教育经历

2025.08-2026.02，访问学者，加州大学伯克利分校
2023-至今，博士（在读）土木工程，清华大学
2021-2023，辅修工学学士学位，软件工程，清华大学，GPA: 3.85/4.00， 30学分
2019-2023，主修工学学士学位，土木、水利与海洋工程（土木工程方向），清华大学，GPA: 3.98/4.00， 191学分

工作经历

2025.06-2025.08, 多模态大模型算法实习生，快手
2023-2024，本科生课程《土木工程与防灾减灾》，助教
2023-2024，研究生课程《灾害学》，助教
2020-2022，清华大学土木工程系暨建设管理系学生科协，部员、部长、主席
2019-2021，清华大学学生创客空间社团，会员、常务副会长

荣誉

学业荣誉

2024.12，清华大学综合优秀一等奖学金
2023.08，清华大学博士生“未来学者”奖学金
2023.07，北京市优秀毕业生
2023.06，清华大学优秀毕业生
2023.03，茅以升科学技术奖——工程教育学生奖（全国共9人）
2022.12，清华大学本科生特等奖学金提名（全校共15人）
2022.12，国家奖学金
2022.12，清华大学综合优秀奖学金
2022.12，清华大学科技创新优秀奖学金（清华之友-龙湖奖学金）
2021.12，清华大学综合优秀奖学金（清华之友-汐泰奖学金）
2020.12，清华大学综合优秀奖学金（清华之友-恒大奖学金）
2020.12，清华大学实践优秀奖学金（清华之友-金门一等奖学金）

科学研究

2025.06，清华大学首批“探臻学者”（全校共15人）
2024.06，“星河奖”（土木工程诊治与运维领域）优秀论文银奖（唯二学生获奖）
2023.12，北京市普通高校优秀本科毕业设计（论文）
2023.06，清华大学本科生综合论文训练优秀论文
2023.05，第48届日内瓦国际发明展评审团特别嘉许金奖（团队内唯一本科生）

科创竞赛

2024.07，“北京西城 · 高校发展联盟”青年数字创新创意大赛一等奖（2/100+)
2022.12，“盈建科杯”全国大学生智能建造数字化设计大赛一等奖（队长，1/600+)
2022.07，“中铁十四局杯”第四届“茅以升公益桥-小桥工程”创新设计大赛特等奖（队长，1/99)

技能

计算机技能
- 编程语言：python、C、C++、C#、java、js、matlab、sql
- 前端：Vue.js
- 后端：Django、flask
- 操作系统：Windows、Linux
- AI：基本熟悉深度学习和机器学习常用模型（CNN、RNN、GNN、LSTM、Transformer……）
有限元分析
- Marc、Midas、SAP2000
建模绘图
- AutoCAD、Revit、Sketchup

论文发表

HouseMind: Tokenization Allows Multimodal Large Language Models to Understand, Generate and Edit Architectural Floor Plans

QIN S Z, WEBER R E, LU X Z. Tokenization Allows Multimodal Large Language Models to Understand, Generate and Edit Architectural Floor Plans[C/OL]. CVPR, 2026. https://arxiv.org/abs/2603.11640.

Intelligent design of dimensions of reinforced concrete frame structure components using diffusion models

Gu Y, Qin S, Liao W, et al. Intelligent design of dimensions of reinforced concrete frame structure components using diffusion models[J/OL]. Computers in Industry, 2026, 175: 104428. DOI:10.1016/j.compind.2025.104428.

Leveraging data-driven artificial intelligence in optimization design for building structures: A review

QIN S Z, FEI Y F, LIAO W J, et al. Leveraging data-driven artificial intelligence in optimization design for building structures: A review[J/OL]. Engineering Structures, 2025, 341: 120810. DOI:10.1016/j.engstruct.2025.120810.

Comparative analysis of intelligent retrofit design methods of RC frame structures using buckling-restrained braces

QIN S Z, LIAO W J, TAN Z, et al. Comparative analysis of intelligent retrofit design methods of RC frame structures using buckling-restrained braces[J/OL]. Bulletin of Earthquake Engineering, 2025[2025-04-15]. DOI:10.1007/s10518-025-02164-3.

Graph neural network-assisted evolutionary algorithm for rapid optimization design of shear-wall structures

FEI Y F, QIN S Z, LIAO W J, et.al. Graph neural network-assisted evolutionary algorithm for rapid optimization design of shear-wall structures[J/OL]. Advanced Engineering Informatics, 2025, 65: 103129. DOI:10.1016/j.aei.2025.103129.

Intelligent design for component size generation in reinforced concrete frame structures using heterogeneous graph neural networks

QIN S Z, LIAO W J, HUANG Y L, et.al. Intelligent design for component size generation in reinforced concrete frame structures using heterogeneous graph neural networks[J/OL]. Automation in Construction, 2025, 171: 105967. DOI:10.1016/j.autcon.2025.105967.

Intelligent generation and optimization method for the retrofit design of RC frame structures using buckling-restrained braces

TAN Z, QIN S Z, HU K G, et al. Intelligent generation and optimization method for the retrofit design of RC frame structures using buckling-restrained braces[J/OL]. Earthquake Engineering & Structural Dynamics, 2025, 54(2): 530-547. DOI:10.1002/eqe.4268.

ChatHouseDiffusion: Prompt-guided generation and editing of floor plans

QIN S Z, HE C Y, CHEN Q Y, et al. ChatHouseDiffusion: Prompt-guided generation and editing of floor plans[A/OL]. arXiv, 2024[2024-10-17]. http://arxiv.org/abs/2410.11908. DOI:10.48550/arXiv.2410.11908.

Intelligent design and optimization system for shear wall structures based on large language models and generative artificial intelligence

QIN S Z, GUAN H, LIAO W J, et.al. Intelligent design and optimization system for shear wall structures based on large language models and generative artificial intelligence[J/OL]. Journal of Building Engineering, 2024, 95: 109996. DOI:10.1016/j.jobe.2024.109996.

AIstructure-Copilot: Assistant for generative AI-driven intelligent design of building structures

QIN S Z, LIAO W J, HUANG S N, et.al. AIstructure-Copilot: Assistant for generative AI-driven intelligent design of building structures[J/OL]. Smart Construction, 2024, 1(1): 0001. DOI:10.55092/sc20240001.

基于BIM和深度学习点云分割的施工检查方法模拟研究

张帆, 孙楚津, 覃思中, 赵馨怡. 基于BIM和深度学习点云分割的施工检查方法模拟研究[J]. 工程力学, 2024, 41(2): 194-201. DOI: 10.6052/j.issn.1000-4750.2022.04.0281

An efficient assessment method for intelligent design results of shear wall structure based on mechanical performance, material consumption, and empirical rules

QIN S Z, LIAO W J, LIN Y Q, LU X Z. An efficient assessment method for intelligent design results of shear wall structure based on mechanical performance, material consumption, and empirical rules[J/OL]. Engineering Mechanics, 2023, 40(12): 148-159. DOI:10.6052/j.issn.1000-4750.2023.05.0360.

覃思中