该数据集来源于在水、热、力多物理场耦合作用下，小型岩土试样（如黄土）内部三维微结构的原位、高精度动态观测实验。观测系统基于自主研制的多场耦合三维微结构原位扫描装备，由多功能三轴压力室、高精度力学加载模块、可控渗流模块、宽温域温度控制模块与多尺度微米 CT 成像系统组成，可对试样施加围压、轴向应力、渗流压力以及 -20°C至 +60°C的温度场，并在真实应力状态下同步获取微米级三维结构图像。系统在加载与扫描的一体化条件下运行，可在力学加载、渗流、水热耦合以及破坏全过程中，于任意时刻暂停并实施原位扫描，连续记录试样内部孔隙结构、裂隙扩展、颗粒重排等多尺度微结构的实时演化。\n
本数据集可用于重建岩土试样在水–热–力耦合作用下的三维微结构演化过程。通过多时段 CT 体数据与 DVC 位移场，可实现孔隙结构、裂隙发育与局部变形区的可视化识别，并将加载数据、渗流数据与温度响应与结构变化进行耦合分析。该数据集能够支持构建岩土材料的微观–宏观行为数字孪生模型，为揭示破坏机制、校准多尺度数值模拟以及评估水、热、力耦合条件下的岩土稳定性提供关键数据支撑。", "ds_source": "
“水–热–力耦合作用下的三维微结构动态演化过程高精度观测装备”数据集来源于同济大学与长安大学联合研发的多场耦合三维微结构原位观测系统。其中，力学加载数据来自加载模块内的高灵敏度压力传感器与位移传感器，围压加载范围为 0–1 MPa，轴向加载额定载荷为 200 N，位移测量范围为 0–8 mm，加载速度为 0.01–0.2 mm/s。渗流数据由可控渗流模块提供，通过精确控制水头压力并同步监测试样内部孔隙水压变化，用于刻画水-力耦合行为。温度数据由宽温域温控模块采集，该模块集成恒温槽、平流泵和背压阀，可在-20°C至 +60°C范围内对试样施加稳定且可控的温度场，温控精度达到 ±1℃，用于模拟冻融循环、地热梯度变化等复杂环境条件。微结构演化数据来源于微米 CT 扫描系统，全场扫描分辨率为 5–10 μm，局部关键区域可达 0.5–1 μm，能够无损捕捉试样在加载过程中的微观信息。CT数据集的采集时间范围对应相关实验在 2025 年 12 月 10 日 于西北大学大陆动力学实验室完成一次采集。加载相关试验自2025年11月15日后于在长安大学长期运行。", "ds_process_way": "
（1）依据时间戳解析力学加载、渗流与温控模块的原始监测数据，对多源传感器数据进行同步对齐、噪声剔除与滤波处理（采用 50 Hz 低通滤波）。(2) 对微米 CT 原始投影影像使用备用内置的 FDK 锥体束重建算法重建三维体素图像，分别获得 5-10μm 全场扫描，0.5-1μm 局部高分辨率结构数量固定。（3）对重建体数据进行图像去噪、阈值分割（Otsu 或局部阈值）等预处理，将灰度体素转换为固体—孔隙的二值微结构数据。（4）采用数字体相关（DVC）算法对不同时刻的 CT 三维体数据进行相关匹配，求解试样内部的三维位移场与应变场，用于识别孔隙塌落、裂隙萌生与局部变形集中区。（5）将 DVC 位移场与力学、渗流和温度记录进行耦合分析，通过阶段性整理构建试样在加载、峰值、软化与破坏过程中的微结构动态演化序列。（6）依据试样编号、扫描阶段与分辨率对数据文件进行规范命名，并在可视化平台中实现三维微结构及裂隙演化的动态展示。", "ds_quality": "
本数据集在采集与处理过程中采用多重质量控制措施，以保证力学、渗流、温度及三维 CT 数据的精度与可靠性。力学加载与渗流模块的传感器均经检定确认性能正常，并通过时间戳同步、异常值剔除与曲线连续性校验确保数据传输与记录的稳定性。三维 CT 数据采用 FDK 重建计算法，并通过过滤噪声去除加伪影抑制处理提高图像质量；重复扫描验证显示重建灰度一致性误差小于 1-2 灰度级。数字体相关 (DVC) 分析使用留一法验证场位移求解精度，最大 RMSE 小于 0.5 体素，保证微结构演化识别的可靠性。整体而言，本数据集的力学与渗流数据采集可靠性超过 99%，CT 重建及 DVC 位移场计算精度满足微米级结构分析要求，可稳定支撑岩土材料在水–热–力耦合条件下的微结构演化研究与数字孪生建模。", "ds_acq_start_time": "2025-11-15 00:00:00", "ds_acq_end_time": "2025-11-15 00:00:00", "ds_acq_place": "长安大学,西北大学", "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": 47068, "ds_files_count": 0, "ds_format": "*.txt", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "ec414cc9-3c4e-44fd-a296-5b84140a62cb.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "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" ], "quality_level": 0, "publish_time": "2026-05-25 17:00:42", "last_updated": "2026-05-25 17:00:42", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.loess.db7393.2026", "i18n": { "en": { "title": "\"High-Precision Observation Equipment for the Three-Dimensional Microstructural Dynamic Evolution Process under Water-Heat-Force Coupling Effects\" dataset", "ds_format": "*.txt", "ds_source": "
The dataset ‘High-Precision Monitoring Equipment for Three-Dimensional Microstructural Dynamic Evolution under Hydrothermal-Mechanical Coupling’ originates from the multi-field coupled three-dimensional microstructure in-situ observation system jointly developed by Tongji University and Chang'an University. Mechanical loading data is derived from high-sensitivity pressure and displacement sensors within the loading module, with confining pressure ranging from 0–1 MPa, axial loading rated at 200 N, displacement measurement spanning 0–8 mm, and loading rates between 0.01–0.2 mm/s. Seepage data is provided by the controllable seepage module, which precisely regulates head pressure while simultaneously monitoring pore water pressure variations within the specimen to characterise water-force coupling behaviour. Temperature data is acquired via a wide-range temperature control module integrating a thermostatic bath, laminar flow pump, and back-pressure valve. This system applies stable, controllable temperature fields across -20°C to +60°C with ±1°C precision, simulating complex environmental conditions such as freeze-thaw cycles and geothermal gradient variations. Microstructural evolution data originate from a micron-scale CT scanning system. Full-field scanning resolution ranges from 5–10 μm, with local critical regions achievable at 0.5–1 μm, enabling non-destructive capture of microscopic information during specimen loading. The data collection timeframe corresponds to the relevant experiments completed on 10 December 2025 at the Continental Dynamics Laboratory, Northwestern University.", "ds_quality": "
This dataset employs multiple quality control measures during acquisition and processing to ensure the accuracy and reliability of mechanical, seepage, temperature, and three-dimensional CT data. Sensors in both the mechanical loading and seepage modules were calibrated and verified as functioning correctly. Data transmission and recording stability were ensured through timestamp synchronisation, outlier exclusion, and curve continuity verification. Three-dimensional CT data were reconstructed using the FDK algorithm, with image quality enhanced through noise filtering and artefact suppression. Repeat scans demonstrated reconstruction consistency errors below 1–2 grey levels. Digital volume correlation (DVC) analysis employed the leave-one-out method to validate displacement field solution accuracy, achieving a maximum root mean square error (RMSE) below 0.5 voxels, thereby ensuring reliable microstructural evolution identification. Overall, this dataset achieves over 99% reliability in mechanical and seepage data acquisition. The precision of CT reconstruction and DVC displacement field calculations meets micrometre-scale structural analysis requirements, providing stable support for studying microstructural evolution and digital twin modelling of geomaterials under coupled hydrothermal-mechanical conditions.", "ds_ref_way": "", "ds_abstract": "
