Soil-Cement Mixtures Reinforced with Fibers: A Data-Driven Approach for Mechanical Properties Prediction
The reinforcement of stabilized soils with fibers arises as an interesting technique to overcome the two main limitations of the stabilized soils: the weak tensile/flexural strength and the higher brittleness of the behavior. These types of mixtures require extensive laboratory characterization sinc...
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| Format: | Article |
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MDPI AG
2021-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/17/8099 |
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| author | Joaquim Tinoco António Alberto S. Correia Paulo J. Venda Oliveira |
| author_facet | Joaquim Tinoco António Alberto S. Correia Paulo J. Venda Oliveira |
| author_sort | Joaquim Tinoco |
| collection | DOAJ |
| description | The reinforcement of stabilized soils with fibers arises as an interesting technique to overcome the two main limitations of the stabilized soils: the weak tensile/flexural strength and the higher brittleness of the behavior. These types of mixtures require extensive laboratory characterization since they entail the study of a great number of parameters, which consumes time and resources. Thus, this work presents an alternative approach to predict the unconfined compressive strength (UCS) and the tensile strength of soil-binder-water mixtures reinforced with short fibers, following a Machine Learning (ML) approach. Four ML algorithms (Artificial Neural Networks, Support Vector Machines, Random Forest and Multiple Regression) are explored for mechanical prediction of reinforced soil-binder-water mixtures with fibers. The proposed models are supported on representative databases with approximately 100 records for each type of test (UCS and splitting tensile strength tests) and on the consideration of sixteen properties of the composite material (soil, fibers and binder). The predictive models provide an accurate estimation (R<sup>2</sup> higher than 0.95 for Artificial Neuronal Networks algorithm) of the compressive and the tensile strength of the soil-water-binder-fiber mixtures. Additionally, the results of the proposed models are in line with the main experimental findings, i.e., the great effect of the binder content in compressive and tensile strength, and the significant effect of the type and the fiber properties in the assessment of the tensile strength. |
| first_indexed | 2024-03-10T08:15:58Z |
| format | Article |
| id | doaj.art-c064934a8b954df5bc77ffef1d0b7362 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T08:15:58Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c064934a8b954df5bc77ffef1d0b73622023-11-22T10:21:33ZengMDPI AGApplied Sciences2076-34172021-08-011117809910.3390/app11178099Soil-Cement Mixtures Reinforced with Fibers: A Data-Driven Approach for Mechanical Properties PredictionJoaquim Tinoco0António Alberto S. Correia1Paulo J. Venda Oliveira2ISISE, Department of Civil Engineering, University of Minho, 4710-057 Braga, PortugalCIEPQPF-Chemical Process Engineering and Forest Products Research Centre, Department of Civil Engineering, University of Coimbra, 3004-531 Coimbra, PortugalISISE, Department of Civil Engineering, University of Coimbra, 3004-531 Coimbra, PortugalThe reinforcement of stabilized soils with fibers arises as an interesting technique to overcome the two main limitations of the stabilized soils: the weak tensile/flexural strength and the higher brittleness of the behavior. These types of mixtures require extensive laboratory characterization since they entail the study of a great number of parameters, which consumes time and resources. Thus, this work presents an alternative approach to predict the unconfined compressive strength (UCS) and the tensile strength of soil-binder-water mixtures reinforced with short fibers, following a Machine Learning (ML) approach. Four ML algorithms (Artificial Neural Networks, Support Vector Machines, Random Forest and Multiple Regression) are explored for mechanical prediction of reinforced soil-binder-water mixtures with fibers. The proposed models are supported on representative databases with approximately 100 records for each type of test (UCS and splitting tensile strength tests) and on the consideration of sixteen properties of the composite material (soil, fibers and binder). The predictive models provide an accurate estimation (R<sup>2</sup> higher than 0.95 for Artificial Neuronal Networks algorithm) of the compressive and the tensile strength of the soil-water-binder-fiber mixtures. Additionally, the results of the proposed models are in line with the main experimental findings, i.e., the great effect of the binder content in compressive and tensile strength, and the significant effect of the type and the fiber properties in the assessment of the tensile strength.https://www.mdpi.com/2076-3417/11/17/8099soil-cement mixturesfibersmechanical propertiesmachine learningartificial neural networks |
| spellingShingle | Joaquim Tinoco António Alberto S. Correia Paulo J. Venda Oliveira Soil-Cement Mixtures Reinforced with Fibers: A Data-Driven Approach for Mechanical Properties Prediction Applied Sciences soil-cement mixtures fibers mechanical properties machine learning artificial neural networks |
| title | Soil-Cement Mixtures Reinforced with Fibers: A Data-Driven Approach for Mechanical Properties Prediction |
| title_full | Soil-Cement Mixtures Reinforced with Fibers: A Data-Driven Approach for Mechanical Properties Prediction |
| title_fullStr | Soil-Cement Mixtures Reinforced with Fibers: A Data-Driven Approach for Mechanical Properties Prediction |
| title_full_unstemmed | Soil-Cement Mixtures Reinforced with Fibers: A Data-Driven Approach for Mechanical Properties Prediction |
| title_short | Soil-Cement Mixtures Reinforced with Fibers: A Data-Driven Approach for Mechanical Properties Prediction |
| title_sort | soil cement mixtures reinforced with fibers a data driven approach for mechanical properties prediction |
| topic | soil-cement mixtures fibers mechanical properties machine learning artificial neural networks |
| url | https://www.mdpi.com/2076-3417/11/17/8099 |
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