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发表论文： ResearchGate:https://www.researchgate.net/profile/Qian-Wang-119 Google Scholar: https://scholar.google.com/citations?user=pd2EAqgAAAAJ&hl=en
研究方向一：基于点云的结构尺寸质量检查 1. Tang, X., Wang, M., Wang, Q.*, Guo, J., and Zhang, J., 2022. Benefits of terrestrial laser scanning for construction QA/QC: a time and cost analysis. Journal of Management in Engineering, 38(2), 05022001. 2. Tan, Y., Li, S., and Wang, Q.*, 2020. Automated geometric quality inspection of prefabricated housing units using BIM and LiDAR.Remote Sensing, 12(15), 2492. 3. Guo, J., Wang, Q.*, and Park, J.H., 2020. Geometric quality inspection of prefabricated MEP modules with 3D laser scanning. Automation in Construction, 111, 103053. 4. Wang, Q., Sohn, H., and Cheng, J.C.P.*, 2019. Development of high-accuracy edge line estimation algorithms using terrestrial laser scanning. Automation in Construction, 101, 59-71. 5. Kim, M.K., Wang, Q.*, and Li, H., 2019. Non-contact sensing based geometric quality assessment of buildings and civil structures: a review. Automation in Construction, 100, 163-179. 6. Guo, J., Yuan, L., and Wang, Q.*, 2020. Time and cost analysis of geometric quality assessment of structural columns based on 3D terrestrial laser scanning. Automation in Construction, 110, 103014. 7. Wang, Q.*, 2019. Automatic checks from 3D point cloud data for safety regulation compliance for scaffold work platforms. Automation in Construction, 104, 38-51. 8. Wang, Q., Sohn, H.*, and Cheng, J.C.P., 2016. Development of a mixed pixel filter for improved dimension estimation using AMCW laser scanner. ISPRS Journal of Photogrammetry and Remote Sensing, 119, 246-258. 9. Wang, Q., Kim, M.K., Cheng, J.C.P., and Sohn, H.*, 2016. Automated quality assessment of precast concrete elements with geometry irregularities using terrestrial laser scanning. Automation in Construction, 68, 170-182. 10. Wang, Q., Cheng, J.C.P.*, and Sohn, H., 2017. Automated estimation of reinforced precast concrete rebar positions using colored laser scan data. Computer-Aided Civil and Infrastructure Engineering, 32(9), 787–802.
研究方向二：基于点云的三维模型重建 1. Wang, Q.*, Li, J.*, Tang, X., and Zhang, X., 2022. How data quality affects model quality in scan-to-BIM: A case study of MEP scenes. Automation in Construction, 144, 104598. 2. Wang, B., Wang, Q.*, Cheng, J.C.P.*, and Yin, C., 2022. Object verification based on deep learning point feature comparison for scan-to-BIM. Automation in Construction, 142, 104515. 3. Qiu, Q., Wang, M., Guo, J., Liu, Z., and Wang, Q.*, 2022. An adaptive down-sampling method of laser scan data for scan-to-BIM. Automation in Construction, 135, 104135. 4. Wang, B., Wang, Q.*, Cheng, J.C.P.*, Song, C., and Yin, C., 2022. Vision-assisted BIM reconstruction from 3D LiDAR point clouds for MEP scenes. Automation in Construction, 133, 103997. 5. Wang, B., Yin, C., Luo, H., Cheng, J.C.P.*, and Wang, Q.*, 2021.Fully automated generation of parametric BIM for MEP scenes based on terrestrial laser scanning data. Automation in Construction, 125, 103615. 6. Qiu, Q., Wang, M., Tang, X., and Wang, Q.*, 2021. Scan planning for existing buildings without BIM based on user-defined data quality requirements and genetic algorithm. Automation in Construction, 130, 103841. 7. Yang, L., Cheng, J.C.P.*, and Wang, Q.*, 2020. Semi-automated generation of parametric BIM for steel structures based on terrestrial laser scanning data. Automation in Construction, 112, 103037. 8. Yuan, L., Guo, J., and Wang, Q.*, 2020. Automatic classification of common building materials from 3D terrestrial laser scan data. Automation in Construction, 110, 103017. 9. Wang, Q.*, Guo, J., and Kim, M.K., 2019.An application oriented scan-to-BIM framework. Remote Sensing, 11(3), 365. 