Performance Prediction of Cement Stabilized Soil Incorporating Solid Waste and Propylene Fiber

Ding, Zhiqing; Wang, Yufei; Fu, Guihai; Wang, Yan; Xie, Cheng; Zhao, Xiangming; Lu, Xiwen; Wang, Xiangyu

doi:10.3390/ma15124250

Cited by 12 publications

(5 citation statements)

References 59 publications

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“…Himouri et al [24] showed that the anti-drying shrinkage, mechanical strength and water absorption performance of stabilized soil improved with Date Palm fibers and cement were better than those of solely using cement. The above study found that the mechanical properties of cement-stabilized soil incorporated with fibers were enhanced [19][20][21][22][23][24]. In addition, in some research, the strength deterioration of cement-stabilized soil reinforced with fibers was reduced, and the durability was enhanced [5,[25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 81%

“…Thus, some researchers have synergistically used fibers and inorganic stabilized agents to treat river silt. Zhang et al [19,20], Munoz et al [21], Sahlabadi et al [22] and Lu et al [23] found that the mechanical properties of cement-stabilized soil incorporating fibers were improved compared with using cement alone. Himouri et al [24] showed that the anti-drying shrinkage, mechanical strength and water absorption performance of stabilized soil improved with Date Palm fibers and cement were better than those of solely using cement.…”

Section: Introductionmentioning

confidence: 99%

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Experimental Study on the Strength Deterioration and Mechanism of Stabilized River Silt Reinforced with Cement and Alginate Fibers

Wang,

et al. 2024

Materials

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River silt deposited by water in coastal areas is unsuitable for engineering construction. Thus, the in situ stabilization treatment of river silt as the bearing layer has been an important research area in geotechnical engineering. The strength degradation behavior and mechanism of stabilized river silt reinforced with cement and alginate fibers (AFCS) in different engineering environments are crucial for engineering applications. Therefore, freeze–thaw (F–T) cycle tests, wetting-drying (W–D) cycle tests, water immersion tests and seawater erosion tests were conducted to explore the strength attenuation of stabilized river silt reinforced with the same cement content (9% by wet weight) and different fiber contents (0%, 0.3%, 0.6% and 0.9% by weight of wet soil) and fiber lengths (3 mm, 6 mm and 9 mm). The reinforcement and damage mechanism of AFCS was analyzed by scanning electron microscopy (SEM) imaging. The results indicate that the strength of AFCS was improved from 84% to 180% at 15 F–T cycle tests, and the strength of AFCS was improved by 26% and 40% at 30 W–D cycles, which showed better stability and excellent characteristics owing to the hygroscopic characteristics of alginate fiber arousing the release of calcium and magnesium ions within the alginate. Also, the strength attenuation of AFCS was reduced with the increase in the length and content of alginate fibers. Further, the strength of specimens in the freshwater environment was higher than that in the seawater environment at the same fiber content, and the softening coefficient of AFCS in the freshwater environment was above 0.85, indicating that the AFCS had good water stability. The optimal fiber content was found to be 0.6% based on the unconfined compressive strength (UCS) reduction in specimens cured in seawater and a freshwater environment. And the strength of AFCS was improved by about 10% compared with that of cement-stabilized soil (CS) in a seawater environment. A stable spatial network structure inside the soil was formed, in which the reinforcing effect of fibers was affected by mechanical connection, friction and interfacial bonding. However, noticeable cracks developed in the immersed and F–T specimens. These microscopic characteristics contributed to decreased mechanical properties for AFCS. The results of this research provide a reference for the engineering application of AFCS.

