本数据集基于自主研发的黏弹黏塑（VE-VP）本构模型，通过有限元软件CODE_BRIGHT开展了系统的数值模拟，生成了用于机器学习预测黄土滑坡滑移线的标准化数据。数据集通过改变坡高、坡角、土体强度、基质吸力、降雨强度及持续时间等多参数组合，模拟了不同工况下边坡的力学响应与水力耦合过程，输出结果以直观反映滑移线分布的位移云图为主。数据采用“参数编号”方式命名，清晰标识各模拟工况，具备耦合机理先进、参数覆盖全面、结果可视化程度高的特点，适用于黄土滑坡机理研究、极端降雨下滑坡预警模型的构建以及基于机器学习的滑移线智能预测等应用。
", "ds_source": "", "ds_process_way": "", "ds_quality": "", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "黄土高原", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 3196539, "ds_files_count": 0, "ds_format": "png，jpg", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "070df7a8-50a3-45f0-a21b-bc80df5a790b.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": "", "organization_id": "bf138922-7121-438c-8d1b-19d5f751c907", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45", "170.50" ], "quality_level": 0, "publish_time": "2026-05-25 17:44:43", "last_updated": "2026-05-25 17:44:43", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.loess.db7396.2026", "i18n": { "en": { "title": "Numerical Simulation Dataset of Loess Slope Landslide Slip Lines Under Extreme Rainfall Conditions", "ds_format": "png、jpg", "ds_source": "", "ds_quality": "", "ds_ref_way": "", "ds_abstract": "This dataset was developed under the National Key R&D Program project \"Stability Analysis and Early Warning Technology for Loess Slopes Under Extreme Rainfall.\" It employs a self-developed Visco-Elastic-Visco-Plastic (VE-VP) constitutive model to conduct systematic numerical simulations via the finite element software CODE_BRIGHT, generating standardized data for machine learning-based prediction of loess landslide slip lines. By varying parameter combinations including slope height, slope angle, soil strength, matric suction, rainfall intensity, and duration, the dataset simulates the mechanical-hydraulic coupled responses of slopes under various conditions. The primary output consists of displacement contour maps that visually depict slip line distribution. Data is named using a \"parameter-coding\" system that clearly identifies each simulation scenario. Characterized by its advanced coupled mechanism, comprehensive parameter coverage, and high visual clarity of results, this dataset is suitable for research on loess landslide mechanisms, the development of early warning models for landslides under extreme rainfall, and intelligent prediction of slip lines based on machine learning.", "ds_time_res": "", "ds_acq_place": "Loess Plateau", "ds_space_res": "", "ds_projection": "", "ds_process_way": "", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "黄土边坡", "极端降雨", "滑坡预测", "数值模拟", "参数化数据集", "机器学习" ], "ds_subject_tags": [ "地理学", "地质学" ], "ds_class_tags": [], "ds_locus_tags": [ "黄土边坡", "极端降雨", "滑坡预测", "数值模拟", "参数化数据集", "机器学习" ], "ds_time_tags": [], "ds_contributors": [ { "true_name": "李磊", "email": "2432917@tongji.edu.cn", "work_for": "同济大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "李磊", "email": "2432917@tongji.edu.cn", "work_for": "同济大学", "country": "中国" } ], "ds_managers": [ { "true_name": "李磊", "email": "2432917@tongji.edu.cn", "work_for": "同济大学", "country": "中国" } ], "category": "灾害" }