{"title":"硫酸盐冻融对再生粗骨料自密实混凝土应力-应变关系的影响:实验和机器学习算法","authors":"","doi":"10.1016/j.conbuildmat.2024.138383","DOIUrl":null,"url":null,"abstract":"<div><p>Based on experimental studies, this paper proposes two efficient machine learning models to evaluate the macroscopic mechanical properties of recycled coarse aggregate self-compacting concrete (RCASCC) after sulfate freeze-thaw action. Initially, the stress-strain curves of RCASCC after sulfate freeze-thaw action were measured, yielding the peak stress (<em>σ</em><sub><em>c</em></sub>), peak strain (<em>ε</em><sub><em>c</em></sub>), and elastic modulus (<em>E</em>) for each group of RCASCC. Among these, a strong linear correlation was observed between the elastic modulus and the peak stress. An RCASCC uniaxial compression behavior model considering the effects of sulfate freeze-thaw cycles has been established. This model is used to predict the stress-strain characteristics of RCASCC under uniaxial compression after exposure to sulfate freeze-thaw cycles. Using the stress-strain data of uniaxial compression test, a machine learning model for RCASCC after sulfate freeze-thaw cycle was developed by using MATLAB. Eight different machine learning algorithms are used to train and test the model, and six performance indicators are used to measure its generalization performance. The three models, RF, ET and GB, exhibit the highest prediction accuracy compared to other machine learning models. The relative importance of strain and Na<sub>2</sub>SO<sub>4</sub> mass fraction is largest and smallest in the three models, RF, ET and GB, respectively. Based on RF, ET and GB models with good predictive performance, we plot the stress-strain curves of the predicted models. The fit is better for the ascending and descending segments of the curves in each group, and worse for the curves near the peak. RF and ET can better predict the macroscopic mechanical properties of RCASCCC under different conditions.</p></div>","PeriodicalId":288,"journal":{"name":"Construction and Building Materials","volume":null,"pages":null},"PeriodicalIF":7.4000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Effect of sulfate freeze-thaw on the stress-strain relationship of recycled coarse aggregate self-compacting concrete: Experimental and machine learning algorithms\",\"authors\":\"\",\"doi\":\"10.1016/j.conbuildmat.2024.138383\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Based on experimental studies, this paper proposes two efficient machine learning models to evaluate the macroscopic mechanical properties of recycled coarse aggregate self-compacting concrete (RCASCC) after sulfate freeze-thaw action. Initially, the stress-strain curves of RCASCC after sulfate freeze-thaw action were measured, yielding the peak stress (<em>σ</em><sub><em>c</em></sub>), peak strain (<em>ε</em><sub><em>c</em></sub>), and elastic modulus (<em>E</em>) for each group of RCASCC. Among these, a strong linear correlation was observed between the elastic modulus and the peak stress. An RCASCC uniaxial compression behavior model considering the effects of sulfate freeze-thaw cycles has been established. This model is used to predict the stress-strain characteristics of RCASCC under uniaxial compression after exposure to sulfate freeze-thaw cycles. Using the stress-strain data of uniaxial compression test, a machine learning model for RCASCC after sulfate freeze-thaw cycle was developed by using MATLAB. Eight different machine learning algorithms are used to train and test the model, and six performance indicators are used to measure its generalization performance. The three models, RF, ET and GB, exhibit the highest prediction accuracy compared to other machine learning models. The relative importance of strain and Na<sub>2</sub>SO<sub>4</sub> mass fraction is largest and smallest in the three models, RF, ET and GB, respectively. Based on RF, ET and GB models with good predictive performance, we plot the stress-strain curves of the predicted models. The fit is better for the ascending and descending segments of the curves in each group, and worse for the curves near the peak. RF and ET can better predict the macroscopic mechanical properties of RCASCCC under different conditions.</p></div>\",\"PeriodicalId\":288,\"journal\":{\"name\":\"Construction and Building Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.4000,\"publicationDate\":\"2024-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Construction and Building Materials\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0950061824035256\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Construction and Building Materials","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950061824035256","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
Effect of sulfate freeze-thaw on the stress-strain relationship of recycled coarse aggregate self-compacting concrete: Experimental and machine learning algorithms
Based on experimental studies, this paper proposes two efficient machine learning models to evaluate the macroscopic mechanical properties of recycled coarse aggregate self-compacting concrete (RCASCC) after sulfate freeze-thaw action. Initially, the stress-strain curves of RCASCC after sulfate freeze-thaw action were measured, yielding the peak stress (σc), peak strain (εc), and elastic modulus (E) for each group of RCASCC. Among these, a strong linear correlation was observed between the elastic modulus and the peak stress. An RCASCC uniaxial compression behavior model considering the effects of sulfate freeze-thaw cycles has been established. This model is used to predict the stress-strain characteristics of RCASCC under uniaxial compression after exposure to sulfate freeze-thaw cycles. Using the stress-strain data of uniaxial compression test, a machine learning model for RCASCC after sulfate freeze-thaw cycle was developed by using MATLAB. Eight different machine learning algorithms are used to train and test the model, and six performance indicators are used to measure its generalization performance. The three models, RF, ET and GB, exhibit the highest prediction accuracy compared to other machine learning models. The relative importance of strain and Na2SO4 mass fraction is largest and smallest in the three models, RF, ET and GB, respectively. Based on RF, ET and GB models with good predictive performance, we plot the stress-strain curves of the predicted models. The fit is better for the ascending and descending segments of the curves in each group, and worse for the curves near the peak. RF and ET can better predict the macroscopic mechanical properties of RCASCCC under different conditions.
期刊介绍:
Construction and Building Materials offers an international platform for sharing innovative and original research and development in the realm of construction and building materials, along with their practical applications in new projects and repair practices. The journal publishes a diverse array of pioneering research and application papers, detailing laboratory investigations and, to a limited extent, numerical analyses or reports on full-scale projects. Multi-part papers are discouraged.
Additionally, Construction and Building Materials features comprehensive case studies and insightful review articles that contribute to new insights in the field. Our focus is on papers related to construction materials, excluding those on structural engineering, geotechnics, and unbound highway layers. Covered materials and technologies encompass cement, concrete reinforcement, bricks and mortars, additives, corrosion technology, ceramics, timber, steel, polymers, glass fibers, recycled materials, bamboo, rammed earth, non-conventional building materials, bituminous materials, and applications in railway materials.