2020. Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic. Sci. Total Environ., 725: 138858. https://doi.org/10.1016/j.scitotenv.2020.138858.
  • 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. https://doi.org/10.3390/buildings12040489.
  • Ferdaus MM, Dam T, Anavatti S, Das S. 2024. Digital technologies for a net-zero energy future: A comprehensive review. Renew Sustain Energy Rev, 202: 114681. https://doi.org/10.1016/j.rser.2024.114681.
  • Gorina L, Korneeva E, Kovaleva O, Strielkowski W. 2024. Energy-saving technologies and energy efficiency in the post-COVID era. Sustain Dev, 32(5): 5294–5310. https://doi.org/10.1002/sd.2978.
  • Gurram MK, Wang MX, Wang YC, Pang J. 2022. Impact of urbanization and environmental factors on spatial distribution of COVID-19 cases during the early phase of epidemic in Singapore. Sci Rep, 12: 9758. https://doi.org/10.1038/s41598-022-12941-8.
  • Han P, Wang L, Song Y, Zheng X. 2022. Designing for the post-pandemic era: Trends, focuses, and strategies learned from architectural competitions based on a text analysis. Front Public Health, 10: 1084562. https://doi.org/10.3389/fpubh.2022.1084562.
  • Inusah Y, Kazaz A, Ulubeyli S. 2025. Barriers to e-tendering implementation in the construction industry: A comprehensive review and analysis of a decade and beyond. Sustainability, 17(5): 2052. https://doi.org/10.3390/su17052052.
  • Jogunola O, Morley C, Akpan IJ, Tsado Y, Adebisi B, Yao L. 2022. Energy consumption in commercial buildings in a post-COVID-19 world. IEEE Eng Manag Rev, 50(1): 54–64. https://doi.org/10.1109/EMR.2022.3146591.
  • Karatzas S, Papageorgiou G, Lazari V, Bersimis S, Fousteris A, Economou P, Chassiakos A. 2024. A text analytic framework for gaining insights on the integration of digital twins and machine learning for optimizing indoor building environmental performance. Dev Built Environ, 18: 100386. https://doi.org/10.1016/j.dibe.2024.100386.
  • Kazeem KO, Olawumi TO, Osunsanmi T. 2023. Roles of artificial intelligence and machine learning in enhancing construction processes and sustainable communities. Buildings, 13(8): 2061. https://doi.org/10.3390/buildings13082061.
  • Kozlovska M, Petkanic S, Vranay F, Vranay D. 2023. Enhancing energy efficiency and building performance through BEMS-BIM integration. Energies, 16(17): 6327. https://doi.org/10.3390/en16176327.
  • Lv Z, Chen D, Lv H. 2022. Smart city construction and management by digital twins and BIM big data in COVID-19 scenario. ACM Trans Internet Technol, 18(2s). https://doi.org/10.1145/3529395.
  • Martínez-Cuevas C, Torres S, Pedrote Sanz M. 2024.. A digital twin of a university campus from an urban sustainability approach: Case study in Madrid (Spain). Urban Sci, 8(4): 167. https://doi.org/10.3390/urbansci8040167.
  • Megahed NA, Ghoneim EM. 2020. Antivirus-built environment: Lessons learned from COVID-19 pandemic. Sustain Cities Soc, 61: 102350. https://doi.org/10.1016/j.scs.2020.102350.
  • Megahed NA, Ghoneim EM. 2021. Indoor air quality: Rethinking rules of building design strategies in post-pandemic architecture. Environ Res, 193: 110471. https://doi.org/10.1016/j.envres.2020.110471.
  • Megahed NA, Hassan AM. Evolution of BIM to DTs: A paradigm shift for the post-pandemic AECO industry. Urban Sci 6(4): 67. https://doi.org/10.3390/urbansci6040067.
  • Microsoft Corporation, Microsoft Power BI [Computer software]. Microsoft. https://powerbi.microsoft.com.
  • Morawska L, Tang J, Bahnfleth W, Bluyssen P, Boerstra A, Buonanno G, et al. How can airborne transmission of COVID-19 indoors be minimised? Environ Int 142: 105832. https://doi.org/10.1016/j.envint.2020.105832.
  • Mouratidis K, Yiannakou A. COVID-19 and urban planning: Built environment, health, and wellbeing in Greek cities before and during the pandemic. Cities 121: 103491. https://doi.org/10.1016/j.cities.2021.103491.
  • Naeem G, Asif M, Khalid M. Industry 4.0 digital technologies for the advancement of renewable energy: Functions, applications, potential and challenges. Energy Convers Manag X 24: 100779. https://doi.org/10.1016/j.ecmx.2024.100779.
  • Nakicenovic N, Messner D, Zimm C, Clarke G, Rockström J, Aguiar AP, Boza-Kiss B, Campagnolo L, Chabay I, Collste D, et al. The digital revolution and sustainable development: Opportunities and challenges. World in 2050 Initiative, Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria, pp:25.
  • Oguntona OA, Akinradewo OI. Hindrances to the utilisation of the metaverse for net-zero buildings in South Africa. Infrastructures 10(2): 46. https://doi.org/10.3390/infrastructures10020046.
  • Oulefki A, Kheddar H, Amira A, Kurugollu F, Himeur Y. Innovative AI strategies for enhancing smart building operations through digital twins: A survey. SSRN. https://doi.org/10.2139/ssrn.5015571.
  • Pamidimukkala A, Kermanshachi S, Adepu N. Identifying critical factors that affected construction project performance during COVID-19. Int J Build Pathol Adapt, https://doi.org/10.1108/IJBPA-09-2024-0187.
  • Parracho DFR, Nour El-Din M, Esmaeili I, Freitas SS, Rodrigues L, Poças Martins J, Corvacho H, Delgado JMPQ, Guimarães AS. Modular construction in the digital age: A systematic review on smart and sustainable innovations. Buildings 15(5): 765. https://doi.org/10.3390/buildings15050765.
