Cycle-resolved friction evolution and apparent endpoint energy-to-wear ratios for cementitious-material–rock contacts under debris-flow-like reciprocating sliding
Description
This dataset supports the manuscript entitled “Abrasion resistance of cementitious materials under debris-flow-like sliding: Three-stage friction modelling and apparent endpoint energy-to-wear ratios”. The dataset contains cycle-resolved processed friction coefficients; representative force–displacement loops; endpoint cumulative dissipated energy and wear volume; derived apparent endpoint ratios linking wear volume to cumulative dissipated energy; raw profilometer exports and their file mapping; archived nanoindentation records; logarithmic fitting bins; fitted parameters; comparisons among five segmented friction models; bin-number and edge-placement sensitivity analyses; temporal-holdout and late-window trend results; conditional bootstrap intervals and bandwidth-sensitivity summaries for the fitted friction-model parameters; and Python code for reproducing the fitting, validation and uncertainty analyses. The experimental matrix comprises three cementitious materials (C1 and C3 standard-sand mortars and C2 hardened cement paste) paired with three rock counterparts (D, a rock collected from a debris-flow channel; S1, shale; and S2, sandstone). The contact pairs were tested under Water, kaolin-containing Debris-slurry and selected Dry conditions. The reciprocating abrasion tests were conducted under a normal load of 50 N, a cyclic tangential displacement of ±5000 µm, a loading frequency of 1 Hz and a duration of 3000 cycles. Nineteen primary friction histories are used for model fitting. One independent C3–D–Water repeat is included only for a descriptive assessment of the repeatability of the friction history and cumulative dissipated energy; no independent replicate wear volume is available for this condition. The reported endpoint ratios are therefore contact- and condition-specific descriptors. They should not be interpreted as universal material constants, evidence of process-wide linear energy–wear behaviour or independently validated constitutive parameters. The supplied parameter intervals describe conditional uncertainty within the fitted histories under the selected model and resampling settings. They do not quantify between-specimen variability, sensor-calibration uncertainty or uncertainty across alternative model classes. The complete unmodified high-frequency force–displacement archive, approximately 30 GB in size, is not included because each condition file exceeds 1 GB. It is available from the corresponding author on reasonable request. Cycles within one test, indentation positions within one specimen and multiple line profiles from one wear scar are not independent abrasion-test replicates.
Files
Steps to reproduce
1. Download the complete dataset while preserving its folder structure. 2. Keep Supplementary_Data.xlsx, Supplementary_Fitting_Code.py, Supplementary_Validation_Code.py and Supplementary_Uncertainty_Code.py in the same directory, as provided. 3. Use Python 3.10 or later with NumPy, pandas and openpyxl installed. 4. Run the following commands from that directory: python -B Supplementary_Fitting_Code.py --output-dir reproduced_fits python -B Supplementary_Validation_Code.py --output-dir reproduced_validation python -B Supplementary_Uncertainty_Code.py --draws 5000 --output-dir reproduced_uncertainty 5. The fitting script reproduces the logarithmic binning, comparison of the five segmented models and parameters of the selected four-segment plateau model. 6. The validation script reproduces the temporal-holdout and late-window trend results. 7. The uncertainty script reproduces the conditional parameter intervals and bandwidth-sensitivity summaries. It uses 5000 resamples by default and is substantially more computationally intensive than the other two scripts.
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Funders
- National Key R&D Program of ChinaGrant ID: 2023YFC3008300 and 2023YFC3008305
- Fundamental Research Funds for the Central UniversitiesGrant ID: 25CAFUC04027
- Key Laboratory of Mountain Hazards and Engineering Resilience, Institute of Mountain Hazards and Environment, Chinese Academy of SciencesGrant ID: KLMHER-Z06 and KLMHER-T07
- Sichuan Science and Technology ProgramGrant ID: 2025ZNSFSC1344