Predictive Modeling of Shear Strength of Enzyme-Induced Calcium Carbonate Precipitation (EICP)-Solidified Rubber–Clay Mixtures Using Machine Learning Algorithms
The development of reliable predictive models for soil behavior represents a crucial advancement in geotechnical engineering, particularly for optimizing material compositions and reducing experimental uncertainties. Traditional experimental approaches for determining the optimal rubber particle siz...
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| Veröffentlicht in: | Polymers Jg. 17; H. 7; S. 976 |
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| Abstract | The development of reliable predictive models for soil behavior represents a crucial advancement in geotechnical engineering, particularly for optimizing material compositions and reducing experimental uncertainties. Traditional experimental approaches for determining the optimal rubber particle size and content are often resource-intensive, time-consuming, and subject to significant variability. In this study, the shear strength of clay mixed with rubber particles solidified by the Enzyme-Induced Calcium Carbonate Precipitation (EICP) technique was investigated and predictively modeled using a machine learning algorithm. The effects of different rubber contents and particle sizes on the shear strength of the clay were analyzed experimentally, and a hybrid model of a convolutional neural network (CNN) and long short-term memory (LSTM) network optimized based on the crown porcupine optimization (CPO) algorithm was proposed to predict the shear strength of the EICP-treated clay mixed with rubber particles. The superiority of the CPO-CNN-LSTM model in predicting shear strength was verified by comparing multiple machine learning algorithms. The results show that the addition of rubber particles significantly improves the shear strength of the clay, especially at a 5% rubber content. The coefficient of determination (R2) of the CPO-CNN-LSTM model on the training and test datasets reaches 0.98 and 0.97, respectively, which exhibit high prediction accuracy and generalization ability. |
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| AbstractList | The development of reliable predictive models for soil behavior represents a crucial advancement in geotechnical engineering, particularly for optimizing material compositions and reducing experimental uncertainties. Traditional experimental approaches for determining the optimal rubber particle size and content are often resource-intensive, time-consuming, and subject to significant variability. In this study, the shear strength of clay mixed with rubber particles solidified by the Enzyme-Induced Calcium Carbonate Precipitation (EICP) technique was investigated and predictively modeled using a machine learning algorithm. The effects of different rubber contents and particle sizes on the shear strength of the clay were analyzed experimentally, and a hybrid model of a convolutional neural network (CNN) and long short-term memory (LSTM) network optimized based on the crown porcupine optimization (CPO) algorithm was proposed to predict the shear strength of the EICP-treated clay mixed with rubber particles. The superiority of the CPO-CNN-LSTM model in predicting shear strength was verified by comparing multiple machine learning algorithms. The results show that the addition of rubber particles significantly improves the shear strength of the clay, especially at a 5% rubber content. The coefficient of determination (R2) of the CPO-CNN-LSTM model on the training and test datasets reaches 0.98 and 0.97, respectively, which exhibit high prediction accuracy and generalization ability. The development of reliable predictive models for soil behavior represents a crucial advancement in geotechnical engineering, particularly for optimizing material compositions and reducing experimental uncertainties. Traditional experimental approaches for determining the optimal rubber particle size and content are often resource-intensive, time-consuming, and subject to significant variability. In this study, the shear strength of clay mixed with rubber particles solidified by the Enzyme-Induced Calcium Carbonate Precipitation (EICP) technique was investigated and predictively modeled using a machine learning algorithm. The effects of different rubber contents and particle sizes on the shear strength of the clay were analyzed