| Bayesian network models for probabilistic evaluation of earthquake-induced liquefaction based on CPT and Vs databases J Hu, H Liu Engineering geology 254, 76-88, 2019 | 70 | 2019 |
| Supervised learning methods for modeling concrete compressive strength prediction at high temperature M Ahmad, JL Hu, F Ahmad, XW Tang, M Amjad, MJ Iqbal, M Asim, ... Materials 14 (8), 1983, 2021 | 65 | 2021 |
| Assessment of seismic liquefaction potential based on Bayesian network constructed from domain knowledge and history data JL Hu, XW Tang, JN Qiu Soil Dynamics and Earthquake Engineering 89, 49-60, 2016 | 65 | 2016 |
| A Bayesian network approach for predicting seismic liquefaction based on interpretive structural modeling JL Hu, XW Tang, JN Qiu Georisk: Assessment and Management of Risk for Engineered Systems and …, 2015 | 47 | 2015 |
| Relationship between earthquake-induced uplift of rectangular underground structures and the excess pore water pressure ratio in saturated sandy soils J Hu, Q Chen, H Liu Tunnelling and Underground Space Technology 79, 35-51, 2018 | 40 | 2018 |
| A new approach for constructing two Bayesian network models for predicting the liquefaction of gravelly soil J Hu Computers and Geotechnics 137, 104304, 2021 | 38 | 2021 |
| Rockburst hazard prediction in underground projects using two intelligent classification techniques: a comparative study M Ahmad, JL Hu, M Hadzima-Nyarko, F Ahmad, XW Tang, ZU Rahman, ... Symmetry 13 (4), 632, 2021 | 36 | 2021 |
| Identification of ground motion intensity measure and its application for predicting soil liquefaction potential based on the Bayesian network method J Hu, H Liu Engineering geology 248, 34-49, 2019 | 33 | 2019 |
| Data cleaning and feature selection for gravelly soil liquefaction J Hu Soil Dynamics and Earthquake Engineering 145, 106711, 2021 | 31 | 2021 |
| Identifying significant influence factors of seismic soil liquefaction and analyzing their structural relationship XW Tang, JL Hu, JN Qiu KSCE journal of civil engineering 20 (7), 2655-2663, 2016 | 31 | 2016 |
| Analysis of the influences of sampling bias and class imbalance on performances of probabilistic liquefaction models JL Hu, XW Tang, JN Qiu International Journal of Geomechanics 17 (6), 04016134, 2017 | 26 | 2017 |
| The uplift behavior of a subway station during different degree of soil liquefaction JL Hu, HB Liu Procedia engineering 189, 18-24, 2017 | 23 | 2017 |
| Assessment of liquefaction-induced hazards using Bayesian networks based on standard penetration test data XW Tang, X Bai, JL Hu, JN Qiu Natural Hazards and Earth System Sciences 18 (5), 1451-1468, 2018 | 22 | 2018 |
| Minimum training sample size requirements for achieving high prediction accuracy with the BN model: A case study regarding seismic liquefaction J Hu, W Zou, J Wang, L Pang Expert Systems with Applications 185, 115702, 2021 | 20 | 2021 |
| Influence of data quality on the performance of supervised classification models for predicting gravelly soil liquefaction J Hu, J Wang Engineering geology 324, 107254, 2023 | 12 | 2023 |
| Seismic gravelly soil liquefaction assessment based on dynamic penetration test using expanded case history dataset N Pirhadi, J Hu, Y Fang, I Jairi, X Wan, J Lu Bulletin of Engineering Geology and the Environment 80, 8159-8170, 2021 | 12 | 2021 |
| Key factors influencing earthquake-induced liquefaction and their direct and mediation effects J Hu, Y Tan, W Zou Plos one 16 (2), e0246387, 2021 | 12 | 2021 |
| Datasets for gravelly soil liquefaction case histories J Hu, J Wang, W Zou, B Yang Data in Brief 36, 107104, 2021 | 9 | 2021 |
| DPT-based seismic liquefaction triggering assessment in gravelly soils based on expanded case history dataset N Pirhadi, X Wan, J Lu, Y Fang, I Jairi, J Hu Engineering Geology 311, 106894, 2022 | 7 | 2022 |
| Continuous-discrete hybrid Bayesian network models for predicting earthquake-induced liquefaction based on the Vs database J Hu, J Wang, Z Zhang, H Liu Computers & Geosciences 169, 105231, 2022 | 7 | 2022 |
