Predicting structural data of bridge utilizing Large
Language Models

Shreejan Maharjan1[0009-0001-1299-8790] and Pang-jo Chun1[0000-0002-9755-8435]

1 The University of Tokyo, Tokyo 113-8485, Japan
chun@g.ecc.u-tokyo.ac.jp

Abstract

The applicability of synergetic technologies of Artificial Intelligence (AI) and Digital Twin (DT) for a comprehensive bridge inspection framework depends on the completeness of the structure's data availability. Incomplete or missing data of the structures is often observed, which hinders the processes for effective inspection and maintenance. This study presents a methodology that utilizes Large Language Models (LLMs) to address the challenge of extracting and predicting incomplete structural information for bridges. The proposed approach involves utilizing a closed-source LLM processed with bridge-specific dataset that enables accurate information retrieval from an existing database. The generative model then makes predictions and generates high-quality data that is subsequently used to build a comprehensive bridge database. The experimental results emphasize the effectiveness of the proposed LLM model in accurately predicting incomplete bridge data within the engineering constraints. This study addresses the existing challenge of incomplete bridge data by proposing an effective framework for data generation. By bridging this gap, the applicability of BIM and digital twin technologies is significantly improved, making them more accessible and feasible for future inspection and maintenance work on infrastructures.

Keywords: Large Language Models, Artificial Intelligence, Information extraction, Bridge structural data prediction, Finite Element Method.

Introduction

The aging infrastructure crisis is a critical global issue, particularly in the Architecture, Engineering, and Construction (AEC) sector. In Japan, around 43% of bridges are over 50 years old [1], necessitating urgent maintenance strategies. Traditional bridge inspection relies on manual visual assessments and documented reports, making the process time-consuming, costly, and labor-intensive. Advancements in AI-driven bridge inspection, such as ML/DL for image-based damage detection [2][3] ,semi-autonomous robotic systems [4], IoT-based real-time monitoring [5][6], and LSTMs for deterioration prediction [7], have significantly improved inspection efficiency. However, AI models' success depends on high-quality, extensive datasets [8]. Incomplete or missing data limits the effectiveness of Building Information Modeling (BIM) and Digital Twins (DTs) in monitoring and maintenance [9].

The advent of AI has revolutionized the conventional bridge maintenance framework in recent years. The study by Yamane [10], which utilizes DL-based method to detect damage prevalent in the bridge and localization into 3D BIM illustrates tremendous contribution to enhance bridge inspection and management tasks. Furthermore, Lin [11] proposed a structure-oriented segmentation of individual components of bridge utilizing the 3d point clouds contributes to the feasibility of digitalizing existing structures into digital models. However, the lack of definite digital data has been one of the hurdles preventing application of these advanced AI models to be implemented in the infrastructure inspection and management.

While ML/DL-based models show enhanced performance and efficient workflow, their performance depends on annotated domain-specific datasets [12] [13]. Additionally, these models primarily handle structured data and struggle with unstructured, multimodal formats [14]. In this regard, LLMs offer a promising solution by efficiently processing unstructured and multimodal data, making them valuable for extracting and predicting bridge structure data where information is incomplete or unavailable. Furthermore, LLMs, with their enormous inventory of knowledge and ability to interpret even unstructured and semi-structured inputs, emerge as powerful tools for automated information extraction and predictive modeling.

Chen et al. [15], for instance, demonstrated the generative ability of LLM by parsing historical inspection report and producing inferred maintenance recommendations. Similarly, Gao et al. [16] depicted the capacity of LLM combined with Graph Neural Network (GNN) in generating missing or incomplete data based on historical records. Liu et al. [17] further proposed a zero-shot and few-shot prompting with GPT-3 to automate building code compliance checks—an especially valuable approach in domains with limited annotated data. In other works, Qin et al. [18]introduced an LLM-controlled system for the design of shear wall structures, where natural language inputs are used to direct generative modules. This approach enables a fully integrated, prompt-driven design process.

The integration of ML/DL, and the evolving capabilities of LLMs has increasingly demonstrated the feasibility of AI-driven approaches in bridge maintenance. Nevertheless, most ML/DL frameworks continue to depend heavily on fine-tuning and domain-specific training—practices that are both resource-intensive and difficult to implement, particularly in contexts of data scarcity. As a result, these state-of-the-art models often fall short in real-world infrastructure scenarios.

