Integrating Large Language Models for Automated Structural Analysis

• URL: http://arxiv.org/abs/2504.09754v1
• Date: Sun, 13 Apr 2025 23:10:33 GMT
• Title: Integrating Large Language Models for Automated Structural Analysis
• Authors: Haoran Liang, Mohammad Talebi Kalaleh, Qipei Mei,
• Abstract summary: We propose a framework that integrates Large Language Models (LLMs) with structural analysis software.<n>LLMs parse structural descriptions from text and translate them into Python scripts.<n>It employs domain-specific prompt design and in-context learning strategies to enhance the LLM's problem-solving capabilities and generative stability.
• Score: 0.7373617024876725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated analysis for engineering structures offers considerable potential for boosting efficiency by minimizing repetitive tasks. Although AI-driven methods are increasingly common, no systematic framework yet leverages Large Language Models (LLMs) for automatic structural analysis. To address this gap, we propose a novel framework that integrates LLMs with structural analysis software. LLMs serve as the core engine: they parse structural descriptions from text and translate them into executable Python scripts. Moreover, the framework integrates the generative capabilities of LLMs with code-based finite element (FE) tools like OpenSeesPy. It employs domain-specific prompt design and in-context learning strategies to enhance the LLM's problem-solving capabilities and generative stability, enabling fully automated structural analysis from descriptive text to model outputs. In our experiments, we introduce a well-curated small-scale benchmark dataset of 20 structural analysis word problems (SAWPs) with ground-truth solutions and evaluate the performance of different LLMs within our framework in solving these SAWPs. The role of system instructions, crafted by structural engineers, is also investigated to understand their impact on LLM-driven structural analysis. Additionally, the generative stability of our framework is examined. Through multiple validation experiments on the benchmark, our results demonstrate that the proposed framework can substantially increase the level of automation in solving SAWPs compared to traditional methods. Quantitatively, the framework, built on GPT-4o, achieved 100% accuracy, surpassing GPT-4 (85%), Gemini 1.5 Pro (80%), and Llama-3.3 (30%) on the test examples. Furthermore, integrating domain-specific instructions enhanced performance by 30% on problems with asymmetrical structural configurations.

Related papers

• RooflineBench: A Benchmarking Framework for On-Device LLMs via Roofline Analysis [53.90240071275054]
  The transition toward localized intelligence through Small Language Models (SLMs) has intensified the need for rigorous performance characterization on resource-constrained edge hardware.<n>We propose a systematic framework that unifies architectural primitives and hardware constraints through the lens of operational intensity (OI)<n>By defining an inference-potential region, we introduce the Relative Inference Potential as a novel metric to compare efficiency differences between Large Language Models (LLMs) on the same hardware substrate.
  arXiv  Detail & Related papers  (2026-02-12T03:02:22Z)
• Understanding Multi-Agent LLM Frameworks: A Unified Benchmark and Experimental Analysis [2.903627214446312]
  We introduce an architectural taxonomy for systematically comparing multi-agent LLM frameworks along fundamental dimensions.<n>We develop a unified evaluation suite that integrates existing benchmarks under a standardized execution pipeline.<n>Our results show that framework-level design choices alone can increase latency by over 100x, reduce planning accuracy by up to 30%, and lower coordination success from above 90% to below 30%.
  arXiv  Detail & Related papers  (2026-02-03T05:37:56Z)
• LM4Opt-RA: A Multi-Candidate LLM Framework with Structured Ranking for Automating Network Resource Allocation [0.7933039558471408]
  We tackle complex analytical and mathematical reasoning tasks requiring nuanced contextual understanding.<n>Existing benchmarks datasets cannot address the complexities of such problems with dynamic environments, variables, and heterogeneous constraints.<n>We introduce NL4RA, a curated dataset comprising 50 resource allocation optimization problems formulated as LP, ILP, and MILP.<n>We then evaluate the performance of well-known open-source LLMs with varying parameter counts.
