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8 pages, 2106 KiB

Open AccessArticle

Study of Heat Treatment Effect on Mechanical Properties of Epoxy Resin Reinforced with Fiber Glass

by Zhenbo Lan, Jiangang Deng, Zhuolin Xu, Zhu Ye and Yu Nie

Polymers 2023, 15(12), 2734; https://doi.org/10.3390/polym15122734 - 19 Jun 2023

Cited by 2 | Viewed by 1524

Abstract

In this paper, mechanical properties of the diglycidyl ether of bisphenol A epoxy resin (EP) reinforced with a 20% fiber glass (GF) with layered structure after high temperature aging are studied. Tensile and flexural stress–strain curves of the GF/EP composite after aging tests [...] Read more.

In this paper, mechanical properties of the diglycidyl ether of bisphenol A epoxy resin (EP) reinforced with a 20% fiber glass (GF) with layered structure after high temperature aging are studied. Tensile and flexural stress–strain curves of the GF/EP composite after aging tests in the temperature range of 85–145 °C in air were measured. Tensile and flexural strength demonstrate gradual decrease with the increase in the aging temperature. The failure mechanism at the micro scale is studied by the scanning electron microscopy. A separation of the GFs and EP matrix and evident pullout of the GFs are observed. Degradation of the mechanical properties is explained by a cross-linking and chain scission of the initial molecular structure of the composite and decrease in the interfacial adhesion force between GFs and EP matrix caused by oxidation of the EP matrix and difference of the GF and EP coefficients of thermal expansion. Full article

(This article belongs to the Special Issue Fiber-Reinforced Composite)

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17 pages, 15422 KiB

Open AccessArticle

Characterization of the Mechanical and Morphological Properties of Cow Dung Fiber-Reinforced Polymer Composites: A Comparative Study with Corn Stalk Fiber Composites and Sisal Fiber Composites

by Siyang Wu, Mingzhuo Guo, Jiale Zhao, Qian Wu, Jian Zhuang and Xinming Jiang

Polymers 2022, 14(22), 5041; https://doi.org/10.3390/polym14225041 - 21 Nov 2022

Cited by 8 | Viewed by 3024

Abstract

Natural fibers and their composites have attracted much attention due to the growing energy crisis and environmental awareness. In this work, a natural lignocellulosic fiber was extracted from cow dung waste and its potential use as reinforcing material in resin-based polymer composites was [...] Read more.

Natural fibers and their composites have attracted much attention due to the growing energy crisis and environmental awareness. In this work, a natural lignocellulosic fiber was extracted from cow dung waste and its potential use as reinforcing material in resin-based polymer composites was evaluated. For this purpose, cow dung fiber-reinforced composites (CDFC) were fabricated, and their mechanical and morphological properties were systematically investigated and compared with corn stalk fiber composites (CSFC) and sisal fiber composites (SFC). The results showed that the addition of cow dung fibers reduced the density of the polymer composites, increased the water absorption, and enhanced the impact strength and shear strength. The highest impact and shear strengths were obtained at 6 wt.% and 9 wt.% of fiber loading, respectively, which increased by 23.8% and 34.6% compared to the composite without the fibers. Further comparisons revealed that at the same fiber addition level, the CDFC exhibited better mechanical properties than the CSFC; notably, the CDFC-3 (adding 3 wt.% of fiber loading) had an impact strength closer to the SFC-3. Furthermore, an SEM analysis suggested that the cow dung fibers exhibited a rough and crinkly surface with more node structures, and presented good interfacial bonding with the composite matrix. This work revealed that cow dung fibers are a promising candidate as reinforcement for resin-based polymer composites, which promotes an alternative application for cow dung waste resources in the automotive components field. Full article

(This article belongs to the Special Issue Fiber-Reinforced Composite)

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12 pages, 2611 KiB

Open AccessArticle

Study on the Expansion and Compression Resistance of 3D-Textile-Reinforced Self-Stressing Concrete

by Xinyu Lu, Boxin Wang, Jiahuan Guo and Tianqi Zhang

Polymers 2022, 14(20), 4336; https://doi.org/10.3390/polym14204336 - 14 Oct 2022

Viewed by 1289

Abstract

Textile-reinforced concrete (TRC), as a kind of high-crack-resistance and high-corrosion-resistance material, has been widely studied. The current research has begun the exploration of the change of textile form, such as 3D-textile-reinforced concrete (3D TRC), and its superior bending performance has been verified. In [...] Read more.

