CONSTRUCTA : Automating Commercial Construction Schedules in Fabrication Facilities with Large Language Models (2025)

Abstract: Automating planning with LLMs presents transformative opportunities for traditional industries, yet remains underexplored. In commercial construction, the complexity of automated scheduling often requires manual intervention to ensure precision. We propose CONSTRUCTA, a novel framework leveraging LLMs to optimize construction schedules in complex projects like semiconductor fabrication. CONSTRUCTA addresses key challenges by: (1) integrating construction-specific knowledge through static RAG; (2) employing context-sampling techniques inspired by architectural expertise to provide relevant input; and (3) deploying Construction DPO to align schedules with expert preferences using RLHF. Experiments on proprietary data demonstrate performance improvements of +42.3% in missing value prediction, +79.1% in dependency analysis, and +28.9% in automated planning compared to baseline methods, showcasing its potential to revolutionize construction workflows and inspire domain-specific LLM advancements.

## introduction automating construction schedules in large-scale commercial projects, such as semiconductor fabrication, is an inherently complex task due to the dynamic nature of project contexts, intricate dependency structures, and the critical need for expertdriven decision-making @xcite . the difficulty lies in managing the vast number of interdependent activities, each with unique resource requirements and constraints, while simultaneously adapting to real-time changes and unforeseen disruptions @xcite . these factors necessitate seamless † work done during a genai research internship at intel incubation and disruptive innovation (idi) group. integration of domain knowledge and human expertise to ensure project feasibility and efficiency. traditional methods, relying on rigid rules and static assumptions, often fail to adapt to the variability and uncertainty inherent in large-scale construction projects, leaving a critical need for more flexible and context-aware approaches @xcite . despite recent advancements in machine learning, the potential of large language models (llms) for construction scheduling remains underexplored due to several limitations. llms, pretrained on broad datasets, lack the domain-specific knowledge needed for intricate project dependencies and constraints @xcite . moreover, the size and complexity of construction plans make it impractical to load entire projects into llms for automation @xcite . instead, construction scheduling demands dynamic handling of real-time updates and evolving conditions. llms face three key challenges: (1) capturingthe intricate dependencies between construction activities, (2) adapting to context-sensitive changes in task priorities or resource availability, and (3) aligning outputs with expert-driven preferences. these challenges highlight the need for tailored frameworks to bridge the gap between llm capabilities and the demands of large-scale construction projects. to address these limitations, we present con-structa 1 , a novel framework designed to optimize construction schedules dynamically by leveraging llms with three key components: (1) static retrieval-augmented generation (srag or static rag), which introduces domain-specific construction knowledge, enabling llms to understand definitions, rules, and constraints critical to commercial construction; (2) contextualized knowledge rag (knowledge rag or krag), which incorporates the expertise of architects by dynamically sampling context-sensitive information, ensuring the relevance of inputs to evolving project conditions; and (3) construction rlhf, which aligns the outputs of llms with expert feedback to enhance their in-depth understanding and produce human-aligned scheduling decisions. we evaluate constructa on a proprietary dataset comprising 4,340 semiconductor fabrication activities characterized by intricate dependencies and constraints. constructa delivers substantial performance improvements, including a 42.3% boost in missing value prediction, 79.1% in dependency analysis, and 28.9% in automated planning compared to baseline methods. further analysis across levels and areas shows adaptability, while construction rlhf distills raw data into actionable insights, demonstrating scalability and robustness for complex construction tasks. ## methodology our methodology starts with an expert-provided schedule (figure 1 , part (a)) and refines it using static rag for retrieval, knowledge rag for dependencies, and construction rlhf for rule alignment (parts (c), (b), and (d)). the outputs, including retrieved knowledge and preference-aligned task relationships, are integrated into prompts for dynamic, context-aware scheduling. ## experimental design this section outlines the experimental configurations for static rag, knowledge rag and construction rlhf, emphasizing embedding methods, model configurations, and optimization strategies. static and knowledge rag the srag setup used 500-token chunks for efficient processing, with embeddings generated via all-minilm-l6-v2 3 . static rag focused on terminologies and definitions, while knowledge rag retrieved context from manuals and domainspecific references. construction rlhf the plan agent used gpt-4o @xcite , and the expert agent employed llama3.2-3b model @xcite for expert preference alignment. training involved 10 epochs of sft for initialization, followed by 10 epochs of cpa-dpo for preference refinement. the trained expert agent supported contextual refinements. ## result and analysis we evaluate constructa across key scheduling tasks, highlighting its ability to address complex dependencies, handle missing data, and align schedules with expert-defined constraints. ## future applications and industry adoption constructa presents strong potential for llm adoption in construction scheduling, improving automation, adaptability, and decision support. traditional methods struggle with real-time changes, while constructa continuously refines schedules based on evolving constraints @xcite . by learning from historical schedules and domain-specific constraints, it optimizes resource allocation, mitigates conflicts, and enhances project execution. for broader adoption, constructa can integrate with existing construction management software as an intelligent planning tool. its ability to handle dynamic scheduling and dependency modeling makes it valuable for large-scale projects. future work will address deployment challenges, including computational efficiency, latency, and seamless integration with industry platforms @xcite , ensuring scalability for commercial applications such as semiconductor fabrication. ## related works research on llm-powered construction scheduling is limited, with prior work focusing on deterministic methods and rl in other domains @xcite @xcite @xcite . this work pioneers construction automation using rag and rlhf. ## conclusion in conclusion, we presented constructa, an approach for automating construction schedules by integrating llms, contextualized knowledge rag, and rlhf to optimize workflows with expert input. this framework advances traditional methods, offering flexibility, scalability, and adaptability for large-scale projects with complex dependencies. future work includes implementing the construction dpo model, incorporating multimodal inputs, and evolving constructa into a dynamic recommender system for continuous project adaptation.