  arXiv  Detail & Related papers  (2025-11-13T23:19:43Z)
• From Parameters to Performance: A Data-Driven Study on LLM Structure and Development [73.67759647072519]
  Large language models (LLMs) have achieved remarkable success across various domains.<n>Despite the rapid growth in model scale and capability, systematic, data-driven research on how structural configurations affect performance remains scarce.<n>We present a large-scale dataset encompassing diverse open-source LLM structures and their performance across multiple benchmarks.
  arXiv  Detail & Related papers  (2025-09-14T12:20:39Z)
• Teaching LLMs to Think Mathematically: A Critical Study of Decision-Making via Optimization [1.246870021158888]
  This paper investigates the capabilities of large language models (LLMs) in formulating and solving decision-making problems using mathematical programming.<n>We first conduct a systematic review and meta-analysis of recent literature to assess how well LLMs understand, structure, and solve optimization problems across domains.<n>Our systematic evidence is complemented by targeted experiments designed to evaluate the performance of state-of-the-art LLMs in automatically generating optimization models for problems in computer networks.
  arXiv  Detail & Related papers  (2025-08-25T14:52:56Z)
• A Large Language Model-Empowered Agent for Reliable and Robust Structural Analysis [14.754785659805869]
  Large language models (LLMs) have exhibited remarkable capabilities across diverse open-domain tasks, yet their application in specialized domains such as civil engineering remains largely unexplored.<n>This paper starts bridging this gap by evaluating and enhancing the reliability and robustness of LLMs in structural analysis of beams.<n> Experimental results demonstrate that the agent achieves accuracy exceeding 99.0% on the benchmark dataset, exhibiting reliable and robust performance across diverse conditions.
  arXiv  Detail & Related papers  (2025-06-27T04:16:53Z)
• 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)
• Evaluating Large Language Models for Real-World Engineering Tasks [75.97299249823972]
  This paper introduces a curated database comprising over 100 questions derived from authentic, production-oriented engineering scenarios.<n>Using this dataset, we evaluate four state-of-the-art Large Language Models (LLMs)<n>Our results show that LLMs demonstrate strengths in basic temporal and structural reasoning but struggle significantly with abstract reasoning, formal modeling, and context-sensitive engineering logic.
  arXiv  Detail & Related papers  (2025-05-12T14:05:23Z)
• BLADE: Benchmark suite for LLM-driven Automated Design and Evolution of iterative optimisation heuristics [2.2485774453793037]
  BLADE is a framework for benchmarking LLM-driven AAD methods in a continuous black-box optimisation context. It integrates benchmark problems with instance generators and textual descriptions aimed at capability-focused testing, such as specialisation and information exploitation. BLADE provides an out-of-the-box' solution to systematically evaluate LLM-driven AAD approaches.
  arXiv  Detail & Related papers  (2025-04-28T18:34:09Z)
• Elucidating the Design Space of Multimodal Protein Language Models [69.3650883370033]
  Multimodal protein language models (PLMs) integrate sequence and token-based structural information. This paper systematically elucidates the design space of multimodal PLMs to overcome their limitations. Our advancements approach finer-grained supervision, demonstrating that token-based multimodal PLMs can achieve robust structural modeling.
  arXiv  Detail & Related papers  (2025-04-15T17:59:43Z)
• Generating Structured Plan Representation of Procedures with LLMs [5.623006055588189]
  We introduce SOP Structuring ( SOPStruct), a novel approach to transform SOPs into structured representations.<n> SOPStruct produces a standardized representation of SOPs across different domains, reduces cognitive load, and improves user comprehension.<n>Our research highlights the transformative potential of Large Language Models to streamline process modeling.
  arXiv  Detail & Related papers  (2025-03-28T22:38:24Z)
• GateLens: A Reasoning-Enhanced LLM Agent for Automotive Software Release Analytics [9.549568621873386]
  GateLens is an LLM-based system for analyzing data in the automotive domain.<n>Unlike traditional multi-agent or planning-based systems that can be slow, opaque, and costly to maintain, GateLens emphasizes speed, transparency, and reliability.