Textile-reinforced concrete (TRC), as a kind of high-crack-resistance and high-corrosion-resistance material, has been widely studied. The current research has begun the exploration of the change of textile form, such as 3D-textile-reinforced concrete (3D TRC), and its superior bending performance has been verified. In order to pursue better mechanical properties, combined with the characteristics of self-stressing concrete and 3D textiles, three-dimensional-textile-reinforced self-stressing concrete (3D-TRSSC) specimens were designed in this research. The expansive and compressive properties of specimens with two types of textiles were tested by self-stress and compressibility tests, and the results showed the compressive property and failure mode of 3D-TRSSC were improved compared with 2D-TRSSC and SSC: the increase in compressive strength was 16.3% and 35.1%, respectively. In order to explain the improvement of the compressive strength of the 3D-TRSSC specimens, the triaxial self-stress state analysis of the compressive specimen was carried out, and then a set of calculation methods based on deformation analysis was designed to explain the upward displacement of the necking position of the TRSSC compressive specimen. The theoretical results and experimental data were 27.2 mm and 28–30 mm, respectively. In addition, the improvement of the compressive strength of the 3D-TRSSC specimens relative to that of the 2D-TRSSC specimen was predicted. The calculation results were highly consistent with the predicted values. Full article

(This article belongs to the Special Issue Fiber-Reinforced Composite)

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23 pages, 4923 KiB

Open AccessArticle

Application of Ensemble Machine Learning Methods to Estimate the Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete

by Madiha Anjum, Kaffayatullah Khan, Waqas Ahmad, Ayaz Ahmad, Muhammad Nasir Amin and Afnan Nafees

Polymers 2022, 14(18), 3906; https://doi.org/10.3390/polym14183906 - 19 Sep 2022

Cited by 12 | Viewed by 2255

Abstract

In this study, compressive strength (CS) of fiber-reinforced nano-silica concrete (FRNSC) was anticipated using ensemble machine learning (ML) approaches. Four types of ensemble ML methods were employed, including gradient boosting, random forest, bagging regressor, and AdaBoost regressor, to achieve the study’s aims. The [...] Read more.

In this study, compressive strength (CS) of fiber-reinforced nano-silica concrete (FRNSC) was anticipated using ensemble machine learning (ML) approaches. Four types of ensemble ML methods were employed, including gradient boosting, random forest, bagging regressor, and AdaBoost regressor, to achieve the study’s aims. The validity of employed models was tested and compared using the statistical tests, coefficient of determination (R²), and k-fold method. Moreover, a Shapley Additive Explanations (SHAP) analysis was used to observe the interaction and effect of input parameters on the CS of FRNSC. Six input features, including fiber volume, coarse aggregate to fine aggregate ratio, water to binder ratio, nano-silica, superplasticizer to binder ratio, and specimen age, were used for modeling. In predicting the CS of FRNSC, it was observed that gradient boosting was the model of lower accuracy and the AdaBoost regressor had the highest precision in forecasting the CS of FRNSC. However, the performance of random forest and the bagging regressor was also comparable to that of the AdaBoost regressor model. The R² for the gradient boosting, random forest, bagging regressor, and AdaBoost regressor models were 0.82, 0.91, 0.91, and 0.92, respectively. Also, the error values of the models further validated the exactness of the ML methods. The average error values for the gradient boosting, random forest, bagging regressor, and AdaBoost regressor models were 5.92, 4.38, 4.24, and 3.73 MPa, respectively. SHAP study discovered that the coarse aggregate to fine aggregate ratio shows a greater negative correlation with FRNSC’s CS. However, specimen age affects FRNSC CS positively. Nano-silica, fiber volume, and the ratio of superplasticizer to binder have both positive and deleterious effects on the CS of FRNSC. Employing these methods will promote the building sector by presenting fast and economical methods for calculating material properties and the impact of raw ingredients. Full article

(This article belongs to the Special Issue Fiber-Reinforced Composite)

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11 pages, 3311 KiB

Open AccessArticle

Effect of Polycarboxylic Grinding Aid on Cement Chemistry and Properties

by Jianyu Yang, Guanglin Li, Weijun Yang and Junfeng Guan

Polymers 2022, 14(18), 3905; https://doi.org/10.3390/polym14183905 - 19 Sep 2022

Cited by 2 | Viewed by 2628

Abstract

In view of the disadvantages of polycarboxylic acid grinding aids, such as poor reinforcement effect and cumbersome synthesis process, a new type of polycarboxylic acid grinding aid was prepared to meet the requirements of multifunctional admixture for cement concrete. The polycarboxylate grinding aid [...] Read more.

