Dimitrios Kampelopoulos, Athina Tsanousa, Stefanos Vrochidis, Ioannis Kompatsiaris
{"title":"A review of LLMs and their applications in the architecture, engineering and construction industry","authors":"Dimitrios Kampelopoulos, Athina Tsanousa, Stefanos Vrochidis, Ioannis Kompatsiaris","doi":"10.1007/s10462-025-11241-7","DOIUrl":null,"url":null,"abstract":"<div><p>During the past decade, there has been rapid emergence, continuous development and advancements in the field of Artificial Intelligence (AI), and a broad adaptation ofLarge Language Models (LLMs) in a wide variety of application domains transforming and streamlining industry practices. However, the construction industry has yet to fully incorporate these technologies, delaying their wide-scale adaptation. Only a limited number of recent studies have explored the opportunities, capabilities and potential of current LLM implementations in the broad domain of Architecture Engineering and Construction (AEC) industry, leaving a significant gap in this field of research. This study aims to address this gap and provide an extensive review of already established state-of-the-art applications and use case scenarios of LLMs in the AEC industry. Apart from that, by exploring the key contributions and limitations of these applications, and by considering relative reviews on this subject, it was possible to categorize them, to extract the emerging challenges and future directions of the field and propose actionable recommendations for industry stakeholders. This study also includes an introduction to important concepts and recent advancements of LLM technologies, focusing on transformer-based architectures and providing an extensive list of LLM families.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 8","pages":""},"PeriodicalIF":10.7000,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11241-7.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Intelligence Review","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s10462-025-11241-7","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
During the past decade, there has been rapid emergence, continuous development and advancements in the field of Artificial Intelligence (AI), and a broad adaptation ofLarge Language Models (LLMs) in a wide variety of application domains transforming and streamlining industry practices. However, the construction industry has yet to fully incorporate these technologies, delaying their wide-scale adaptation. Only a limited number of recent studies have explored the opportunities, capabilities and potential of current LLM implementations in the broad domain of Architecture Engineering and Construction (AEC) industry, leaving a significant gap in this field of research. This study aims to address this gap and provide an extensive review of already established state-of-the-art applications and use case scenarios of LLMs in the AEC industry. Apart from that, by exploring the key contributions and limitations of these applications, and by considering relative reviews on this subject, it was possible to categorize them, to extract the emerging challenges and future directions of the field and propose actionable recommendations for industry stakeholders. This study also includes an introduction to important concepts and recent advancements of LLM technologies, focusing on transformer-based architectures and providing an extensive list of LLM families.
期刊介绍:
Artificial Intelligence Review, a fully open access journal, publishes cutting-edge research in artificial intelligence and cognitive science. It features critical evaluations of applications, techniques, and algorithms, providing a platform for both researchers and application developers. The journal includes refereed survey and tutorial articles, along with reviews and commentary on significant developments in the field.