Large Language Model-Driven Code Compliance Checking in Building Information Modeling
- URL: http://arxiv.org/abs/2506.20551v1
- Date: Wed, 25 Jun 2025 15:50:34 GMT
- Title: Large Language Model-Driven Code Compliance Checking in Building Information Modeling
- Authors: Soumya Madireddy, Lu Gao, Zia Din, Kinam Kim, Ahmed Senouci, Zhe Han, Yunpeng Zhang,
- Abstract summary: This research addresses the time-consuming and error-prone nature of manual code compliance checking in Building Information Modeling.<n>It introduces a Large Language Model (LLM)-driven approach to semi-automate this critical process.<n>The developed system integrates LLMs such as GPT, Claude, Gemini, and Llama, with Revit software to interpret building codes, generate Python scripts, and perform semi-automated compliance checks.
- Score: 3.2648052741820166
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This research addresses the time-consuming and error-prone nature of manual code compliance checking in Building Information Modeling (BIM) by introducing a Large Language Model (LLM)-driven approach to semi-automate this critical process. The developed system integrates LLMs such as GPT, Claude, Gemini, and Llama, with Revit software to interpret building codes, generate Python scripts, and perform semi-automated compliance checks within the BIM environment. Case studies on a single-family residential project and an office building project demonstrated the system's ability to reduce the time and effort required for compliance checks while improving accuracy. It streamlined the identification of violations, such as non-compliant room dimensions, material usage, and object placements, by automatically assessing relationships and generating actionable reports. Compared to manual methods, the system eliminated repetitive tasks, simplified complex regulations, and ensured reliable adherence to standards. By offering a comprehensive, adaptable, and cost-effective solution, this proposed approach offers a promising advancement in BIM-based compliance checking, with potential applications across diverse regulatory documents in construction projects.
Related papers
- Monadic Context Engineering [59.95390010097654]
This paper introduces Monadic Context Engineering (MCE) to provide a formal foundation for agent design.<n>We demonstrate how Monads enable robust composition, how Applicatives provide a principled structure for parallel execution, and crucially, how Monad Transformers allow for the systematic composition of these capabilities.<n>This layered approach enables developers to construct complex, resilient, and efficient AI agents from simple, independently verifiable components.
arXiv Detail & Related papers (2025-12-27T01:52:06Z) - Generation of Programmatic Rules for Document Forgery Detection Using Large Language Models [10.32461766065764]
Document forgery poses a growing threat to legal, economic, and governmental processes.<n>Existing plausibility checks are manually implemented by software engineers, which is time-consuming.<n>Recent advances in code generation with large language models (LLMs) offer new potential for automating and scaling the generation of these checks.
arXiv Detail & Related papers (2025-12-22T10:08:25Z) - A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System [56.40989626804489]
This survey provides the first holistic analysis of Large Language Models-powered software engineering.<n>We review over 150 recent papers and propose a taxonomy along two key dimensions: (1) Solutions, categorized into prompt-based, fine-tuning-based, and agent-based paradigms, and (2) Benchmarks, including tasks such as code generation, translation, and repair.
arXiv Detail & Related papers (2025-10-10T06:56:50Z) - Automatic Building Code Review: A Case Study [6.530899637501737]
Building officials face labor-intensive, error-prone, and costly manual reviews of design documents as projects increase in size and complexity.<n>This study introduces a novel agent-driven framework that integrates BIM-based data extraction with automated verification.
arXiv Detail & Related papers (2025-10-03T00:30:14Z) - PAT-Agent: Autoformalization for Model Checking [17.082027022913998]
PAT-Agent is an end-to-end framework for natural language autoformalization and formal model repair.<n>It combines the generative capabilities of large language models with the rigor of formal verification.
arXiv Detail & Related papers (2025-09-28T06:32:14Z) - Automated Facility Enumeration for Building Compliance Checking using Door Detection and Large Language Models [35.359387547360434]
Building compliance checking (BCC) is a critical process for ensuring that constructed facilities meet regulatory standards.<n>Despite its importance, this problem has been largely overlooked in the literature.<n>Recent advances in large language models (LLMs) offer new opportunities to enhance automation.
arXiv Detail & Related papers (2025-09-21T23:41:44Z) - CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward [50.97588334916863]
We develop CompassVerifier, an accurate and robust lightweight verifier model for evaluation and outcome reward.<n>It demonstrates multi-domain competency spanning math, knowledge, and diverse reasoning tasks, with the capability to process various answer types.<n>We introduce VerifierBench benchmark comprising model outputs collected from multiple data sources, augmented through manual analysis of metaerror patterns to enhance CompassVerifier.