experimentally, and a hybrid model of a convolutional neural network (CNN) and long short-term memory (LSTM) network optimized based on the crown porcupine optimization (CPO) algorithm was proposed to predict the shear strength of the EICP-treated clay mixed with rubber particles. The superiority of the CPO-CNN-LSTM model in predicting shear strength was verified by comparing multiple machine learning algorithms. The results show that the addition of rubber particles significantly improves the shear strength of the clay, especially at a 5% rubber content. The coefficient of determination ( ) of the CPO-CNN-LSTM model on the training and test datasets reaches 0.98 and 0.97, respectively, which exhibit high prediction accuracy and generalization ability. The development of reliable predictive models for soil behavior represents a crucial advancement in geotechnical engineering, particularly for optimizing material compositions and reducing experimental uncertainties. Traditional experimental approaches for determining the optimal rubber particle size and content are often resource-intensive, time-consuming, and subject to significant variability. In this study, the shear strength of clay mixed with rubber particles solidified by the Enzyme-Induced Calcium Carbonate Precipitation (EICP) technique was investigated and predictively modeled using a machine learning algorithm. The effects of different rubber contents and particle sizes on the shear strength of the clay were analyzed experimentally, and a hybrid model of a convolutional neural network (CNN) and long short-term memory (LSTM) network optimized based on the crown porcupine optimization (CPO) algorithm was proposed to predict the shear strength of the EICP-treated clay mixed with rubber particles. The superiority of the CPO-CNN-LSTM model in predicting shear strength was verified by comparing multiple machine learning algorithms. The results show that the addition of rubber particles significantly improves the shear strength of the clay, especially at a 5% rubber content. The coefficient of determination (R [sup.2]) of the CPO-CNN-LSTM model on the training and test datasets reaches 0.98 and 0.97, respectively, which exhibit high prediction accuracy and generalization ability. The development of reliable predictive models for soil behavior represents a crucial advancement in geotechnical engineering, particularly for optimizing material compositions and reducing experimental uncertainties. Traditional experimental approaches for determining the optimal rubber particle size and content are often resource-intensive, time-consuming, and subject to significant variability. In this study, the shear strength of clay mixed with rubber particles solidified by the Enzyme-Induced Calcium Carbonate Precipitation (EICP) technique was investigated and predictively modeled using a machine learning algorithm. The effects of different rubber contents and particle sizes on the shear strength of the clay were analyzed experimentally, and a hybrid model of a convolutional neural network (CNN) and long short-term memory (LSTM) network optimized based on the crown porcupine optimization (CPO) algorithm was proposed to predict the shear strength of the EICP-treated clay mixed with rubber particles. The superiority of the CPO-CNN-LSTM model in predicting shear strength was verified by comparing multiple machine learning algorithms. The results show that the addition of rubber particles significantly improves the shear strength of the clay, especially at a 5% rubber content. The coefficient of determination (R2) of the CPO-CNN-LSTM model on the training and test datasets reaches 0.98 and 0.97, respectively, which exhibit high prediction accuracy and generalization ability.The development of reliable predictive models for soil behavior represents a crucial advancement in geotechnical engineering, particularly for optimizing material compositions and reducing experimental uncertainties. Traditional experimental approaches for determining the optimal rubber particle size and content are often resource-intensive, time-consuming, and subject to significant variability. In this study, the shear strength of clay mixed with rubber particles solidified by the Enzyme-Induced Calcium Carbonate Precipitation (EICP) technique was investigated and predictively modeled using a machine learning algorithm. The effects of different rubber contents and particle sizes on