Study on Strength Behavior of Basalt Fiber-Reinforced Loess by Digital Image Technology (DIT) and Scanning Electron Microscope (SEM)

Xu, Jian; Wu, Zhipeng; Chen, Hui; Shao, Longtan; Zhou, Xiangang; Wang, Songhe

doi:10.1007/s13369-021-05787-1

Cited by 53 publications

(24 citation statements)

References 40 publications

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“…The samples were cured in the laboratory environment with a temperature of 20 ± 1 °C and a natural humidity of about 26% for 14, 28, and 60 days. In order to make the results applicable for the field projects, it is better to simulate the real field condition [ 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 ]. In other words, these specific curing times were selected in this research since foundations and structures are often built on lime-based concretes after two to eight weeks of concrete casting [ 21 ].…”

Section: Experimental Program and Methodologymentioning

confidence: 99%

Properties of Lime-Cement Concrete Containing Various Amounts of Waste Tire Powder under Different Ground Moisture Conditions

et al. 2022

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Lime-cement concrete (LCC) is a non-structural concrete in which lime and cement are used as the main binders. However, although LCC has many applications in reducing the settlement of foundations and providing a support layer for shallow foundations, little research has been conducted to evaluate its behaviour in various moisture conditions. Previous researchers have studied the feasibility of using waste tires in conventional concrete to alleviate their negative environmental impacts. However, in field projects, rubber has not been widely used because its application leads to the strength reduction of concrete. In the case of LCC, attaining high strengths is not required and thus application of waste tire particles sounds reasonable. This research evaluated the impact of various rubber powder contents on the fresh, geotechnical and durability properties of LCC at different saturation degrees induced by the capillary action and groundwater level increment, which has not been studied before. The results of more than 320 tests showed that the application of tire powder increases workability and decreases the water absorption of LCC. Moreover, all 60-day cured specimens exposed to 100% saturation degree experienced a strength reduction of less than 10% by using rubber powder contents varying from 0 to 20%. Moreover, increasing the saturation degree from 0 to 100% decreased the average compressive strength by 13.5 and 22% for 60-day cured samples of two different mix designs. The results of this research confirm that LCC containing up to 10% rubber powder could be promisingly used underneath or close to the groundwater table without its strength and geotechnical properties being jeopardized due to rubber employment and/or exposure to ground moisture.

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Section: Experimental Program and Methodologymentioning

confidence: 99%

Properties of Lime-Cement Concrete Containing Various Amounts of Waste Tire Powder under Different Ground Moisture Conditions

et al. 2022

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“…Several porosities exist on the fiber periphery. These entrapped air voids caused by fiber agglomeration remarkably weaken the fiber enhancement on compressive strength [55]. Compared with UCS tests, fibrous material promoted FS results more evidently.…”

Section: Effect Of Polypropylene Fibermentioning

confidence: 89%

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

Ding

Wang

et al. 2022

Materials

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Cement stabilized soil (CSS) yields wide application as a routine cementitious material due to cost-effectiveness. However, the mechanical strength of CSS impedes development. This research assesses the feasible combined enhancement of unconfined compressive strength (UCS) and flexural strength (FS) of construction and demolition (C&D) waste, polypropylene fiber, and sodium sulfate. Moreover, machine learning (ML) techniques including Back Propagation Neural Network (BPNN) and Random Forest (FR) were applied to estimate UCS and FS based on the comprehensive dataset. The laboratory tests were conducted at 7-, 14-, and 28-day curing age, indicating the positive effect of cement, C&D waste, and sodium sulfate. The improvement caused by polypropylene fiber on FS was also evaluated from the 81 experimental results. In addition, the beetle antennae search (BAS) approach and 10-fold cross-validation were employed to automatically tune the hyperparameters, avoiding tedious effort. The consequent correlation coefficients (R) ranged from 0.9295 to 0.9717 for BPNN, and 0.9262 to 0.9877 for RF, respectively, indicating the accuracy and reliability of the prediction. K-Nearest Neighbor (KNN), logistic regression (LR), and multiple linear regression (MLR) were conducted to validate the BPNN and RF algorithms. Furthermore, box and Taylor diagrams proved the BAS-BPNN and BAS-RF as the best-performed model for UCS and FS prediction, respectively. The optimal mixture design was proposed as 30% cement, 20% C&D waste, 4% fiber, and 0.8% sodium sulfate based on the importance score for each variable.

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“…A detailed description of such measurements is presented in several previously published works. These indexes are expressed as follows (Zhou et al, 2021a;Zhou et al, 2021b;Xu et al, 2021):…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Application of a Novel Hybrid Machine Learning Algorithm in Shallow Landslide Susceptibility Mapping in a Mountainous Area

Ghasemian

Shahabi

Shirzadi

et al. 2022

Front. Environ. Sci.

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Landslides can be a major challenge in mountainous areas that are influenced by climate and landscape changes. In this study, we propose a hybrid machine learning model based on a rotation forest (RoF) meta classifier and a random forest (RF) decision tree classifier called RoFRF for landslide prediction in a mountainous area near Kamyaran city, Kurdistan Province, Iran. We used 118 landslide locations and 25 conditioning factors from which their predictive usefulness was measured using the chi-square technique in a 10-fold cross-validation analysis. We used the sensitivity, specificity, accuracy, F1-measure, Kappa, and area under the receiver operating characteristic curve (AUC) to validate the performance of the proposed model compared to the Artificial Neural Network (ANN), Logistic Model Tree (LMT), Best First Tree (BFT), and RF models. The validation results demonstrated that the landslide susceptibility map produced by the hybrid model had the highest goodness-of-fit (AUC = 0.953) and higher prediction accuracy (AUC = 0.919) compared to the benchmark models. The hybrid RoFRF model proposed in this study can be used as a robust predictive model for landslide susceptibility mapping in the mountainous regions around the world.

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Study on Strength Behavior of Basalt Fiber-Reinforced Loess by Digital Image Technology (DIT) and Scanning Electron Microscope (SEM)

Cited by 53 publications

References 40 publications

Properties of Lime-Cement Concrete Containing Various Amounts of Waste Tire Powder under Different Ground Moisture Conditions

Properties of Lime-Cement Concrete Containing Various Amounts of Waste Tire Powder under Different Ground Moisture Conditions

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

Application of a Novel Hybrid Machine Learning Algorithm in Shallow Landslide Susceptibility Mapping in a Mountainous Area

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