研究领域

• 智能建造与运维

• 建筑质量检测及健康监测

• 计算机视觉与土建工程的交叉应用

• 建造机器人

论文与著作(代表性)

1.   Guo, J., Wang, Q., Li, Y.,& Liu, P. (2020). Façadedefects classification from imbalanced dataset using meta learning‐basedconvolutional neural network. Computer‐Aided Civil and InfrastructureEngineering, 35(12), 1403-1418.

2.   Guo, J., Wang, Q., & Li, Y. (2021) Semi‐supervisedlearning based on convolutional neural network and uncertainty filter forfaçade defects classification. Computer‐Aided Civil and InfrastructureEngineering. 36(3), 302-317.

3.   Guo, J., Wang, Q., & Park, J. H. (2020).Geometric quality inspection of prefabricated MEP modules with 3D laserscanning. Automation in Construction, 111, 103053.

4.   Guo, J., Yuan, L., & Wang, Q. (2020). Time andcost analysis of geometric quality assessment of structural columns based on 3Dterrestrial laser scanning. Automation in Construction, 110, 103014.

5.   Guo, J., Wang, Q., & Li, Y. (2021).Evaluation-oriented façade defects detection using rule-based deep learningmethod. Automation in Construction, 131, 103910.

6.   Guo, J. & Wang, Q. (2022). Human-relateduncertainty analysis for automation-enabled façade visual inspection: a Delphistudy. Journal of Management in Engineering (ASCE), 38(2), 04021088.

7.   Guo, J., Wang, Q., Su,S., & Li, Y. (2023). Informativeness‐guided active learning for deep learning–based façadedefects detection. Computer‐Aided Civil and Infrastructure Engineering.

8.   Jingjing Guo, Pengkun Liu, BoXiao, Lu Deng, Qian Wang*. “Surface defect detection of civil structures using images: Review fromdata perspective”, Automation in Construction, 2024, 158, 105186,

9.   Honghu Chu, Lu Deng, Huaqing Yuan,Lizhi Long, Jingjing Guo*. “A transformer and self-cascadeoperation-based architecture for segmenting high-resolution bridge cracks”. Automation inConstruction, 2024, 158, 105194,

荣誉与奖励

2022   国家优秀自费留学生奖学金，中国国家留学基金委

2022   博士后国际交流计划引进项目（第一批），合作导师：邓露

2020   第十五届“春晖杯”中国留学人员创新创业大赛 优胜奖（最高奖项）

2020   第十二届“中国青少年科技创新奖”新加坡地区唯一候选人

2018   重庆大学优秀毕业生干部