LLMs, on the other hand, have shown significant potential in extracting relevant insights and compensating for data gaps and inconsistencies. Despite these advances, their applications remain largely confined to conventional tasks such as content summarization, factual retrieval, and inference from semi-structured inputs. The more complex challenge of predicting structured design parameters—especially when original specifications are missing—remains largely unexplored. This is a critical gap, given that current methodologies still rely on training approaches, which are often impractical in data-scarce environments.

To address this challenge, the present study introduces a hybrid AI-driven framework that employs LLMs for predicting bridge design parameters and structural details. The proposed methodology combines a feature-based similarity search—used to retrieve reference designs from a curated knowledge base—with LLM-powered inference to generate structured outputs. Crucially, the LLM functions without fine-tuning, instead leveraging zero-shot inference strategies guided by historical data patterns and engineering constraints.

Methodology

This study proposes a framework for bridge structure data prediction that integrates a synergistic LLM-based data extraction module with a hybrid AI and constraint-based prediction approach. First, a context-aware LLM-driven information extraction workflow is developed to process unstructured text documents into a structured format. This structured data is then utilized to develop an inference-driven, optimized LLM module that predicts bridge structural data while adhering to engineering principles, historical data patterns and extracted features. The overall methodological framework is illustrated in Fig. 1.

Fig. 1. Overall framework of methodology

Information Extraction

The information extraction pipeline is specifically designed to retrieve structured data from bridge design documents that contain a mixture of narrative descriptions, tabular specifications and engineering annotations. These diverse formats increase the complexity of the documents for data retrieval and require a specific approach that can handle the technical vocabulary and nuances of domain-specific terminologies. To this end, the study proposes a framework with two modules. The first module is the Context-Aware Extraction module, which utilizes OpenAI’s GPT-4o model to extract relevant design parameters from unstructured text. Rather than relying on simple keyword matching, this module constructs dynamic prompts from three main components: (i) raw text content from the document, (ii) targeted extraction instructions, and (iii) a predefined JSON schema that outlines the required output format. The model interprets the semantic meaning of technical terms — many of which are deeply rooted in civil engineering terminology — and can thus resolve ambiguities and assign information precisely to the appropriate fields. In this role, the LLM acts less like a traditional parser and more like a domain-specific interpreter. If a value is missing or unclear, the model leaves the corresponding field empty, preserving the integrity of the data instead of making assumptions.

Next, the Computational Inference module identifies important structural values that are not directly specified in the source text. Based on fundamental principles of structural engineering and rule-based logic, this component infers design details that are only implied and not written out. One such example is the slab width, which is calculated as: Slab width = Left overhang + ∑ (beam spacing) + Right overhang). By applying structural logic, the module fills critical data gaps that the language model alone cannot solve. This approach not only improves the accuracy of the system but also provides more clarity to the extracted information by anchoring it to the technical foundations. Once these values are derived, they are compiled into a standardized JSON format and can be inserted into modeling or design workflows.

Bridge data prediction

The bridge data prediction module operates through a hybrid, two-stage process designed to infer missing or optimized structural attributes from minimal input. By combining data-driven retrieval with language model inference, the framework balances engineering precision with semantic flexibility—ensuring that the predicted outputs are both technically grounded and contextually relevant.

Figure 2. Framework for bridge data prediction

The prediction process begins with a search based on empirical design. Using two fundamental geometric features, the length of the main girder (L) and the width of the deck (B), the system identifies the most similar bridges from a curated reference dataset--retreived from the unstructured documents. Each entry in this dataset represents a fully documented bridge design and thus provides a reliable basis for inference. To quantify the similarity, the system applies a standard Euclidean distance metric over normalized feature values (Equation 1). Based on this calculation, the top n most similar bridges (typically n = 10) are selected as the prediction context. These closest matches are extracted from the design database and used to inform the next stage of the pipeline.

(1)

Prompt:	Bridge Length = 35.0 m, Slab Width = 12.5 m. Retrieve similar bridge designs and suggest optimized parameters
Thought:	Key features influencing bridge configuration include girder spacing, slab thickness, and material type. Previous records suggest 3–5 girders for this slab width
Action:	Top 10 closest bridges retrieved based on L and B.
Prompt:	Generate complete structural parameters using these retrieved designs. Output in standardized JSON format
Thought:	Span-to-depth ratio typically ranges from 15–25. Girder spacing likely falls between 2.5 and 3.0 m. Material likely reinforced concrete
Action:	Output structured in valid JSON format

Fig. 3. Example of prompt design

With a reference set in place, the second stage employs OpenAI’s GPT-4o model to generate a complete and coherent prediction of bridge design parameters. A structured prompt is constructed which contains the input dimensions, the top n reference bridges (formatted in JSON), and explicit instructions to produce response that adhere to a predefined schema. Crucially, the prompt directs the model to infer values that are both logically consistent and aligned with standard engineering practices. Rather than working as a general-purpose text-generator, the LLM model functions as a domain-specific assistant—drawing from real examples and producing structured output suitable for engineering analysis. Once the model returns its response, the system extracts the structured JSON output.