In view of the disadvantages of polycarboxylic acid grinding aids, such as poor reinforcement effect and cumbersome synthesis process, a new type of polycarboxylic acid grinding aid was prepared to meet the requirements of multifunctional admixture for cement concrete. The polycarboxylate grinding aid (PC) was prepared using acrylic acid, sodium allyl sulfonate, and isoprenol polyoxyethylene ether (TPEG) as raw materials, and ammonium persulfate as initiator in the nitrogen atmosphere. The effect of PC and its compound with triethanolamine (TEA) and triisopropanolamine (TIPA) on cement particle size and strength, and hydration process and structures of hydrated products were investigated. Moreover, the grinding mechanism of grinding aids was also proposed. The results indicate that the PC has good performance in both grinding and high-efficiency water-reducing. The average particle diameter of cement was reduced by 3.65 μm when 0.03 wt% of PC was added as grinding aid. Moreover, a high initial fluidity of the cement paste, 290 mm, could be reached when 0.08 wt% of PC was added. The fluidity loss of cement paste after 30 min and 60 min was 265 mm and 260 mm, respectively. After PC compounding with TEA and TIPA, 4.07 μm and 4.7 μm of the average particle size of the cement can be reduced, respectively. Based on the investigations on the hydration rate of cement hydration, the phases, and the microstructures of the hardened slurry, it could be concluded that grinding aids can change the hydration process of cement and improve the morphologies and structures of hydration products without influence on the type of hydrated products. Note that the compounded grinding aids, such as PC with TEA or PC with TIPA, can more effectively enhance the early and late strength of cement. This shows excellent comprehensive performance. In this study, a new type of polycarboxylic acid grinding aid was prepared to meet the requirements of the versatility of cement concrete additives, and to simplify the synthesis process, reduce production costs, improve the grinding effect, and improve the performance of cement concrete. Full article

(This article belongs to the Special Issue Fiber-Reinforced Composite)

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19 pages, 14141 KiB

Open AccessArticle

Compressive Strength Estimation of Steel-Fiber-Reinforced Concrete and Raw Material Interactions Using Advanced Algorithms

by Kaffayatullah Khan, Waqas Ahmad, Muhammad Nasir Amin, Ayaz Ahmad, Sohaib Nazar and Anas Abdulalim Alabdullah

Polymers 2022, 14(15), 3065; https://doi.org/10.3390/polym14153065 - 29 Jul 2022

Cited by 23 | Viewed by 2597

Abstract

Steel-fiber-reinforced concrete (SFRC) has been introduced as an effective alternative to conventional concrete in the construction sector. The incorporation of steel fibers into concrete provides a bridging mechanism to arrest cracks, improve the post-cracking behavior of concrete, and transfer stresses in concrete. Artificial [...] Read more.

Steel-fiber-reinforced concrete (SFRC) has been introduced as an effective alternative to conventional concrete in the construction sector. The incorporation of steel fibers into concrete provides a bridging mechanism to arrest cracks, improve the post-cracking behavior of concrete, and transfer stresses in concrete. Artificial intelligence (AI) approaches are in use nowadays to predict concrete properties to conserve time and money in the construction industry. Accordingly, this study aims to apply advanced and sophisticated machine-learning (ML) algorithms to predict SFRC compressive strength. In the current work, the applied ML approaches were gradient boosting, random forest, and XGBoost. The considered input variables were cement, fine aggregates (sand), coarse aggregates, water, silica fume, super-plasticizer, fly ash, steel fiber, fiber diameter, and fiber length. Previous studies have not addressed the effects of raw materials on compressive strength in considerable detail, leaving a research gap. The integration of a SHAP analysis with ML algorithms was also performed in this paper, addressing a current research need. A SHAP analysis is intended to provide an in-depth understanding of the SFRC mix design in terms of its strength factors via complicated, nonlinear behavior and the description of input factor contributions by assigning a weighing factor to each input component. The performances of all the algorithms were evaluated by applying statistical checks such as the determination coefficient (R²), the root mean square error (RMSE), and the mean absolute error (MAE). The random forest ML approach had a higher, i.e., 0.96, R² value with fewer errors, producing higher precision than other models with lesser R² values. The SFRC compressive strength could be anticipated by applying the random forest ML approach. Further, it was revealed from the SHapley Additive exPlanations (SHAP) analysis that cement content had the highest positive influence on the compressive strength of SFRC. In this way, the current study is beneficial for researchers to effectively and quickly evaluate SFRC compressive strength. Full article

