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A Two-Stage Hybrid Modeling Strategy for Early-Age Concrete Temperature Prediction Using Decoupled Physical Processes.

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Titel: A Two-Stage Hybrid Modeling Strategy for Early-Age Concrete Temperature Prediction Using Decoupled Physical Processes.
Autoren: Hu, Xiaoyi, Gan, Min, Zhang, Liangliang, Yu, Zhou, Lin, Xin
Quelle: Buildings (2075-5309); Oct2025, Vol. 15 Issue 19, p3479, 21p
Schlagwörter: CONCRETE, HYDRATION, THERMAL stress cracking, FINITE element method, RANDOM forest algorithms, CALORIMETRY
Abstract: Predicting early-age temperature evolution in mass concrete is crucial for controlling thermal cracks. This process involves two distinct physical stages: an initial, hydration-driven heating stage (Stage I) and a subsequent, environment-dominated cooling stage (Stage II). To address this challenge, we propose a novel two-stage hybrid modeling strategy that decouples the underlying physical processes. This approach was developed and validated using a 450-h on-site monitoring dataset. For the deterministic heating phase (Stage I), we employed polynomial regression. For the subsequent stochastic cooling phase (Stage II), a Random Forest algorithm was used to model the complex environmental interactions. The proposed hybrid model was benchmarked against several alternatives, including a physics-based finite element model (FEM) and a single Random Forest model. During the critical cooling stage, our approach demonstrated superior performance, achieving a Root Mean Square Error (RMSE) of 0.24   ° C . This represents a 17.2% improvement over the best-performing single model. Furthermore, cumulative error analysis indicated that the hybrid model maintained a stable and unbiased prediction trend throughout the monitoring period. This addresses a key weakness in single-stage models, where underlying phase-specific errors can accumulate and lead to long-term drift. The proposed framework offers an accurate, robust, and transferable paradigm for modeling other complex engineering processes that exhibit distinct multi-stage characteristics. [ABSTRACT FROM AUTHOR]
Copyright of Buildings (2075-5309) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A Two-Stage Hybrid Modeling Strategy for Early-Age Concrete Temperature Prediction Using Decoupled Physical Processes.
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  Data: <searchLink fieldCode="AR" term="%22Hu%2C+Xiaoyi%22">Hu, Xiaoyi</searchLink><br /><searchLink fieldCode="AR" term="%22Gan%2C+Min%22">Gan, Min</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Liangliang%22">Zhang, Liangliang</searchLink><br /><searchLink fieldCode="AR" term="%22Yu%2C+Zhou%22">Yu, Zhou</searchLink><br /><searchLink fieldCode="AR" term="%22Lin%2C+Xin%22">Lin, Xin</searchLink>
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  Data: Buildings (2075-5309); Oct2025, Vol. 15 Issue 19, p3479, 21p
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  Data: <searchLink fieldCode="DE" term="%22CONCRETE%22">CONCRETE</searchLink><br /><searchLink fieldCode="DE" term="%22HYDRATION%22">HYDRATION</searchLink><br /><searchLink fieldCode="DE" term="%22THERMAL+stress+cracking%22">THERMAL stress cracking</searchLink><br /><searchLink fieldCode="DE" term="%22FINITE+element+method%22">FINITE element method</searchLink><br /><searchLink fieldCode="DE" term="%22RANDOM+forest+algorithms%22">RANDOM forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22CALORIMETRY%22">CALORIMETRY</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Predicting early-age temperature evolution in mass concrete is crucial for controlling thermal cracks. This process involves two distinct physical stages: an initial, hydration-driven heating stage (Stage I) and a subsequent, environment-dominated cooling stage (Stage II). To address this challenge, we propose a novel two-stage hybrid modeling strategy that decouples the underlying physical processes. This approach was developed and validated using a 450-h on-site monitoring dataset. For the deterministic heating phase (Stage I), we employed polynomial regression. For the subsequent stochastic cooling phase (Stage II), a Random Forest algorithm was used to model the complex environmental interactions. The proposed hybrid model was benchmarked against several alternatives, including a physics-based finite element model (FEM) and a single Random Forest model. During the critical cooling stage, our approach demonstrated superior performance, achieving a Root Mean Square Error (RMSE) of 0.24   ° C . This represents a 17.2% improvement over the best-performing single model. Furthermore, cumulative error analysis indicated that the hybrid model maintained a stable and unbiased prediction trend throughout the monitoring period. This addresses a key weakness in single-stage models, where underlying phase-specific errors can accumulate and lead to long-term drift. The proposed framework offers an accurate, robust, and transferable paradigm for modeling other complex engineering processes that exhibit distinct multi-stage characteristics. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Buildings (2075-5309) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.3390/buildings15193479
    Languages:
      – Code: eng
        Text: English
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        PageCount: 21
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      – SubjectFull: THERMAL stress cracking
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      – SubjectFull: FINITE element method
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      – SubjectFull: RANDOM forest algorithms
        Type: general
      – SubjectFull: CALORIMETRY
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      – TitleFull: A Two-Stage Hybrid Modeling Strategy for Early-Age Concrete Temperature Prediction Using Decoupled Physical Processes.
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            NameFull: Hu, Xiaoyi
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            NameFull: Gan, Min
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            – D: 01
              M: 10
              Text: Oct2025
              Type: published
              Y: 2025
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