  • Pham VB, Wong P, Abbasnejad B. A systematic review of criteria influencing the integration of BIM and immersive technology in building projects. ITcon 30: 243–297. https://doi.org/10.36680/j.itcon.2025.011.
  • Rampini L, Cecconi FR. Artificial intelligence in construction asset management: A review of present status, challenges and future opportunities. ITcon 27: 884–913. https://doi.org/10.36680/j.itcon.2022.043.
  • Rathore MM, Shah SA, Shukla D, Bentafat E, Bakiras S. The role of AI, machine learning, and big data in digital twinning: A systematic literature review, challenges, and opportunities. IEEE Access 9: 32030–32052. https://doi.org/10.1109/ACCESS.2021.3060863.
  • Renganayagalu SK, Bodal T, Bryntesen TR, Kvalvik P. Optimising energy performance of buildings through digital twins and machine learning: Lessons learnt and future directions. Proc 2024 4th Int Conf Appl Artif Intell (ICAPAI): 1–6. https://doi.org/10.1109/ICAPAI61893.2024.10541224.
  • Royan F. Digital sustainability: The path to net zero for design & manufacturing and architecture, engineering, & construction (AEC) industries. Analysis of the market in UK & Ireland, Nordics and Benelux. Frost & Sullivan, London ,UK, pp:125.
  • Selcuk E. Impact of the COVID-19 pandemic on construction project performance: Challenges and strategic responses. J Asian Archit Build Eng. https://doi.org/10.1080/13467581.2025.2457385.
  • Sepasgozar SME, Khan AA, Smith K, Romero JG, Shen X, Shirowzhan S, Li H, Tahmasebinia F. BIM and digital twin for developing convergence technologies as future of digital construction. Buildings 13(2): 441. https://doi.org/10.3390/buildings13020441.
  • Shakil MH, Munim ZH, Tasnia M, Sarowar S. COVID-19 and the environment: A critical review and research agenda. Sci Total Environ 141022. https://doi.org/10.1016/j.scitotenv.2020.141022.
  • Strielkowski W, Zenchenko S, Tarasova A, Radyukova Y. Management of smart and sustainable cities in the post-COVID-19 era: Lessons and implications. Sustainability 14(12): 7267. https://doi.org/10.3390/su14127267.
  • Tahmasebinia F, Lin L, Wu S, Kang Y, Sepasgozar S. Exploring the benefits and limitations of digital twin technology in building energy. Appl Sci 13(15): 8814. https://doi.org/10.3390/app13158814.
  • Tang S, Shelden DR, Eastman CM, Pishdad-Bozorgi P, Gao X. A review of building information modeling (BIM) and the internet of things (IoT) devices integration: Present status and future trends. Autom Constr 101: 127–139. https://doi.org/10.1016/j.autcon.2019.01.020.
  • Tokazhanov G, Tleuken A, Guney M, Turkyilmaz A, Karaca F. How is COVID-19 experience transforming sustainability requirements of residential buildings? A review. Sustainability 12(20): 8732. https://doi.org/10.3390/su12208732.
  • Villano F, Mauro GM, Pedace A. A review on machine/deep learning techniques applied to building energy simulation, optimization and management. Thermo 4(1): 100–139. https://doi.org/10.3390/thermo4010008.
  • Wang Q, Li S, Jiang F. Uncovering the impact of the COVID-19 pandemic on energy consumption: New insight from difference between pandemic-free scenario and actual electricity consumption in China. J Clea Prod 313: 127897. https://doi.org/10.1016/j.jclepro.2021.127897.
  • Wang W, Gao S, Mi L, Xing J, Shang K, Qiao Y, Xu N. Exploring the adoption of BIM amidst the COVID-19 crisis in China. Build Res Inf 49(8): 930–947. https://doi.org/10.1080/09613218.2021.1921565.
  • Xie X, Ramakrishna S, Manganelli M. Smart building technologies in response to COVID-19. Energies 15(15): 5488. https://doi.org/10.3390/en15155488.
  • Yang B, Lv Z, Wang F. Digital twins for intelligent green buildings. Buildings 12(6): 856. https://doi.org/10.3390/buildings12060856.
  • Yap JBH, Chow IN, Shavarebi K. Criticality of construction industry problems in developing countries: Analyzing Malaysian projects. J Manag Eng 35(5): 04019020. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000709.
  • Yoon S, Lee J, Li J, Wang P. Virtual in-situ modeling between digital twin and BIM for advanced building operations and maintenance. Autom Constr 168: 105823. https://doi.org/10.1016/j.autcon.2024.105823..
  • Zahedi F, Alavi H, Majrouhi Sardroud J, Dang H. Digital twins in the sustainable construction industry. Buildings 14: Article 11. https://doi.org/10.3390/buildings14113613
  • Zhang X, Shen J, Saini PK, Lovati M, Han M, Huang P, Huang Z. Digital twin for accelerating sustainability in positive energy district: A review of simulation tools and applications. Front Sustain Cities 3: 35. https://doi.org/10.3389/frsc.2021.663269.
  • Zhang Z, Wei Z, Court S, Yang L, Wang S. Thirunavukarasu A, Zhao Y, A review of digital twin technologies for enhanced sustainability in the construction industry. Buildings 14(4): 1113. https://doi.org/10.3390/buildings14041113.
  • Zhou Y, Liu J. Advances in emerging digital technologies for energy efficiency and energy integration in smart cities. Energy Build 315: 114289. https://doi.org/10.1016/j.enbuild.2024.114289.
Toplam 63 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Görsel İletişim Tasarımı (Diğer), Mimari Mühendislik
Bölüm Research Articles
Yazarlar