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Section: Introductionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Experimental Study on the Strength Deterioration and Mechanism of Stabilized River Silt Reinforced with Cement and Alginate Fibers

Wang,

et al. 2024

Materials

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“…Through the analysis of the prediction accuracy, stability, and parameter sensitivity of the optimal model, the prediction performance of the machine learning model for salinized frozen soil UCS driven by the three-level characteristic parameters is explored to provide a new reference for further improving the prediction ability of a model for UCS. [39] Note: R: Pearson's correlation coefficient; R 2 : determination coefficient; RMSE: root mean square error; MSE: mean square error; BP: back-propagation; REG: multiple linear regression; RDF: random decision forest; BLR: Bayesian linear regressor; XGB: extreme gradient boosting; KNN: k-nearest neighbor; RF: random forest; TCEF Soils: soils treated with calcium-based additives blended with eco-friendly pozzolans; GB-ML: machine learning using the gradient boosting; Australia-EBCA-Soils: earth building sites in Canberra, Australia; GB-PSO: gradient boosting machines-particle swarm optimizer; CPF Soils: cement stabilized soil incorporating solid waste and propylene fiber; BAS-BP: beetle antennae search BP.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Unconfined Compressive Strength of Salinized Frozen Soil Based on Machine Learning

Zhao,

Bing

2024

Buildings

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Unconfined compressive strength (UCS) is an important parameter of rock and soil mechanical behavior in foundation engineering design and construction. In this study, salinized frozen soil is selected as the research object, and soil GDS tests, ultrasonic tests, and scanning electron microscopy (SEM) tests are conducted. Based on the classification method of the model parameters, 2 macroscopic parameters, 38 mesoscopic parameters, and 19 microscopic parameters are selected. A machine learning model is used to predict the strength of soil considering the three-level characteristic parameters. Four accuracy evaluation indicators are used to evaluate six machine learning models. The results show that the radial basis function (RBF) has the best UCS predictive performance for both the training and testing stages. In terms of acceptable accuracy and stability loss, through the analysis of the gray correlation and rough set of the three-level parameters, the total amount and proportion of parameters are optimized so that there are 2, 16, and 16 macro, meso, and micro parameters in a sequence, respectively. In the simulation of the aforementioned six machine learning models with the optimized parameters, the RBF still performs optimally. In addition, after parameter optimization, the sensitivity proportion of the third-level parameters is more reasonable. The RBF model with optimized parameters proved to be a more effective method for predicting soil UCS. This study improves the prediction ability of the UCS by classifying and optimizing the model parameters and provides a useful reference for future research on salty soil strength parameters in seasonally frozen regions.

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“…Therefore, in recent years, many researchers have investigated materials suitable for replacing natural aggregates with concrete [ 1 , 2 ]. The most commonly used methods involve crushed waste tires [ 3 ], crushed abandoned concrete [ 4 ], and sea sand [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

A Study of the Compressive Behavior of Recycled Rubber Concrete Reinforced with Hybrid Fibers

Zheng

et al. 2023

Materials

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With the development of the automotive industry, a large amount of waste rubber is produced every year. The application and development of recycled rubber concrete (RRC) can effectively reduce ‘black pollution’ caused by waste rubber. However, the addition of recycled rubber particles can lead to a decrease in the compressive behavior of concrete. Previous research has demonstrated that by preventing crack growth, fiber addition can increase the strength and ductility of concrete. In this work, a total of 28 RRC mixes are designed, and the compressive behavior of RRC reinforced by steel fibers (SFs) and glass fibers (GFs) is investigated. The workability of fresh RRC can be negatively impacted by an increase in both fiber contents, with the GF content having a more notable effect. With the addition of fibers, the maximum increase rates for the compressive strength, elastic modulus, strain at peak stress, and compressive toughness were 27%, 8%, 45%, and 152%, respectively. A constitutive model is concurrently put forward to forecast the stress–strain curves of RRC with various fiber contents. These findings indicate that the maximum improvement in compressive behavior is achieved when the GF content was 0.4% and the SF content was 1.2%. The proposed constitutive model can be used to predict the stress–strain curve of hybrid fiber-reinforced recycled rubber concrete (HFRRRC).

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Performance Prediction of Cement Stabilized Soil Incorporating Solid Waste and Propylene Fiber

Cited by 12 publications

References 59 publications

Experimental Study on the Strength Deterioration and Mechanism of Stabilized River Silt Reinforced with Cement and Alginate Fibers

Experimental Study on the Strength Deterioration and Mechanism of Stabilized River Silt Reinforced with Cement and Alginate Fibers

Prediction of the Unconfined Compressive Strength of Salinized Frozen Soil Based on Machine Learning

A Study of the Compressive Behavior of Recycled Rubber Concrete Reinforced with Hybrid Fibers

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