Domain-specific adaptation of large language models and integration with knowledge graph analytics for enhanced bridge maintenance decision making

Bridges are critical components of transportation infrastructure, ensuring mobility and economic connectivity. However, a large portion of U.S. bridges are in poor condition, raising significant safety and maintenance challenges. According to the 2025 ASCE Infrastructure Report Card, 42% of U.S. bridges are over 50 years old and 6.8% are in poor condition, with an estimated $191 billion in bridge-related system rehabilitation needs. Despite extensive data collection efforts by transportation agencies, existing data-driven models for bridge condition assessment and maintenance decision making remain limited. Most models rely primarily on abstract data from single sources, such as the National Bridge Inventory (NBI), which lacks the rich contextual information contained in textual inspection reports, images, and other heterogeneous data sources. Consequently, these models struggle to leverage the full potential of the available multimodal data to support accurate condition assessment, deterioration prediction, and cost-effective maintenance strategies. To address these limitations, a novel large language model (LLM)-based analytics framework for bridge data integration and enhanced maintenance decision support is proposed. The proposed framework is composed of six primary components: (1) an LLM-based semantic information extraction method for extracting information entities that describe bridge conditions and maintenance actions from bridge textual reports; (2) a low-rank adaptation (LoRA)-based finetuning method to efficiently adapt pretrained LLMs to the bridge domain; (3) an LLM-based semantic relation extraction method for extracting semantic relations from bridge reports to link the extracted, yet isolated, information entities in the form of a bridge knowledge graph; (4) an LLM-based data augmentation method to mitigate training data scarcity and enable advanced encoding of high-dimensional datasets; (5) a vector encoding-based multimodal data linking method to link diverse inspection data types (e.g., text, images) across multiple sources; and (6) an LLM-based method that integrates knowledge-enriched prompts, retrieval-augmented generation (RAG), reinforcement learning from human feedback, and the bridge knowledge graph to support bridge maintenance decision-making, including automated analysis of bridge conditions and generation of context-aware maintenance plans. The experimental results demonstrated the promise of the proposed framework. Overall, this research provides a novel, multimodal bridge analytics framework that advances automated condition assessment, improves maintenance decision support, and demonstrates the potential of LLM-based methods to transform data-driven bridge management.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Civil Engineering

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Chen, Qiyang

Contributors dc:contributor

• El-Gohary, Nora
• El-Rayes, Khaled
• Golparvar-Fard, Mani
• Zhai, ChengXiang
• Jebelli, Houtan

• large language model
• bridge maintenance decision making
• information extraction, data-driven
• finetuning

Statement dc:rights

• Copyright 2025 Qiyang Chen

Language dc:language

en

Handle dc:identifier

https://hdl.handle.net/2142/132801

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/132801