Aslıhan Şenel Solmaz 0000-0002-1018-4769

Yayımlanma Tarihi 15 Mayıs 2025
Gönderilme Tarihi 18 Aralık 2024
Kabul Tarihi 26 Mart 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 8 Sayı: 3

Kaynak Göster

APA Şenel Solmaz, A. (2025). Technological Advances in AEC: AI, Machine Learning, BIM, and the Future of Sustainable Building Design in a Post-Pandemic World. Black Sea Journal of Engineering and Science, 8(3), 757-774. https://doi.org/10.34248/bsengineering.1603629
AMA Şenel Solmaz A. Technological Advances in AEC: AI, Machine Learning, BIM, and the Future of Sustainable Building Design in a Post-Pandemic World. BSJ Eng. Sci. Mayıs 2025;8(3):757-774. doi:10.34248/bsengineering.1603629
Chicago Şenel Solmaz, Aslıhan. “Technological Advances in AEC: AI, Machine Learning, BIM, and the Future of Sustainable Building Design in a Post-Pandemic World”. Black Sea Journal of Engineering and Science 8, sy. 3 (Mayıs 2025): 757-74. https://doi.org/10.34248/bsengineering.1603629.
EndNote Şenel Solmaz A (01 Mayıs 2025) Technological Advances in AEC: AI, Machine Learning, BIM, and the Future of Sustainable Building Design in a Post-Pandemic World. Black Sea Journal of Engineering and Science 8 3 757–774.
IEEE A. Şenel Solmaz, “Technological Advances in AEC: AI, Machine Learning, BIM, and the Future of Sustainable Building Design in a Post-Pandemic World”, BSJ Eng. Sci., c. 8, sy. 3, ss. 757–774, 2025, doi: 10.34248/bsengineering.1603629.
ISNAD Şenel Solmaz, Aslıhan. “Technological Advances in AEC: AI, Machine Learning, BIM, and the Future of Sustainable Building Design in a Post-Pandemic World”. Black Sea Journal of Engineering and Science 8/3 (Mayıs2025), 757-774. https://doi.org/10.34248/bsengineering.1603629.
JAMA Şenel Solmaz A. Technological Advances in AEC: AI, Machine Learning, BIM, and the Future of Sustainable Building Design in a Post-Pandemic World. BSJ Eng. Sci. 2025;8:757–774.
MLA Şenel Solmaz, Aslıhan. “Technological Advances in AEC: AI, Machine Learning, BIM, and the Future of Sustainable Building Design in a Post-Pandemic World”. Black Sea Journal of Engineering and Science, c. 8, sy. 3, 2025, ss. 757-74, doi:10.34248/bsengineering.1603629.
Vancouver Şenel Solmaz A. Technological Advances in AEC: AI, Machine Learning, BIM, and the Future of Sustainable Building Design in a Post-Pandemic World. BSJ Eng. Sci. 2025;8(3):757-74.

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