the shear strength of the clay were analyzed experimentally, and a hybrid model of a convolutional neural network (CNN) and long short-term memory (LSTM) network optimized based on the crown porcupine optimization (CPO) algorithm was proposed to predict the shear strength of the EICP-treated clay mixed with rubber particles. The superiority of the CPO-CNN-LSTM model in predicting shear strength was verified by comparing multiple machine learning algorithms. The results show that the addition of rubber particles significantly improves the shear strength of the clay, especially at a 5% rubber content. The coefficient of determination (R2) of the CPO-CNN-LSTM model on the training and test datasets reaches 0.98 and 0.97, respectively, which exhibit high prediction accuracy and generalization ability. |
| Audience | Academic |
| Author | Ma, Qiang Shu, Hang Li, Meng Xi, Lei |
| AuthorAffiliation | 1 Hubei Key Laboratory of Environmental Geotechnology and Ecological Remediation for Lake & River, Hubei University of Technology, Wuhan 430068, China; maqiang927@163.com (Q.M.); lmeng1014@163.com (M.L.); leixi@whut.edu.cn (L.X.) 3 Hubei Provincial Ecological Road Engineering Technology Research Center, Hubei University of Technology, Wuhan 430068, China 2 Key Laboratory of Intelligent Health Perception and Ecological Restoration of Rivers and Lakes, Ministry of Education, Hubei University of Technology, Wuhan 430068, China |
| AuthorAffiliation_xml | – name: 3 Hubei Provincial Ecological Road Engineering Technology Research Center, Hubei University of Technology, Wuhan 430068, China – name: 2 Key Laboratory of Intelligent Health Perception and Ecological Restoration of Rivers and Lakes, Ministry of Education, Hubei University of Technology, Wuhan 430068, China – name: 1 Hubei Key Laboratory of Environmental Geotechnology and Ecological Remediation for Lake & River, Hubei University of Technology, Wuhan 430068, China; maqiang927@163.com (Q.M.); lmeng1014@163.com (M.L.); leixi@whut.edu.cn (L.X.) |
| Author_xml | – sequence: 1 givenname: Qiang orcidid: 0000-0001-7335-4182 surname: Ma fullname: Ma, Qiang – sequence: 2 givenname: Meng surname: Li fullname: Li, Meng – sequence: 3 givenname: Hang orcidid: 0000-0002-4773-4007 surname: Shu fullname: Shu, Hang – sequence: 4 givenname: Lei surname: Xi fullname: Xi, Lei |
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| Cites_doi | 10.1111/sum.12571 10.1016/j.catena.2019.104451 10.1016/j.trgeo.2024.101235 10.1139/T08-070 10.1061/(ASCE)1090-0241(1997)123:4(295) 10.1007/s12665-023-11352-w 10.1080/19648189.2019.1679671 10.1007/s11440-017-0574-9 10.1061/(ASCE)GT.1943-5606.0001302 10.1016/j.clay.2009.01.020 10.3390/su151612174 10.1016/j.powtec.2015.07.026 10.1016/j.conbuildmat.2023.131887 10.1016/j.knosys.2023.111257 10.1680/jenge.19.00125 10.3311/PPci.9539 10.3141/1619-06 10.4236/ijg.2012.31012 10.1016/j.enggeo.2009.03.002 10.1016/j.jclepro.2021.128205 10.1007/s11440-021-01176-y 10.1016/j.jclepro.2020.122627 10.1016/j.mtcomm.2023.106403 10.1061/(ASCE)GT.1943-5606.0001973 10.3390/app11041949 10.1002/eqe.2171 10.1080/19386362.2016.1277829 10.3389/feart.2023.1270102 10.1016/j.clay.2018.01.035 10.1016/j.jobe.2017.01.001 10.3390/buildings12050613 10.1080/01490450701436505 10.1016/j.aei.2017.02.005 10.1007/s12517-023-11470-6 10.1016/j.egyr.2022.11.130 10.1038/s41598-018-38361-1 10.1007/s40999-016-0057-7 10.1007/s10706-017-0161-8 10.1016/j.resconrec.2018.10.042 10.1016/j.conbuildmat.2015.07.166 10.1016/j.apgeog.2023.103035 10.1680/jgeot.15.P.168 10.1016/j.enggeo.2021.106374 10.1061/(ASCE)GT.1943-5606.0002480 10.1061/(ASCE)MT.1943-5533.0000696 10.1007/s12145-023-00950-8 10.1080/1064119X.2017.1297877 10.1680/jgein.18.00009 10.1016/j.enggeo.2006.09.002 10.1016/j.conbuildmat.2022.126526 10.1016/j.sandf.2015.02.018 10.1007/s11356-021-16442-5 10.1061/(ASCE)MT.1943-5533.0001804 |
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| Keywords | shear strength enzyme-induced carbonate precipitation predictive modeling rubber soil solidification machine learning rubber–clay mixtures |
| Language | English |
