Design optimization of cement grouting material based on adaptive boosting algorithm and simplicial homology global optimization

Ren, Jiaolong; Zhao, Hongbo; Zhang, Linlin; Zhao, Zedong; Xu, Yan; Cheng, Yuanyuan; Wang, Meng; Chen, Jingchun; Jian, Wang

doi:10.1016/j.jobe.2022.104049

Cited by 35 publications

(17 citation statements)

References 49 publications

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“…The results have shown that the incorporation of nano-SiO 2 , nano-CaCO 3 , carbon nano-tubes, nano-TiO 2 , etc., can improve the crack resistance of cement-based materials [3][4][5][6][7], but nano-silica, carbon nano-tubes, and other nano-materials are difficult to prepare and expensive, and it is difficult to promote their application [2]. Nano-clays such as nano-metakaolinand nano-attapulgite claynot only have the advantages notedabove [5][6][7][8][9][10][11], but also have abundant reserves and low prices. They can also promote hydration reaction, improve the cohesion of cement slurryand the internal particle characteristics of cement mortar, and make its microstructure denser.…”

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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“…Finding new materials to replace some of the cement used in concrete is a necessary way to ensure the sustainable development of the concrete industry [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. At present, the use of fly ash, blast furnace slag, metakaolin, and other mineral admixtures to replace part of the cement used in concrete is the main solution to alleviate the large resource consumption and negative impact on the environment in the cement production process [ 17 , 18 , 19 , 20 , 21 , 22 ]. Blast furnace slag is the most common mineral admixture and a type of industrial waste slag discharged from the blast furnace when smelting pig iron, so it has the characteristics of low environmental damage and low price [ 23 , 24 , 25 ].…”

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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“…Concrete is made up of cementitious material, aggregate, water, admixture, and mineral admixture following a certain proportion by uniform mixing, compaction molding, curing hardening, and becoming a kind of artificial stone [1][2][3][4][5]. It is one of the most important civil building materials at present [6][7][8][9][10][11][12][13]. Concrete not only has the characteristics of abundant raw materials, low price, and a simple manufacturing process but also has the characteristics of high compressive strength and good durability [6,[14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Compressive Strength of the Cement-Fly Ash–Slag Ternary Concrete Using the Firefly Algorithm (FA) and Random Forest (RF) Hybrid Machine-Learning Method

et al. 2022

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Concrete is the most widely used material in construction. It has the characteristics of strong plasticity, good economy, high safety, and good durability. As a kind of structural material, concrete must have sufficient strength to resist various loads. At the same time, due to the brittleness of concrete, compressive strength is the most important mechanical property of concrete. To solve the disadvantages of the low efficiency of the traditional concrete compressive strength prediction methods, this study proposes a firefly algorithm (FA) and random forest (RF) hybrid machine-learning method to predict the compressive strength of concrete. First, a database is built based on the data of published articles. The dataset in the database contains eight input variables (cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age) and one output variable (concrete compressive strength). Then, the correlation of the eight input variables was analyzed, and the results showed that there was no high correlation between the input variables; thus, they could be used as input variables to predict the compressive strength of concrete. Next, this study used the FA algorithm to optimize the hyperparameters of RF to obtain better hyperparameters. Finally, we verified that the FA and RF hybrid machine-learning model proposed in this study can predict the compressive strength of concrete with high accuracy by analyzing the R values and RSME values of the training set and test set and comparing the predicted value and actual value of the training set and test machine.

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Design optimization of cement grouting material based on adaptive boosting algorithm and simplicial homology global optimization

Cited by 35 publications

References 49 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

Predicting the Compressive Strength of the Cement-Fly Ash–Slag Ternary Concrete Using the Firefly Algorithm (FA) and Random Forest (RF) Hybrid Machine-Learning Method

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