A Hybrid RBF-PSO Framework for Real-Time Temperature Field Prediction and Hydration Heat Parameter Inversion in Mass Concrete Structures
This study proposes an RBF-PSO hybrid framework for efficient inversion analysis of hydration heat parameters in mass concrete temperature fields, addressing the computational inefficiency and accuracy limitations of traditional methods. By integrating a Radial Basis Function (RBF) surrogate model w...
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MDPI AG
2025-06-01
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| author | Shi Zheng Lifen Lin Wufeng Mao Yanhong Wang Jinsong Liu Yili Yuan |
| author_facet | Shi Zheng Lifen Lin Wufeng Mao Yanhong Wang Jinsong Liu Yili Yuan |
| author_sort | Shi Zheng |
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| description | This study proposes an RBF-PSO hybrid framework for efficient inversion analysis of hydration heat parameters in mass concrete temperature fields, addressing the computational inefficiency and accuracy limitations of traditional methods. By integrating a Radial Basis Function (RBF) surrogate model with Particle Swarm Optimization (PSO), the method reduces reliance on costly finite element simulations while maintaining global search capabilities. Three objective functions—integral-type (<i>F</i><sub>1</sub>), feature-driven (<i>F</i><sub>2</sub>), and hybrid (<i>F</i><sub>3</sub>)—were systematically compared using experimental data from a C40 concrete specimen under controlled curing. The hybrid F<sub>3</sub>, incorporating Dynamic Time Warping (DTW) for elastic time alignment and feature penalties for engineering-critical metrics, achieved superior performance with a 74% reduction in the prediction error (mean MAE = 1.0 °C) and <2% parameter identification errors, resolving the phase mismatches inherent in F<sub>2</sub> and avoiding F<sub>1</sub>’s prohibitive computational costs (498 FEM calls). Comparative benchmarking against non-surrogate optimizers (PSO, CMA-ES) confirmed a 2.8–4.6× acceleration while maintaining accuracy. Sensitivity analysis identified the ultimate adiabatic temperature rise as the dominant parameter (78% variance contribution), followed by synergistic interactions between hydration rate parameters, and indirect coupling effects of boundary correction coefficients. These findings guided a phased optimization strategy, as follows: prioritizing high-precision calibration of dominant parameters while relaxing constraints on low-sensitivity variables, thereby balancing accuracy and computational efficiency. The framework establishes a closed-loop “monitoring-simulation-optimization” system, enabling real-time temperature prediction and dynamic curing strategy adjustments for heat stress mitigation. Robustness analysis under simulated sensor noise (σ ≤ 2.0 °C) validated operational reliability in field conditions. Validated through multi-sensor field data, this work advances computational intelligence applications in thermomechanical systems, offering a robust paradigm for parameter inversion in large-scale concrete structures and multi-physics coupling problems. |
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| institution | OA Journals |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-06-01 |
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| series | Buildings |
| spelling | doaj-art-8abe178e08fd465593bfdfab9f18b7522025-08-20T02:35:47ZengMDPI AGBuildings2075-53092025-06-011513223610.3390/buildings15132236A Hybrid RBF-PSO Framework for Real-Time Temperature Field Prediction and Hydration Heat Parameter Inversion in Mass Concrete StructuresShi Zheng0Lifen Lin1Wufeng Mao2Yanhong Wang3Jinsong Liu4Yili Yuan5Guangzhou Metro Design & Research Institute Co., Ltd., Guangzhou 510010, ChinaGuangzhou Metro Design & Research Institute Co., Ltd., Guangzhou 510010, ChinaGuangzhou Metro Design & Research Institute Co., Ltd., Guangzhou 510010, ChinaGuangzhou Metro Design & Research Institute Co., Ltd., Guangzhou 510010, ChinaGuangzhou Metro Design & Research Institute Co., Ltd., Guangzhou 510010, ChinaSchool of Architecture and Civil Engineering, Xi’an University of Science and Technology, Xi’an 710055, ChinaThis study proposes an RBF-PSO hybrid framework for efficient inversion analysis of hydration heat parameters in mass concrete temperature fields, addressing the computational inefficiency and accuracy limitations of traditional methods. By integrating a Radial Basis Function (RBF) surrogate model with Particle Swarm Optimization (PSO), the method reduces reliance on costly finite element simulations while maintaining global search capabilities. Three objective functions—integral-type (<i>F</i><sub>1</sub>), feature-driven (<i>F</i><sub>2</sub>), and hybrid (<i>F</i><sub>3</sub>)—were systematically compared using experimental data from a C40 concrete specimen under controlled curing. The hybrid F<sub>3</sub>, incorporating Dynamic Time Warping (DTW) for elastic time alignment and feature penalties for engineering-critical metrics, achieved superior performance with a 74% reduction in the prediction error (mean MAE = 1.0 °C) and <2% parameter identification errors, resolving the phase mismatches inherent in F<sub>2</sub> and avoiding F<sub>1</sub>’s prohibitive computational costs (498 FEM calls). Comparative benchmarking against non-surrogate optimizers (PSO, CMA-ES) confirmed a 2.8–4.6× acceleration while maintaining accuracy. Sensitivity analysis identified the ultimate adiabatic temperature rise as the dominant parameter (78% variance contribution), followed by synergistic interactions between hydration rate parameters, and indirect coupling effects of boundary correction coefficients. These findings guided a phased optimization strategy, as follows: prioritizing high-precision calibration of dominant parameters while relaxing constraints on low-sensitivity variables, thereby balancing accuracy and computational efficiency. The framework establishes a closed-loop “monitoring-simulation-optimization” system, enabling real-time temperature prediction and dynamic curing strategy adjustments for heat stress mitigation. Robustness analysis under simulated sensor noise (σ ≤ 2.0 °C) validated operational reliability in field conditions. Validated through multi-sensor field data, this work advances computational intelligence applications in thermomechanical systems, offering a robust paradigm for parameter inversion in large-scale concrete structures and multi-physics coupling problems.https://www.mdpi.com/2075-5309/15/13/2236RBF-PSOdynamic time warpingfeature penaltiescivil engineering material |
| spellingShingle | Shi Zheng Lifen Lin Wufeng Mao Yanhong Wang Jinsong Liu Yili Yuan A Hybrid RBF-PSO Framework for Real-Time Temperature Field Prediction and Hydration Heat Parameter Inversion in Mass Concrete Structures Buildings RBF-PSO dynamic time warping feature penalties civil engineering material |
| title | A Hybrid RBF-PSO Framework for Real-Time Temperature Field Prediction and Hydration Heat Parameter Inversion in Mass Concrete Structures |
| title_full | A Hybrid RBF-PSO Framework for Real-Time Temperature Field Prediction and Hydration Heat Parameter Inversion in Mass Concrete Structures |
| title_fullStr | A Hybrid RBF-PSO Framework for Real-Time Temperature Field Prediction and Hydration Heat Parameter Inversion in Mass Concrete Structures |
| title_full_unstemmed | A Hybrid RBF-PSO Framework for Real-Time Temperature Field Prediction and Hydration Heat Parameter Inversion in Mass Concrete Structures |
| title_short | A Hybrid RBF-PSO Framework for Real-Time Temperature Field Prediction and Hydration Heat Parameter Inversion in Mass Concrete Structures |
| title_sort | hybrid rbf pso framework for real time temperature field prediction and hydration heat parameter inversion in mass concrete structures |
| topic | RBF-PSO dynamic time warping feature penalties civil engineering material |
| url | https://www.mdpi.com/2075-5309/15/13/2236 |
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