Fatigue prediction of semi-flexible composite mixture based on damage evolution

Ren, Jiaolong; Xu, Yan; Zhao, Zedong; Chen, Jingchun; Cheng, Yuanyuan; Huang, Jiandong; Yang, Chengxu; Wang, Jian

doi:10.1016/j.conbuildmat.2021.126004

Cited by 35 publications

(19 citation statements)

References 36 publications

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“…Kawashima et al [14] believed that NMA can reduce the number of shrinkage cracks in cement-stabilized macadam bases. In summary, the filling effect of nano-clay reduces the porosity of cement-based materials, thereby reducing the occurrence of cracks [15,16]. However, there are few reports concerningnano-clay preventing the cracking of cement-based materials and the overalllifecycle cracking process of nano-clay cement-based materials.…”

Section: Introductionmentioning

confidence: 99%

Study on the Relationship between Early Shrinkage Cracking and Mechanical Properties of Nano-Clay Cement Mortar Based on Fractal Theory

Zhang

Fang

et al. 2023

Buildings

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In order to study the influence of nano-clay on the crack resistance of cement-based materials, two kinds of nano-metakaolin (NMK) and two kinds of nano-attapulgite clay (NMA) were considered. The early cracking process and mechanical properties of nano-clay cement mortar (NCM) was studied by using a plate knife-edge constraint test. Based on fractal theory, the distribution characteristics of NCM surface cracks were revealed, and the calculation method forNCM maximum crack width was given. The results show that the cracking time of the NMK-3 specimen is 2 and 6 h later than that of NMK-1 and NMA-2, respectively; the smaller the particle size of nano-clay, the earlier the cracking time of the specimen. However, nano-clay effectively inhibited the expansion of mortar cracks, and the cracks on the surface of NCM were thin and sparse. At 28 days, the maximum crack width of NMK-3 was 46.7% and 33.3% lower than that of NMK-1 and NMA-2, respectively. NMK hadthe best improvement effect on the mechanical properties cement mortar. The smaller the particle size, the more pronounced the improvement effect.The flexural strength ratio and compressive strength ratio at 7 and 28 days are 76.7%, 67.4%, and 61.2%, respectively.The distribution of surface cracks on NCM has fractal characteristics, and the fractal dimension of surface cracks is smaller than that of ordinary cement mortar. The larger the particle size of nano-clay, the smaller the fractal dimension of cracks. The quantitative relationship between fracture fractal dimension and NCM elastic modulus and shrinkage tensile stress is established.

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Section: Introductionmentioning

confidence: 99%

Study on the Relationship between Early Shrinkage Cracking and Mechanical Properties of Nano-Clay Cement Mortar Based on Fractal Theory

Zhang

Fang

et al. 2023

Buildings

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“…The relationship between the UCS of concrete and the proportion of the admixture is non-linear, so it cannot be simply calculated by a mathematical formula [ 36 , 37 ]. Many researchers have proposed the use of machine learning to achieve more efficient optimization of the UCS of concrete [ 10 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. Zhang et al proposed a new self-organizing fuzzy neural network (SOFNN) method based on clustering and extreme learning machine (ELM) optimization to overcome the fact that traditional machine learning models are difficult for engineers to understand when predicting the UCS of concrete.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Design of Building Materials: Development of an AI-Based Method for Cement-Slag Concrete Design

Zhu

Zhou

et al. 2022

Materials

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Cement-slag concrete has become one of the most widely used building materials considering its economical advantage and satisfying uniaxial compressive strength (UCS). In this study, an AI-based method for cement-slag concrete design was developed based on the balance of economic and mechanical properties. Firstly, the hyperparameters of random forest (RF), decision tree (DT), and support vector machine (SVM) were tuned by the beetle antennae search algorithm (BAS). The results of the model evaluation showed the RF with the best prediction effect on the UCS of concrete was selected as the objective function of UCS optimization. Afterward, the objective function of concrete cost optimization was established according to the linear relationship between concrete cost and each mixture. The obtained results showed that the weighted method can be used to construct the multi-objective optimization function of UCS and cost for cement-slag concrete, which is solved by the multi-objective beetle antennae search (MOBAS) algorithm. An optimal concrete mixture ratio can be obtained by Technique for Order Preference by Similarity to Ideal Solution. Considering the current global environment trend of “Net Carbon Zero”, the multi-objective optimization design should be proposed based on the objectives of economy-carbon emission-mechanical properties for future studies.

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“…They are widely used in the construction industry because of their early strength, high strength, high mobility, strong durability, and other characteristics [ 3 , 4 , 5 , 6 , 7 ]. A large amount of CO 2 is generated during the cement configuration process, which brings a great burden to the environment [ 8 , 9 , 10 , 11 ]. To reduce resource consumption and ease the burden of carbon emissions on the environment, researchers are looking into replacing some cement with active materials such as fly ash and silica fume [ 5 , 12 , 13 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Compressive Strength for Cement-Based Materials with Metakaolin Based on the Hybrid Machine Learning Method

et al. 2022

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Cement-based materials are widely used in construction engineering because of their excellent properties. With the continuous improvement of the functional requirements of building infrastructure, the performance requirements of cement-based materials are becoming higher and higher. As an important property of cement-based materials, compressive strength is of great significance to its research. In this study, a Random Forests (RF) and Firefly Algorithm (FA) hybrid machine learning model was proposed to predict the compressive strength of metakaolin cement-based materials. The database containing five input parameters (cement grade, water to binder ratio, cement-sand ratio, metakaolin to binder ratio, and superplasticizer) based on 361 samples was employed for the prediction. In this model, FA was used to optimize the hyperparameters, and RF was used to predict the compressive strength of metakaolin cement-based materials. The reliability of the hybrid model was verified by comparing the predicted and actual values of the dataset. The importance of five variables was also evaluated, and the results showed the cement grade has the greatest influence on the compressive strength of metakaolin cement-based materials, followed by the water-binder ratio.

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Fatigue prediction of semi-flexible composite mixture based on damage evolution

Cited by 35 publications

References 36 publications

Study on the Relationship between Early Shrinkage Cracking and Mechanical Properties of Nano-Clay Cement Mortar Based on Fractal Theory

Study on the Relationship between Early Shrinkage Cracking and Mechanical Properties of Nano-Clay Cement Mortar Based on Fractal Theory

Intelligent Design of Building Materials: Development of an AI-Based Method for Cement-Slag Concrete Design

Prediction of the Compressive Strength for Cement-Based Materials with Metakaolin Based on the Hybrid Machine Learning Method

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