This dataset originates from in situ, high-precision dynamic observation experiments of three-dimensional microstructures within small geotechnical specimens (such as loess) under coupled multi-physics interactions of water, heat, and force. The observation system is based on an independently developed multi-field coupled three-dimensional microstructure in situ scanning apparatus, comprising a multifunctional triaxial pressure chamber, a high-precision mechanical loading module, a controllable seepage module, a wide-temperature-range thermal control module, and a multi-scale micrometre CT imaging system. This configuration enables the application of confining pressure, axial stress, seepage pressure, and temperature fields ranging from -20°C to +60°C to the specimen, whilst simultaneously acquiring micrometre-scale three-dimensional structural images under real stress conditions. Operating under integrated loading and scanning conditions, the system permits in situ scanning to be paused and initiated at any point during mechanical loading, seepage, hydrothermal coupling, or failure processes. This enables continuous documentation of real-time multiscale microstructural evolution within specimens, including pore structure, fracture propagation, and particle rearrangement.\r\n
This dataset facilitates the reconstruction of three-dimensional microstructural evolution processes in geotechnical specimens subjected to coupled hydrothermal-mechanical loading. Through multi-temporal CT volumetric data and DVC displacement fields, it enables visual identification of pore structures, fracture development, and localised deformation zones. Furthermore, it permits coupled analysis of loading data, seepage data, and temperature responses alongside structural changes. This dataset supports the development of digital twin models capturing the micro-macro behaviour of geomaterials, providing critical data for elucidating failure mechanisms, calibrating multiscale numerical simulations, and assessing geotechnical stability under coupled hydrothermal-mechanical conditions.", "ds_time_res": "", "ds_acq_place": "Chang 'an University, Northwest University", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
(1) Based on the timestamp resolution of the mechanical loading, seepage and thermal control modules' raw monitoring data, synchronous alignment, noise rejection and filtering (using a 50 Hz low-pass filter) were applied to the multi-source sensor data. (2) Reconstruct three-dimensional voxel images from raw micro-CT projection data using the built-in FDK cone-beam reconstruction algorithm, yielding full-field scans at 5–10 μm resolution and locally high-resolution structures at 0.5–1 μm with fixed quantity. (3) Preprocess the reconstructed volumetric data through image denoising and threshold segmentation (Otsu or local thresholding), converting grey-scale voxels into binary microstructural data representing solid-void phases. (4) Employ the Digital Volume Correlation (DVC) algorithm to correlate and match CT three-dimensional volumetric data at different time points, solving for the three-dimensional displacement and strain fields within the specimen. This identifies pore collapse, crack initiation, and localised deformation concentration zones. (5) Coupled analysis of the DVC displacement field with mechanical, seepage, and temperature records is conducted. Through phased organisation, dynamic microstructural evolution sequences during loading, peak stress, softening, and failure stages are reconstructed for the specimen. (6) Data files are standardised according to specimen identification, scanning phase, and resolution. Dynamic visualisation of three-dimensional microstructural and fissure evolution is achieved within the visualisation platform.", "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": [ "水-热-力耦合", "三维微结构", "原位CT扫描", "多尺度结构分析" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "黄土高原" ], "ds_time_tags": [ 2025 ], "ds_contributors": [ { "true_name": "于渤", "email": "bo.yu@chd.edu.cn", "work_for": "长安大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "于渤", "email": "bo.yu@chd.edu.cn", "work_for": "长安大学", "country": "中国" } ], "ds_managers": [ { "true_name": "于渤", "email": "bo.yu@chd.edu.cn", "work_for": "长安大学", "country": "中国" } ], "category": "其他" }