Showing 1,201 - 1,220 results of 9,444 for search 'significantly ((((((teer decrease) OR (greater decrease))) OR (we decrease))) OR (mean decrease))', query time: 0.60s Refine Results
  1. 1201

    Model generalisation validation error analysis. by Hongqi Wang (2208238)

    Published 2024
    “…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”
  2. 1202

    Empirical model prediction error analysis. by Hongqi Wang (2208238)

    Published 2024
    “…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”
  3. 1203

    Fitting curve parameters. by Hongqi Wang (2208238)

    Published 2024
    “…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”
  4. 1204

    Test instrument. by Hongqi Wang (2208238)

    Published 2024
    “…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”
  5. 1205

    Empirical model establishment process. by Hongqi Wang (2208238)

    Published 2024
    “…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”
  6. 1206

    Model prediction error trend chart. by Hongqi Wang (2208238)

    Published 2024
    “…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”
  7. 1207

    Basic physical parameters of red clay. by Hongqi Wang (2208238)

    Published 2024
    “…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”
  8. 1208

    BP neural network structure diagram. by Hongqi Wang (2208238)

    Published 2024
    “…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”
  9. 1209

    Structure diagram of GBDT model. by Hongqi Wang (2208238)

    Published 2024
    “…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”
  10. 1210

    Model prediction error analysis index. by Hongqi Wang (2208238)

    Published 2024
    “…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”
  11. 1211

    Fitting curve parameter table. by Hongqi Wang (2208238)

    Published 2024
    “…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”
  12. 1212

    Model prediction error analysis. by Hongqi Wang (2208238)

    Published 2024
    “…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”
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