(This article belongs to the Special Issue Fiber-Reinforced Composite)

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18 pages, 6435 KiB

Open AccessArticle

Mechanical Properties of Nano-SiO₂ Reinforced Geopolymer Concrete under the Coupling Effect of a Wet–Thermal and Chloride Salt Environment

by Qingqing Jin, Peng Zhang, Jingjiang Wu and Dehao Sha

Polymers 2022, 14(11), 2298; https://doi.org/10.3390/polym14112298 - 5 Jun 2022

Cited by 18 | Viewed by 2059

Abstract

In this study, the mechanical behaviors of nano-SiO₂ reinforced geopolymer concrete (NS-GPC) under the coupling effect of a wet–thermal and chloride salt environment were investigated through a series of basic experiments, and a simulation on the coupling effect of a wet–thermal and [...] Read more.

In this study, the mechanical behaviors of nano-SiO₂ reinforced geopolymer concrete (NS-GPC) under the coupling effect of a wet–thermal and chloride salt environment were investigated through a series of basic experiments, and a simulation on the coupling effect of a wet–thermal and chloride salt environment and SEM test were also included. During the experiments for the coupling effect of the wet–thermal and chloride salt environment, an environment simulation test chamber was utilized to simulate the wet–thermal and chloride salt environment, in which the parameters of relative humidity, temperature, mass fraction of NaCl solution and action time were set as 100%, 45 °C, 5% and 60 d, respectively. The content of nano-SiO₂ (NS) particles added in geopolymer concrete (GPC) were 0, 0.5%, 1.0%, 1.5% and 2.0%. The result indicated that the mechanical properties of NS reinforced GPC decreased under the coupling effect of the wet–thermal and chloride salt environment compared to the control group in the natural environment. When the NS content was 1.5%, the cube and splitting tensile strength, elastic modulus and impact toughness of GPC under the coupling environment of wet–thermal and chloride salt were decreased by 9.7%, 9.8%, 19.2% and 44.4%, respectively, relative to that of the GPC under the natural environment. The addition of NS improved the mechanical properties of GPC under the coupling effect of the wet–thermal and chloride salt environment. Compared to the control group without NS, the maximum increment in cube compressive strength, splitting tensile strength and elastic modulus of NS–GPC under the coupling effect of the wet–thermal and chloride salt environment due to the incorporation of NS reached 25.8%, 9.6% and 17.2%, respectively. Specifically, 1.5% content of NS increased the impact toughness, impact numbers of initial crack and the ultimate failure of GPC by 122.3%, 109% and 109.5%, respectively. Full article

(This article belongs to the Special Issue Fiber-Reinforced Composite)

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Review

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24 pages, 8403 KiB

Open AccessReview

Activation Mechanism of Coal Gangue and Its Impact on the Properties of Geopolymers: A Review

by Ruicong Han, Xiaoning Guo, Junfeng Guan, Xianhua Yao and Ying Hao

Polymers 2022, 14(18), 3861; https://doi.org/10.3390/polym14183861 - 15 Sep 2022

Cited by 35 | Viewed by 3532

Abstract

Coal gangue is one of the industrial solid wastes that may harm the human body through the ecosystem for a long time. Using coal gangue in geopolymer preparation can effectively reduce cement output and meet the sustainability requirements. In this paper, the physical [...] Read more.

Coal gangue is one of the industrial solid wastes that may harm the human body through the ecosystem for a long time. Using coal gangue in geopolymer preparation can effectively reduce cement output and meet the sustainability requirements. In this paper, the physical and chemical characteristics, including the heavy metal content, of coal gangue from different producing areas are described. Then, the mechanism of physical activation (mechanical and thermal activation), chemical activation, and compound activation of coal gangue are illustrated. The machinability, as well as the mechanical, microscopic, and toxicity consolidation properties of geopolymers prepared from coal gangue, are summarized and analyzed. The results indicate that the coal gangue geopolymers can have higher mobility and mechanical strength than cement-based composites by adjusting high calcium element material, alkali activator content, Na₂SiO₃ modulus, and curing condition. After physical activation, coal gangue is used in geopolymer preparation with a chemical activator (alkali excitation agent), which effectively forms a three-dimensional silicon aluminate polymer network. The pore structure is dense, the physical fixation and chemical bonding are strengthened, and the solidification and adsorption of heavy metal ions are improved. Further, it can also be applied to solidifying radioactive waste, which is following the future development direction. Full article

(This article belongs to the Special Issue Fiber-Reinforced Composite)

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