Evaluation metrics

Contrary to fine-tuning and training methodologies, which are evaluated based on loss accumulation during the training process, we employ the Mean Absolute Percentage Error (MAPE) to assess the discrepancy between predicted and actual bridge parameter values. Since different parameters have varying units, normalizing them eliminates scale-induced disproportionation, enabling a more meaningful comparison. Equation (2) defines the mathematical formulation for component-wise accuracy, where ​ and ​represent the ground truth and predicted values for feature iii, respectively. A small constant is introduced to prevent division by zero, and component-wise accuracy is then computed as Equation (3).

(2)

(3)

To determine the overall accuracy of the predicted bridge data, a weighted overall accuracy metric is employed. Different importance weights are assigned to each bridge parameter based on its engineering significance. Equation (4) outlines the computation, wheredenotes the assigned weight for feature i, represents the component-wise accuracy, and N is the total number of features:

(4)

Results and Discussion

For this study, documents containing steel plate girder bridge design data were collected and processed using the proposed information extraction framework to systematically extract individual bridge design parameters. Consequently, 57 distinct bridge datasets were generated from these unstructured documents, serving as reference data for information retrieval and predictive modeling. Following this, the proposed optimized LLM-based prediction framework was employed which generated 10 distinct bridge datasets based on specified queries (bridge length and width), yielding structured JSON data for each bridge.

Fig. 4. Overall accuracy score of predicted bridge data

To evaluate the performance of the prediction, the 10 individual bridge datasets generated by the LLM were compared against the 57 reference datasets, which serve as the ground truth benchmark. First, the component-wise MAPE was calculated to measure deviations from the actual values in the ground truth. This was followed by the computation of component-wise accuracy. Subsequently, the proposed weighted overall accuracy metric was applied to assess the overall prediction accuracy. Figure 3 illustrates the overall accuracy of each predicted bridge, demonstrating that the generated outputs exhibit satisfactory performance.

Feasibility in simulation modeling

This study aims to address the challenge of data redundancy in AI and DT applications, which hinders seamless integration in bridge inspection and maintenance. To overcome this limitation, we propose a framework that leverages LLMs to predict bridge design data. Using this framework, structured datasets were generated for 10 distinct bridges. To assess the feasibility of the generated data for simulation models and AI integration, we examined its applicability in Finite Element Method (FEM) analysis. As depicted in Figure 4, the predicted JSON data was utilized in Abaqus for component modeling. Subsequently, stress analysis was conducted, and von Mises stress values were extracted with deformation scaling factor of 343x to validate the reliability and practical applicability of the data generated. From the Mises stress contours, the maximum stress observed was 106.9 MPa, which remains well below the typical yield strength of structural steel (200 MPa). This confirms that the design remains safely within the elastic range, with no plastic deformation occurring under the simulated loading conditions.

Fig. 5. FEM simulation of bridge based on predicted data

Furthermore, this analysis confirms the potential of the proposed framework for generating data which adhere to simulation modeling. Additionally, it highlights that the data generated from this framework can address incomplete data challenges by providing supplementary information, thereby enhancing the capabilities of AI and DT technologies in the bridge maintenance domain.

Conclusion

The issue of data redundancy in integrating technologies into infrastructure inspection and maintenance systems presents a significant challenge shaping the focus of our study toward developing an effective solution to address this limitation. To mitigate this issue, the study proposed an LLM-based bridge data generation framework. This framework demonstrates its efficiency in processing unstructured documents to generate structured data, enabling the LLM agent to optimize and produce domain-specific information in a desired format. The results indicate that the predicted data generated through this framework achieves high accuracy. Furthermore, integrating the predicted data into a simulation model validates its feasibility and effectiveness for further analysis in bridge maintenance and management.

Future work will focus on tackling the challenges of improving both the accuracy and consistency of data predictions within the proposed model. Currently, the framework is limited to processing data for steel plate girders. Expanding its adaptability to support other bridge types is therefore a key goal moving forward. Incorporating damage detection techniques—such as methods by Chun and Kikuta [19] for identifying structural damage and Hattori et al. [20] approach for pinpointing damage within a 3D model—will further enhance the system. Together, these developments promise to make automated maintenance not only more robust but also significantly more effective.

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