bsdibi/foundation-pit-engineering-instruction-dataset

# Foundation Pit Engineering Instruction Dataset

## Dataset Description

This dataset is constructed to support the research presented in the paper *"A Knowledge Service System for Foundation Pit Engineering Based on LoRA and RAG(https://doi.org/10.1155/adce/8953677)"*. It consists of high-quality instruction-response pairs derived from national and industry standards for foundation pit engineering, designed for fine-tuning large language models (LLMs) in the specialized domain of geotechnical engineering.

## Dataset Summary

- **Total Samples**: 8,000+ Q&A pairs
- **Data Sources**: 35 national and industry standards
- **Language**: Chinese
- **Task Type**: Instruction Fine-Tuning (Supervised Fine-Tuning)

## Data Format

Each data sample follows the standard instruction format for supervised fine-tuning (SFT). The dataset is provided in **JSONL** format (one JSON object per line).

### Example

```json
{"instruction": "基坑支护的设计原则是什么？", "output": "根据《建筑基坑支护技术规程》（JGJ 120-2012），基坑支护的设计应遵循以下原则：1. 安全可靠，确保支护结构满足强度、稳定性和变形要求；2. 经济合理，综合考虑工程地质、水文地质条件和周边环境；3. 保护环境，控制基坑开挖对周边建筑物、地下管线的影响；4. 方便施工，支护结构应与土方开挖、降水等工序协调配合。"}

Fields

Field	Type	Description
instruction	string	The user's question or instruction
output	string	The expected response from the model, based on relevant standards

Note: The system field is not included in this version. Users are recommended to prepend an appropriate system prompt (e.g., "You are a senior foundation pit engineering expert.") during fine-tuning.

Data Sources

The dataset is derived from the following regulatory documents (35 in total), covering the full lifecycle of foundation pit engineering:

• JGJ 120-2012: Technical Code for Support of Building Foundation Pits
• GB 50497-2019: Technical Standard for Monitoring of Building Foundation Pit Engineering
• Other national, industrial, and local standards related to design, construction, monitoring, and maintenance.

Data Construction Process

The dataset was constructed using a hybrid strategy combining automated generation and manual revision:

1. Text Extraction: PDF documents were converted to plain text using PyPDF2 and Tesseract OCR.
2. Structured Parsing: Text was parsed and split according to "chapter-section-article-clause" hierarchy.
3. Q&A Generation: GPT-4 was prompted with structured templates to generate instruction-response pairs.
4. Quality Revision: Automated generation outputs were manually reviewed and corrected for formula errors, internal references, and table/chart citations.

Data Statistics

Split	Number of Samples
Training Set	~7,500
Validation Set	~500
Total	8,000+

Use Cases

This dataset is intended for:

• Supervised fine-tuning (SFT) of LLMs for foundation pit engineering Q&A
• Domain adaptation of general-purpose LLMs to geotechnical engineering
• Evaluation of model performance on professional, knowledge-intensive tasks

Limitations

• The dataset is limited to Chinese regulatory documents and may not generalize to other languages or jurisdictions.
• Some answers may reference tables or figures that are not included in the text; users should refer to the original standards for complete information.
• The dataset has been manually revised but may still contain residual errors.

License

This dataset is released under the MIT License. You are free to use, modify, and distribute it, provided that proper attribution is given to the original source.

Citation

If you use this dataset in your research, please cite the following paper:

@article{shi2026knowledge,
  author = {Shi, Shuqin and Wang, Chenfei and Zhang, Aiguo and Zhang, Kai and Chen, Yuehui and Shen, Jiaqin},
  title = {A Knowledge Service System for Foundation Pit Engineering Based on LoRA and RAG},
  journal = {Advances in Civil Engineering},
  volume = {2026},
  pages = {8953677},
  year = {2026},
  doi = {10.1155/adce/8953677}
}

Contact

For questions, suggestions, or issues regarding this dataset, please contact:

• Shuqin Shi: 2422191019@xmut.edu.cn
• Chenfei Wang (Corresponding Author): wcf001@outlook.com

Acknowledgements

This work is supported by the Scientific Research Fund of the School of Civil Engineering and Architecture, Xiamen University of Technology (Grant No. 4030226004). ```