  arXiv  Detail & Related papers  (2025-03-27T17:48:32Z)
• A Survey on Sparse Autoencoders: Interpreting the Internal Mechanisms of Large Language Models [50.34089812436633]
  Large Language Models (LLMs) have transformed natural language processing, yet their internal mechanisms remain largely opaque.<n> mechanistic interpretability has attracted significant attention from the research community as a means to understand the inner workings of LLMs.<n>Sparse Autoencoders (SAEs) have emerged as a promising method due to their ability to disentangle the complex, superimposed features within LLMs into more interpretable components.
  arXiv  Detail & Related papers  (2025-03-07T17:38:00Z)
• Assessing LLMs for Front-end Software Architecture Knowledge [0.0]
  Large Language Models (LLMs) have demonstrated significant promise in automating software development tasks.<n>This study investigates the capabilities of an LLM in understanding, reproducing, and generating structures within the VIPER architecture.<n> Experimental results, using ChatGPT 4 Turbo 2024-04-09, reveal that the LLM excelled in higher-order tasks like evaluating and creating, but faced challenges with lower-order tasks requiring precise retrieval of architectural details.
  arXiv  Detail & Related papers  (2025-02-26T19:33:35Z)
• IMPROVE: Iterative Model Pipeline Refinement and Optimization Leveraging LLM Agents [17.301758094000125]
  Large language model (LLM) agents have emerged as a promising solution to automate the development of computer vision models.<n>We introduce Iterative Refinement, a novel strategy for LLM-driven ML pipeline design.<n>Iterative Refinement improves stability, interpretability, and overall model performance.
  arXiv  Detail & Related papers  (2025-02-25T01:52:37Z)
• Towards a Probabilistic Framework for Analyzing and Improving LLM-Enabled Software [0.0]
  Large language model (LLM)-enabled systems are a significant challenge in software engineering.<n>We propose a probabilistic framework for systematically analyzing and improving these systems.
  arXiv  Detail & Related papers  (2025-01-10T22:42:06Z)
• StructTest: Benchmarking LLMs' Reasoning through Compositional Structured Outputs [78.84060166851805]
  StructTest is a novel benchmark that evaluates large language models (LLMs) on their ability to follow compositional instructions and generate structured outputs. Assessments are conducted deterministically using a rule-based evaluator, which can be easily extended to new tasks and datasets. We demonstrate that StructTest remains challenging even for top-performing models like Deepseek-V3/R1 and GPT-4o.
  arXiv  Detail & Related papers  (2024-12-23T22:08:40Z)
• Designing Algorithms Empowered by Language Models: An Analytical Framework, Case Studies, and Insights [86.06371692309972]
  This work presents an analytical framework for the design and analysis of large language models (LLMs)-based algorithms.<n>Our proposed framework serves as an attempt to mitigate such headaches.
  arXiv  Detail & Related papers  (2024-07-20T07:39:07Z)
• Large Language Model Agent as a Mechanical Designer [7.136205674624813]
  We propose a framework that leverages a pretrained Large Language Model (LLM) in conjunction with an FEM module to autonomously generate, evaluate, and refine structural designs. LLM operates without domain-specific fine-tuning, using general reasoning to propose design candidates, interpret FEM-derived performance metrics, and apply structurally sound modifications. Compared to Non- Sorting Genetic Algorithm II (NSGA-II), our method achieves faster convergence and fewer FEM evaluations.
  arXiv  Detail & Related papers  (2024-04-26T16:41:24Z)
• LLM Inference Unveiled: Survey and Roofline Model Insights [62.92811060490876]
  Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model. This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
  arXiv  Detail & Related papers  (2024-02-26T07:33:05Z)
• Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty [52.72790059506241]
  Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text. Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
  arXiv  Detail & Related papers  (2023-09-07T01:35:24Z)
• StructGPT: A General Framework for Large Language Model to Reason over Structured Data [117.13986738340027]
  We develop an emphIterative Reading-then-Reasoning(IRR) approach for solving question answering tasks based on structured data. Our approach can significantly boost the performance of ChatGPT and achieve comparable performance against the full-data supervised-tuning baselines.
  arXiv  Detail & Related papers  (2023-05-16T17:45:23Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)

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.