Exploring the effect of basalt fibers on maximum deviator stress and failure deformation of silty soils using ANN, SVM and FL supported by experimental data

Ndepete, Cyrille Prosper; Sert, Sedat; Beycioğlu, Ahmet; Katanalp, Burak Yiğit; Eren, Ezgi; Bağrıaçık, Baki; Topolinski, Syzmon

doi:10.1016/j.advengsoft.2022.103211

Cited by 14 publications

(2 citation statements)

References 52 publications

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“…Their experiment confirmed that the KELM-GA model proposed in their study has better prediction performance compared to the other four machine learning algorithms. Ndepete et al [ 55 ] investigated the effect of fiber size, fiber quantity, water content, and cell pressure on the maximum deflection stress (MDS) and failure deformation (FD) of basalt fiber (BF)-reinforced unsaturated silty soils using different intelligence techniques (artificial neural network (ANN), support vector machine (SVM) and fuzzy logic (FL)). In general, it was experimentally observed that the addition of BF increased the MDS, which generally corresponds to the shear strength of soils.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Predictive Modelling of Basalt Fiber Reinforced Concrete Splitting Tensile Strength Using Ensemble Machine Learning Methods and SHAP Approach

et al. 2023

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Basalt fibers are a type of reinforcing fiber that can be added to concrete to improve its strength, durability, resistance to cracking, and overall performance. The addition of basalt fibers with high tensile strength has a particularly favorable impact on the splitting tensile strength of concrete. The current study presents a data set of experimental results of splitting tests curated from the literature. Some of the best-performing ensemble learning techniques such as Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Random Forest, and Categorical Boosting (CatBoost) have been applied to the prediction of the splitting tensile strength of concrete reinforced with basalt fibers. State-of-the-art performance metrics such as the root mean squared error, mean absolute error and the coefficient of determination have been used for measuring the accuracy of the prediction. The impact of each input feature on the model prediction has been visualized using the Shapley Additive Explanations (SHAP) algorithm and individual conditional expectation (ICE) plots. A coefficient of determination greater than 0.9 could be achieved by the XGBoost algorithm in the prediction of the splitting tensile strength.

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Section: Introductionmentioning

confidence: 99%

Interpretable Predictive Modelling of Basalt Fiber Reinforced Concrete Splitting Tensile Strength Using Ensemble Machine Learning Methods and SHAP Approach

et al. 2023

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“…Many studies have been carried out on concretes in which basalt fiber is used as an admixture. However, the use of basalt fiber additives in soil-related studies is relatively low [21]. Kale et al [16] used three different lengths of fiber (12 mm, 18 mm, and 24 mm) to investigate the effect of basalt fiber additive on improving soil properties.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Different Fiber Reinforcement on Bearing Capacity under Strip Foundation on the Sand Soil: An Experimental Investigation

Aksu Alcan,

Çelik

2023

Applied Sciences

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The aim of this study is to investigate the bearing capacity-settlement behavior of strip footing settling on sand soil randomly reinforced with glass fiber, basalt fiber, macromesh fiber, and four different hybrid fiber additives in which these fibers are used together. Model tests were carried out in the laboratory on the strip footing and placed on the unreinforced and reinforced sand with different fibers. In the study, model tests were carried out on seven types of randomly reinforced soils by using glass, basalt, macrame, and mixtures of these fibers as reinforcement. In the model tests, two different fiber contents, 1% and 2%, and two different fiber lengths, 24 mm and 48 mm, were used. Tests were carried out with Dr = 30% and 50% relative density, and reinforcement depths 1B, 2B, and 3B were selected. In addition, the photographs taken during the test were analyzed with the particle image velocimetry (PIV) method and the displacements on the soil were examined. As a result of the reinforced and unreinforced model tests, the highest ultimate bearing capacity was measured as 680 kPa from the tests with Dr = 50% relative density, 48 mm length, 2% contents, and 3B depth macromesh fiber reinforced. In hybrid fibers, the highest ultimate bearing capacity was measured as 495 kPa, with Dr = 50% relative density, 48 mm length, 2% contents, and 2D depth micromesh and basalt fiber-reinforced tests. In the reinforced tests, it was concluded that the most effective fiber on bearing capacity is macromesh fiber. It can be seen that in the PIV analysis, as the fiber additive increased, the settlements made by the foundation decreased under the same pressure. It has also been observed that adding reinforcement to the soil transfers the stresses occurring in the soil to a wider area.

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Optimizing the Performance Characteristics of SBS - Geopolymer Modified Bitumen: A Data-Driven Approach with ANN and Design Expert Software

Ahmedzade,

Katanalp,

Tastan

et al. 2024

Communications in Computer and Information Science

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Exploring the effect of basalt fibers on maximum deviator stress and failure deformation of silty soils using ANN, SVM and FL supported by experimental data

Cited by 14 publications

References 52 publications

Interpretable Predictive Modelling of Basalt Fiber Reinforced Concrete Splitting Tensile Strength Using Ensemble Machine Learning Methods and SHAP Approach

Interpretable Predictive Modelling of Basalt Fiber Reinforced Concrete Splitting Tensile Strength Using Ensemble Machine Learning Methods and SHAP Approach

The Effect of Different Fiber Reinforcement on Bearing Capacity under Strip Foundation on the Sand Soil: An Experimental Investigation

Optimizing the Performance Characteristics of SBS - Geopolymer Modified Bitumen: A Data-Driven Approach with ANN and Design Expert Software

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