Machine Learning Models for Predicting Freeze–Thaw Damage of Concrete Under Subzero Temperature Curing Conditions Article Swipe

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Yanhua Zhao , Bo Yang , Kai Zhang , Aojun Guo , Yonghui Yu , Su Chen ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.3390/ma18122856

In high-elevation or high-latitude permafrost areas, persistent subzero temperatures significantly impact the freeze–thaw durability of concrete structures. Traditional methods for studying the frost resistance of concrete in permafrost regions do not provide a complete picture for predicting properties, and new approaches are needed using, for example, machine learning algorithms. This study utilizes four machine learning models—Support Vector Machine (SVM), extreme learning machine (ELM), long short-term memory (LSTM), and radial basis function neural network (RBFNN)—to predict freeze–thaw damage factors in concrete under low and subzero temperature conservation conditions. Building on the prediction results, the optimal model is refined to develop a new machine learning model: the Sparrow Search Algorithm-optimized Extreme Learning Machine (SSA-ELM). Furthermore, the SHapley Additive exPlanations (SHAP) value analysis method is employed to interpret this model, clarifying the relationship between factors affecting the freezing resistance of concrete and freeze–thaw damage factors. In conclusion, the empirical formula for concrete freeze–thaw damage is compared and validated against the prediction results from the SSA-ELM model. The study results indicate that the SSA-ELM model offers the most accurate predictions for concrete freeze–thaw resistance compared to the SVM, ELM, LSTM, and RBFNN models. SHAP value analysis quantitatively confirms that the number of freeze–thaw cycles is the most significant input parameter affecting the freeze–thaw damage coefficient of concrete. Comparative analysis shows that the accuracy of the SSA-ELMDE prediction set is improved by 15.46%, 9.19%, 21.79%, and 11.76%, respectively, compared with the prediction results of SVM, ELM, LSTM, and RBF. This parameter positively influences the prediction results for the freeze–thaw damage coefficient. Curing humidity has the least influence on the freeze–thaw damage factor of concrete. Comparing the prediction results with empirical formulas shows that the machine learning model provides more accurate predictions. This introduces a new approach for predicting the extent of freeze–thaw damage to concrete under low and subzero temperature conservation conditions.

Related Topics

Materials Science

Composite Material

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Curing (chemistry) Materials science Composite material

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Type: article
Language: en
Landing Page: https://doi.org/10.3390/ma18122856
PDF: https://www.mdpi.com/1996-1944/18/12/2856/pdf?version=1750155525
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Cited By: 1
References: 56
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DOI: https://doi.org/10.3390/ma18122856

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Title: Machine Learning Models for Predicting Freeze–Thaw Damage of Concrete Under Subzero Temperature Curing Conditions

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Language: en

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Publication year: 2025

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Publication date: 2025-06-17

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Authors: Yanhua Zhao, Bo Yang, Kai Zhang, Aojun Guo, Yonghui Yu, Su Chen

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Landing page: https://doi.org/10.3390/ma18122856

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PDF URL: https://www.mdpi.com/1996-1944/18/12/2856/pdf?version=1750155525

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Concepts: Curing (chemistry), Materials science, Composite material

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Cited by: 1

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referenced_works	https://openalex.org/W4306935894, https://openalex.org/W4310214327, https://openalex.org/W4385861696, https://openalex.org/W4315977451, https://openalex.org/W4389117256, https://openalex.org/W4319335512, https://openalex.org/W4311142532, https://openalex.org/W4320168663, https://openalex.org/W4383336724, https://openalex.org/W4388343276, https://openalex.org/W4386395455, https://openalex.org/W6839895667, https://openalex.org/W4221051647, https://openalex.org/W4292955862, https://openalex.org/W4404932246, https://openalex.org/W4409516359, https://openalex.org/W6880109277, https://openalex.org/W4368367112, https://openalex.org/W4214534528, https://openalex.org/W3026128433, https://openalex.org/W4405093355, https://openalex.org/W4407606126, https://openalex.org/W4408995435, https://openalex.org/W4408562504, https://openalex.org/W4405312764, https://openalex.org/W4379767406, https://openalex.org/W4319437371, https://openalex.org/W4309709481, https://openalex.org/W4317569508, https://openalex.org/W4311571047, https://openalex.org/W4311768397, https://openalex.org/W4207022764, https://openalex.org/W4389917027, https://openalex.org/W4401241647, https://openalex.org/W4318617110, https://openalex.org/W4313593261, https://openalex.org/W4385235923, https://openalex.org/W3207446388, https://openalex.org/W4310725354, https://openalex.org/W4311498863, https://openalex.org/W4388571829, https://openalex.org/W6802727835, https://openalex.org/W4224274569, https://openalex.org/W4376879762, https://openalex.org/W6800592019, https://openalex.org/W3207066068, https://openalex.org/W4283077289, https://openalex.org/W4362704886, https://openalex.org/W4379203926, https://openalex.org/W4322755771, https://openalex.org/W6848688880, https://openalex.org/W4313530577, https://openalex.org/W3198148990, https://openalex.org/W3206453894, https://openalex.org/W4410042186, https://openalex.org/W4285736717
referenced_works_count	56
abstract_inverted_index.a	32, 99, 288
abstract_inverted_index.In	0, 142
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abstract_inverted_index.do	29
abstract_inverted_index.in	26, 78
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abstract_inverted_index.or	2
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abstract_inverted_index.ELM,	184, 240
abstract_inverted_index.RBF.	243
abstract_inverted_index.SHAP	189
abstract_inverted_index.SVM,	183, 239
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abstract_inverted_index.least	260
abstract_inverted_index.model	94, 170, 281
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abstract_inverted_index.(SHAP)	117
abstract_inverted_index.(SVM),	58
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abstract_inverted_index.Search	106
abstract_inverted_index.Vector	56
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abstract_inverted_index.model,	126
abstract_inverted_index.model.	162
abstract_inverted_index.model:	103
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abstract_inverted_index.15.46%,	227
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abstract_inverted_index.Machine	57, 110
abstract_inverted_index.SHapley	114
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abstract_inverted_index.develop	98
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abstract_inverted_index.predict	74
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abstract_inverted_index.refined	96
abstract_inverted_index.regions	28
abstract_inverted_index.results	158, 165, 237, 250, 272
abstract_inverted_index.subzero	7, 83, 303
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abstract_inverted_index.Building	87
abstract_inverted_index.Learning	109
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abstract_inverted_index.accurate	174, 284
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abstract_inverted_index.results,	91
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