arXiv Detail & Related papers (2025-08-05T17:55:24Z) - On LLM-Assisted Generation of Smart Contracts from Business Processes [0.08192907805418582]
Large language models (LLMs) have changed the reality of how software is produced.<n>We present an exploratory study to investigate the use of LLMs for generating smart contract code from business process descriptions.<n>Our results show that LLM performance falls short of the perfect reliability required for smart contract development.
arXiv Detail & Related papers (2025-07-30T20:39:45Z) - Leveraging LLMs for Formal Software Requirements -- Challenges and Prospects [0.0]
VERIFAI1 aims to investigate automated and semi-automated approaches to bridge this gap.<n>This position paper presents a preliminary synthesis of relevant literature to identify recurring challenges and prospective research directions.
arXiv Detail & Related papers (2025-07-18T19:15:50Z) - Augmenting Large Language Models with Static Code Analysis for Automated Code Quality Improvements [0.36832029288386137]
This study examined code issue detection and revision automation by integrating Large Language Models (LLMs) into software development.<n>A static code analysis framework detects issues such as bugs, vulnerabilities, and code smells within a large-scale software project.<n>Retrieval-augmented generation (RAG) is implemented to enhance the relevance and precision of the revisions.
arXiv Detail & Related papers (2025-06-12T03:39:25Z) - Training Language Models to Generate Quality Code with Program Analysis Feedback [66.0854002147103]
Code generation with large language models (LLMs) is increasingly adopted in production but fails to ensure code quality.<n>We propose REAL, a reinforcement learning framework that incentivizes LLMs to generate production-quality code.
arXiv Detail & Related papers (2025-05-28T17:57:47Z) - AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios [51.46347732659174]
Large Language Models (LLMs) have demonstrated advanced capabilities in real-world agentic applications.<n>AgentIF is the first benchmark for systematically evaluating LLM instruction following ability in agentic scenarios.
arXiv Detail & Related papers (2025-05-22T17:31:10Z) - MAS-ZERO: Designing Multi-Agent Systems with Zero Supervision [76.42361936804313]
We introduce MAS-ZERO, the first self-evolved, inference-time framework for automatic MAS design.<n> MAS-ZERO employs meta-level design to iteratively generate, evaluate, and refine MAS configurations tailored to each problem instance.
arXiv Detail & Related papers (2025-05-21T00:56:09Z) - MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering [57.156093929365255]
Gym-style framework for systematically reinforcement learning, evaluating, and improving autonomous large language model (LLM) agents.<n>MLE-Dojo covers diverse, open-ended MLE tasks carefully curated to reflect realistic engineering scenarios.<n>Its fully executable environment supports comprehensive agent training via both supervised fine-tuning and reinforcement learning.
arXiv Detail & Related papers (2025-05-12T17:35:43Z) - Self-Steering Language Models [113.96916935955842]
DisCIPL is a method for "self-steering" language models.<n>DisCIPL uses a Planner model to generate a task-specific inference program.<n>Our work opens up a design space of highly-parallelized Monte Carlo inference strategies.
arXiv Detail & Related papers (2025-04-09T17:54:22Z) - MCCoder: Streamlining Motion Control with LLM-Assisted Code Generation and Rigorous Verification [15.438969500630677]
Large Language Models (LLMs) have demonstrated significant potential in code generation.<n>Most current AI-assisted motion control programming efforts focus on PLCs, with little attention given to high-level languages and function libraries.<n>We introduce MCCoder, an LLM-powered system tailored for generating motion control code, integrated with a soft-motion controller.
arXiv Detail & Related papers (2024-10-19T16:46:21Z) - Using Large Language Models for the Interpretation of Building Regulations [7.013802453969655]
Large language models (LLMs) can generate logically coherent text and source code responding to user prompts.
This paper evaluates the performance of LLMs in translating building regulations into LegalRuleML in a few-shot learning setup.
arXiv Detail & Related papers (2024-07-26T08:30:47Z) - AutoRepo: A general framework for multi-modal LLM-based automated
construction reporting [4.406834811182582]
This paper presents a novel framework named AutoRepo for automated generation of construction inspection reports.
The framework was applied and tested on a real-world construction site, demonstrating its potential to expedite the inspection process.
arXiv Detail & Related papers (2023-10-11T23:42:00Z) - Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models [75.75038268227554]
Self-Checker is a framework comprising a set of plug-and-play modules that facilitate fact-checking.
This framework provides a fast and efficient way to construct fact-checking systems in low-resource environments.
arXiv Detail & Related papers (2023-05-24T01:46:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.