| Microstructure of cement mortar with nano-particles H Li, H Xiao, J Yuan, J Ou Composites part B: engineering 35 (2), 185-189, 2004 | 1637 | 2004 |
| NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations X Jin, S Cai, H Li, GE Karniadakis Journal of Computational Physics 426, 109951, 2021 | 951 | 2021 |
| Abrasion resistance of concrete containing nano-particles for pavement H Li, M Zhang, J Ou Wear 260 (11-12), 1262-1266, 2006 | 723 | 2006 |
| Structural health monitoring in mainland China: review and future trends J Ou, H Li Structural health monitoring 9 (3), 219-231, 2010 | 663 | 2010 |
| A study on mechanical and pressure-sensitive properties of cement mortar with nanophase materials H Li, H Xiao, J Ou Cement and Concrete research 34 (3), 435-438, 2004 | 662 | 2004 |
| Pore structure and chloride permeability of concrete containing nano-particles for pavement M Zhang, H Li Construction and Building Materials 25 (2), 608-616, 2011 | 661 | 2011 |
| 3D printing of graphene aerogels Q Zhang, F Zhang, SP Medarametla, H Li, C Zhou, D Lin Small 12 (13), 1702-1708, 2016 | 561 | 2016 |
| Computer vision and deep learning–based data anomaly detection method for structural health monitoring Y Bao, Z Tang, H Li, Y Zhang Structural Health Monitoring 18 (2), 401-421, 2019 | 560 | 2019 |
| Double-negative-index ceramic aerogels for thermal superinsulation X Xu, Q Zhang, M Hao, Y Hu, Z Lin, L Peng, T Wang, X Ren, C Wang, ... Science 363 (6428), 723-727, 2019 | 539 | 2019 |
| Flexural fatigue performance of concrete containing nano-particles for pavement H Li, M Zhang, J Ou International Journal of fatigue 29 (7), 1292-1301, 2007 | 481 | 2007 |
| The State Of the Art Data Science and Engineering in Structrual Health Monitoring B Yuequan, C Zhicheng, W Shiyin, X Yang, T Zhiyi, L Hui Engineering 5 (2), 234-242, 2019 | 422 | 2019 |
| The state of the art of data science and engineering in structural health monitoring Y Bao, Z Chen, S Wei, Y Xu, Z Tang, H Li Engineering 5 (2), 234-242, 2019 | 413 | 2019 |
| Convolutional neural network‐based data anomaly detection method using multiple information for structural health monitoring Z Tang, Z Chen, Y Bao, H Li Structural Control and Health Monitoring 26 (1), e2296, 2019 | 365 | 2019 |
| Effect of compressive strain on electrical resistivity of carbon black-filled cement-based composites H Li, H Xiao, J Ou Cement and Concrete Composites 28 (9), 824-828, 2006 | 352 | 2006 |
| Effect of compressive strain on electrical resistivity of carbon black-filled cement-based composites L Hui, X Huigang, O Jinping Cement and Concrete Composites 28 (9), 824-828, 2006 | 352 | 2006 |
| Prediction model of velocity field around circular cylinder over various Reynolds numbers by fusion convolutional neural networks based on pressure on the cylinder X Jin, P Cheng, WL Chen, H Li Physics of Fluids 30 (4), 047105, 2018 | 320 | 2018 |
| Self-sensing, ultralight, and conductive 3D graphene/iron oxide aerogel elastomer deformable in a magnetic field X Xu, H Li, Q Zhang, H Hu, Z Zhao, J Li, J Li, Y Qiao, Y Gogotsi ACS nano 9 (4), 3969-3977, 2015 | 308 | 2015 |
| Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images Y Xu, Y Bao, J Chen, W Zuo, H Li Structural Health Monitoring 18 (3), 653-674, 2019 | 288 | 2019 |
| Naturally dried graphene aerogels with superelasticity and tunable Poisson's ratio X Xu, Q Zhang, Y Yu, W Chen, H Hu, H Li Advanced materials 28 (41), 9223-9230, 2016 | 281 | 2016 |
| Machine learning paradigm for structural health monitoring Y Bao, H Li Structural Health Monitoring 20 (4), 1353-1372, 2021 | 258 | 2021 |
Yuequan BaoSchool of Civil Engineering, Harbin Institute of TechnologyVerified email at hit.edu.cn