[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-664186-105":59,"doc-detail-664186-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","transforming-construction-practices-with-large-language-models-664186","Transforming Construction Practices with Large Language Models","","The paper investigates diverse applications of Large Language Models (LLMs) in the construction industry, focusing on how they support human decision-making, increase efficiency, and improve accuracy across complex workflows. It examines LLM capabilities for document analysis and quality control, data processing and information retrieval, content creation and management, and interactive customized system development. Real-world examples demonstrate how these systems drive innovation and operational gains.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/transforming-construction-practices-with-large-language-models-664186/664186/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/transforming-construction-practices-with-large-language-models-664186/664186.png","ImageObject",300,407,{"name":92,"@type":93},"Jiven","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-29",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How do large language models support construction decision-making?","Question",{"text":112,"@type":113},"The paper explains that LLMs enable contextual understanding and generation, which streamlines information processing and improves decision-making in tasks like document analysis and quality control.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What are key application areas of LLMs in construction discussed in the paper?",{"text":117,"@type":113},"It covers document analysis and quality control, data processing and information retrieval, content creation and management, and the development of interactive and customized systems.",{"name":119,"@type":110,"acceptedAnswer":120},"What distinguishes in-context learning, instruction following, and step-by-step reasoning in LLM research?",{"text":121,"@type":113},"The text states that in-context learning uses prompt context without additional specialized training, instruction following relies on fine-tuning to follow complex directives, and step-by-step reasoning uses chain-of-thought prompting for multi-step logic.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},664186,1793242626,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":14,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":143},1099513958607,"https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399","Transforming Construction Practices with Large  \nLanguage Models  \nEhsan Ahmadi, Ph.D. Student, and Chao Wang, Ph.D.  \nBert S. Turner Department of Construction Management  \nLouisiana State University  \nBaton Rouge, Louisiana  \nThis paper investigates the diverse applications of Large Language Models (LLMs) in the construction industry, emphasizing their transformative role in supporting human decision-making, enhancing efficiency, and improving accuracy in various tasks. The study explores LLMs'capabilities in key areas such as document analysis and quality control, data processing and information retrieval, content creation and management, and the development of interactive and customized systems. Through real-world examples, we illustrate how LLMs are driving innovation and progress in construction. These applications showcase LLMs' effectiveness in elevating operational efficiency, from nuanced document analysis to the advancement of interactive system development, fundamentally transforming the approach to construction management.  \nKey Words: Large Language Models, Generative AI, Natural Language Processing, Automation in Construction  \nIntroduction  \nIn the realm of construction, an industry often characterized by complex projects and intricate workflows, the introduction of Large Language Models (LLMs) marks a significant shift towards digitalization and smart technology integration. These models, residing at the intersection of natural language processing and generative AI, aim to emulate human capabilities in understanding and generating language. Generative AI developers strive to create models that can autonomously generate content, particularly in applications like chatbots, constituting what is known as ‘natural language generation’ tasks (Khurana, Koli, Khatter, & Singh, 2023). After years of extensive development, leveraging vast datasets and powerful computational resources, LLMs have evolved significantly, now exhibiting new capabilities like in-context learning (Brown et al., 2020) . This advancement enables them to generate language in a contextually aware and human-like manner, paving the way for significant innovations across various industries.  \nThese advancements in LLMs are particularly impactful in the construction industry, a sector traditionally reliant on manual processes and extensive documentation. The integration of LLMs  \npromises not only to automate and streamline these processes but also to bring a level of analytical depth and precision previously unattainable. The contextual language understanding and generation capabilities of LLMs are revolutionizing information processing and communication in construction projects. Their use in document analysis, quality control, data processing, and document creation streamlines complex tasks and enhances decision-making. This paper explores these applications, employing real-world examples and case studies to demonstrate the transformative impact of LLMs in construction.  \nBackground  \nLarge Language Models  \nLarge language models (LLMs) are models based on transformer architectures that contain hundreds of billions of parameters. These models are pre-trained on vast corpora, often consisting of hundreds of terabytes of textual data (Shanahan, 2023). Notable examples such as GPT (Brown et al., 2020), PaLM (Chowdhery et al., 2022), Claude (Anthropic, 2023), and LLaMA (Touvron et al., 2023) mark significant advancements in natural language processing. LLMs demonstrate profound capabilities in understanding and generating text, managing complex tasks that were traditionally the domain of human intelligence.  \nRecent research underscores the advanced capabilities of large language models, particularly in areas like in-context learning, instruction following, and step-by-step reasoning (Wei et al., 2022; Zhao et al., 2023). These capabilities distinguish LLMs from their predecessors by enabling them to perform complex tasks more effectively. In-context Le","cbCaiu6dQGse4nwC","https://ap.wps.com/l/cbCaiu6dQGse4nwC","pdf",470272,"English","# Introduction\n# Background\n## Large Language Models\n## Current Research and Developments on LLMs in Construction","[{\"question\":\"How do large language models support construction decision-making?\",\"answer\":\"The paper explains that LLMs enable contextual understanding and generation, which streamlines information processing and improves decision-making in tasks like document analysis and quality control.\"},{\"question\":\"What are key application areas of LLMs in construction discussed in the paper?\",\"answer\":\"It covers document analysis and quality control, data processing and information retrieval, content creation and management, and the development of interactive and customized systems.\"},{\"question\":\"What distinguishes in-context learning, instruction following, and step-by-step reasoning in LLM research?\",\"answer\":\"The text states that in-context learning uses prompt context without additional specialized training, instruction following relies on fine-tuning to follow complex directives, and step-by-step reasoning uses chain-of-thought prompting for multi-step logic.\"}]","Transforming Construction Practices with Large Language Models | PDF",23]