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| References | Hoppe (ref_27) 1998; 1619 Salimi (ref_33) 2024; 46 Montoya (ref_47) 2015; 141 Kim (ref_51) 2008; 45 Wang (ref_3) 2017; 35 Ikeagwuani (ref_41) 2023; 16 Dao (ref_31) 2020; 188 Wang (ref_21) 2022; 26 Neupane (ref_6) 2015; 55 Tang (ref_15) 2009; 106 Almajed (ref_5) 2018; 144 Mohamed (ref_48) 2024; 284 ref_24 Lu (ref_49) 2017; 32 Cui (ref_29) 2021; 16 Zhao (ref_54) 2022; 323 Tsang (ref_20) 2012; 41 Krishnan (ref_9) 2021; 147 Jiang (ref_1) 2020; 36 Saberian (ref_28) 2018; 12 Mashiri (ref_22) 2013; 25 Qiu (ref_50) 2022; 8 Xi (ref_42) 2023; 35 Yadav (ref_53) 2017; 9 Whiffin (ref_46) 2007; 24 Zhang (ref_2) 2024; 11 Almajed (ref_11) 2020; 274 Gao (ref_52) 2022; 17 Ahmad (ref_38) 2024; 7 Hosseinpour (ref_12) 2017; 35 Hajiazizi (ref_25) 2019; 53 Talamkhani (ref_39) 2023; 2023 ref_34 ref_30 Cui (ref_55) 2017; 12 Oliveira (ref_7) 2017; 29 Rezaei (ref_13) 2012; 3 Cetin (ref_17) 2006; 88 Meng (ref_4) 2021; 294 ref_37 Tajabadipour (ref_57) 2016; 61 Soltani (ref_18) 2018; 25 Shamshirband (ref_43) 2015; 284 Tajdini (ref_16) 2017; 15 Bosscher (ref_19) 1997; 123 Hamdan (ref_10) 2016; 66 An (ref_56) 2024; 83 ref_44 Borji (ref_32) 2023; 158 Zhang (ref_36) 2023; 392 Yilmaz (ref_14) 2009; 44 Yuan (ref_8) 2022; 29 Saberian (ref_26) 2019; 141 Cardoso (ref_45) 2018; 156 Niyogi (ref_35) 2023; 16 Angelin (ref_23) 2015; 95 Wang (ref_40) 2021; 315 |
| References_xml | – volume: 36 start-page: 185 year: 2020 ident: ref_1 article-title: Bio-mediated Soil Improvement: The Way Forward publication-title: Soil Use Manag. doi: 10.1111/sum.12571 – volume: 188 start-page: 104451 year: 2020 ident: ref_31 article-title: A Spatially Explicit Deep Learning Neural Network Model for the Prediction of Landslide Susceptibility publication-title: CATENA doi: 10.1016/j.catena.2019.104451 – volume: 46 start-page: 101235 year: 2024 ident: ref_33 article-title: Predicting the Precipitated Calcium Carbonate and Unconfined Compressive Strength of Bio-Mediated Sands through Robust Hybrid Optimization Algorithms publication-title: Transp. Geotech. doi: 10.1016/j.trgeo.2024.101235 – volume: 45 start-page: 1457 year: 2008 ident: ref_51 article-title: Sand–Rubber Mixtures (Large Rubber Chips) publication-title: Can. Geotech. J. doi: 10.1139/T08-070 – volume: 123 start-page: 295 year: 1997 ident: ref_19 article-title: Design of Highway Embankments Using Tire Chips publication-title: J. Geotech. Geoenviron. Eng. doi: 10.1061/(ASCE)1090-0241(1997)123:4(295) – volume: 83 start-page: 31 year: 2024 ident: ref_56 article-title: Mechanical Behaviour and Microstructure of Granite Residual Bio-Cemented Soil by Microbially Induced Calcite Precipitation with Different Cementation–Solution Concentrations publication-title: Environ. Earth Sci. doi: 10.1007/s12665-023-11352-w – volume: 26 start-page: 779 year: 2022 ident: ref_21 article-title: Research on Hoop Capacity of Composite Foundation of Discarded Rubber Tires publication-title: Eur. J. Environ. Civ. Eng. doi: 10.1080/19648189.2019.1679671 – volume: 12 start-page: 971 year: 2017 ident: ref_55 article-title: Influence of Cementation Level on the Strength Behaviour of Bio-Cemented Sand publication-title: Acta Geotech. doi: 10.1007/s11440-017-0574-9 – volume: 141 start-page: 04015019 year: 2015 ident: ref_47 article-title: Stress-Strain Behavior of Sands Cemented by Microbially Induced Calcite Precipitation publication-title: J. Geotech. Geoenviron. Eng. doi: 10.1061/(ASCE)GT.1943-5606.0001302 – volume: 7 start-page: 217 year: 2024 ident: ref_38 article-title: Unconfined Compressive Strength Prediction of Stabilized Expansive Clay Soil Using Machine Learning Techniques. Multiscale and Multidiscip publication-title: Model. Exp. Des. – volume: 44 start-page: 166 year: 2009 ident: ref_14 article-title: Gypsum: An Additive for Stabilization of Swelling Clay Soils publication-title: Appl. Clay Sci. doi: 10.1016/j.clay.2009.01.020 – ident: ref_24 doi: 10.3390/su151612174 – volume: 284 start-page: 560 year: 2015 ident: ref_43 article-title: Hybrid Intelligent Model for Approximating Unconfined Compressive Strength of Cement-Based Bricks with Odd-Valued Array of Peat Content (0–29%) publication-title: Powder Technol. doi: 10.1016/j.powtec.2015.07.026 – volume: 392 start-page: 131887 year: 2023 ident: ref_36 article-title: Efficient Machine Learning Method for Evaluating Compressive Strength of Cement Stabilized Soft Soil publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2023.131887 – volume: 284 start-page: 111257 year: 2024 ident: ref_48 article-title: Crested Porcupine Optimizer: A New Nature-Inspired Metaheuristic publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2023.111257 – volume: 11 start-page: 29 year: 2024 ident: ref_2 article-title: Environmental Impact and Mechanical