10. Wang, Q., Sohn, H.*, and Cheng, J.C.P., 2018. Automatic as-built BIM creation of precast concrete bridge deck panels using laser scan data. Journal of Computing in Civil Engineering, 32(3), 04018011. 11. Wang, Q., Tan, Y.*, and Mei, Z., 2020. Computational methods of acquisition and processing of 3D point cloud data for construction applications. Archives of Computational Methods in Engineering, 27(2), 479-499. (ESI highly cited paper as of September/October 2020) 12. Wang, Q.*, and Kim, M.K., 2019. Applications of 3D point cloud data in the construction industry: a fifteen-year review from 2004 to 2018. Advanced Engineering Informatics, 39, 306-319. (Most cited articles from Advanced Engineering Informatics as of January 2022; ESI highly cited paper as of January/February 2022)
研究方向三：基于图像的建筑病害检测 1. Guo, J., Wang, Q.*, Su, S., and Li, Y., 2023.Informativeness-guided active learning for deep learning–based façade defects detection. Computer-Aided Civil and Infrastructure Engineering. 2. Cui, Z., Wang, Q.*, Guo, J., and Lu, N.*, 2022. Few-shot classification of façade defects based on extensible classifier and contrastive learning. Automation in Construction, 141, 104381. 3. Li, J., Wang, Q.*, Ma, J., and Guo, J., 2022. Multi-defect segmentation from façade images using balanced copy-paste method. Computer-Aided Civil and Infrastructure Engineering, 37(11), 1434-1449. 4. Guo, J., and Wang, Q.*, 2022. Human-related uncertainty analysis for automation-enabled façade visual inspection: a Delphi study. Journal of Management in Engineering,38(2), 04021088. 5. Guo, J., Wang, Q.*, and Li, Y., 2021. Evaluation-oriented façade defects detection using rule-based deep learning method. Automation in Construction,131, 103910. 6. Guo, J., Wang, Q.*, and Li, Y., 2021.Semi-supervised learning based on convolutional neural network and uncertainty filter for façade defects classification. Computer-Aided Civil and Infrastructure Engineering,36(3), 302-317. 7. Guo, J., Wang, Q.*, Li, Y., and Liu, P., 2020. Façade defects classification from imbalanced dataset using meta learning-based convolutional neural network. Computer-Aided Civil and Infrastructure Engineering, 35(12), 1403-1418.
研究方向四：BIM与数字孪生 1. Zhou, X., Sun, K., Wang, Q.*, Wang, J., Huang, X., and Zhou, W., 2023. IEDW: A BIM-based indoor electric distribution wiring algorithm using graph theory and capacity-limited multiple traveling salesman problem solver. Advanced Engineering Informatics, 56, 101999. 2. Zhou, X., Wang, M., Liu, Y.*, Wang, Q.*, Guo, M.*, and Zhao, J., 2021. Heterogeneous network modeling and segmentation of building information modeling data for parallel triangulation and visualization. Automation in Construction, 131, 103897. 3. Su, S., Li, S., Ju, J., Wang, Q.*, and Xu, Z., 2021. A building information modeling-based system for estimating building demolition waste and evaluating its environmental impacts. Waste Management, 134, 159-169. 4. Su, S.*, Wang, Q., Han, L., Hong, J., and Liu, Z., 2020. BIM-DLCA: An integrated dynamic environmental impacts assessment model for buildings. Building and Environment, 183, 107218. 5. Chen, K., Chen, W., Cheng, J.C.P.*, and Wang, Q., 2020. Developing efficient mechanisms for BIM-to-AR/VR data transfer. Journal of Computing in Civil Engineering, 34(5), 04020037. 6. Cheng, J.C.P., Chen, W.*, Chen, K., and Wang, Q., 2020. Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms. Automation in Construction, 112, 103087. (ESI highly cited paper as of January/February 2022) 7. Chen, W., Chen, K., Cheng, J.C.P.*, Wang, Q., and Gan, V.J., 2018. BIM-based framework for automatic scheduling of facility maintenance work orders. Automation in Construction, 91, 15-30.
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