Improvement of Microbially Induced Calcium Carbonate Precipitation-Treated Coal Fly Ash–Soil Mixture publication-title: Environ. Geotech. doi: 10.1680/jenge.19.00125 – volume: 61 start-page: 322 year: 2016 ident: ref_57 article-title: Effect of Rubber Tire Chips-Sand Mixtures on Performance of Geosynthetic Reinforced Earth Walls publication-title: Period. Polytech. Civ. Eng. doi: 10.3311/PPci.9539 – volume: 1619 start-page: 47 year: 1998 ident: ref_27 article-title: Field Study of Shredded-Tire Embankment publication-title: Transp. Res. Rec. J. Transp. Res. Board doi: 10.3141/1619-06 – volume: 3 start-page: 105 year: 2012 ident: ref_13 article-title: Geotechnical Properties of Problematic Soils Emphasis on Collapsible Cases publication-title: Int. J. Geosci. doi: 10.4236/ijg.2012.31012 – volume: 106 start-page: 68 year: 2009 ident: ref_15 article-title: Analysis of the Railway Heave Induced by Soil Swelling at a Site in Southern France publication-title: Eng. Geol. doi: 10.1016/j.enggeo.2009.03.002 – volume: 315 start-page: 128205 year: 2021 ident: ref_40 article-title: Unconfined Compressive Strength of Bio-Cemented Sand: State-of-the-Art Review and MEP-MC-Based Model Development publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2021.128205 – volume: 16 start-page: 1429 year: 2021 ident: ref_29 article-title: Effect of Waste Rubber Particles on the Shear Behaviour of Bio-Cemented Calcareous Sand publication-title: Acta Geotech. doi: 10.1007/s11440-021-01176-y – volume: 53 start-page: 183 year: 2019 ident: ref_25 article-title: Experimental Study of Sand Slopes Reinforced by Waste Tires publication-title: Int. J. Min. Geo-Eng. – volume: 274 start-page: 122627 year: 2020 ident: ref_11 article-title: Enzyme-Induced Carbonate Precipitation (EICP)-Based Methods for Ecofriendly Stabilization of Different Types of Natural Sands publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2020.122627 – volume: 35 start-page: 106403 year: 2023 ident: ref_42 article-title: Integration of Machine Learning Models and Metaheuristic Algorithms for Predicting Compressive Strength of Waste Granite Powder Concrete publication-title: Mater. Today Commun. doi: 10.1016/j.mtcomm.2023.106403 – volume: 144 start-page: 04018081 year: 2018 ident: ref_5 article-title: Baseline Investigation on Enzyme-Induced Calcium Carbonate Precipitation publication-title: J. Geotech. Geoenviron. Eng. doi: 10.1061/(ASCE)GT.1943-5606.0001973 – volume: 2023 start-page: 3692090 year: 2023 ident: ref_39 article-title: Machine Learning-Based Prediction of Unconfined Compressive Strength of Sands Treated by Microbially-Induced Calcite Precipitation (MICP): A Gradient Boosting Approach and Correlation Analysis publication-title: Adv. Civ. Eng. – ident: ref_34 doi: 10.3390/app11041949 – volume: 41 start-page: 2009 year: 2012 ident: ref_20 article-title: Seismic Isolation for Low-to-medium-rise Buildings Using Granulated Rubber–Soil Mixtures: Numerical Study publication-title: Earthq. Engng. Struct. Dyn. doi: 10.1002/eqe.2171 – volume: 12 start-page: 347 year: 2018 ident: ref_28 article-title: Experimental and Phenomenological Study of the Effects of Adding Shredded Tire Chips on Geotechnical Properties of Peat publication-title: Int. J. Geotech. Eng. doi: 10.1080/19386362.2016.1277829 – ident: ref_30 doi: 10.3389/feart.2023.1270102 – volume: 156 start-page: 96 year: 2018 ident: ref_45 article-title: Effects of Clay’s Chemical Interactions on Biocementation publication-title: Appl. Clay Sci. doi: 10.1016/j.clay.2018.01.035 – volume: 9 start-page: 177 year: 2017 ident: ref_53 article-title: A Study on the Potential Utilization of Crumb Rubber in Cement Treated Soft Clay publication-title: J. Build. Eng. doi: 10.1016/j.jobe.2017.01.001 – ident: ref_37 doi: 10.3390/buildings12050613 – volume: 24 start-page: 417 year: 2007 ident: ref_46 article-title: Microbial Carbonate Precipitation as a Soil Improvement Technique publication-title: Geomicrobiol. J. doi: 10.1080/01490450701436505 – volume: 32 start-page: 139 year: 2017 ident: ref_49 article-title: Intelligent Fault Diagnosis of Rolling Bearing Using Hierarchical Convolutional Network Based Health State Classification publication-title: Adv. Eng. Inform. doi: 10.1016/j.aei.2017.02.005 – volume: 16 start-page: 390 year: 2023 ident: ref_41 article-title: Taguchi Regression Analysis and Constrained Particle Swarm Optimization for Amended Unconfined Compressive Strength (UCS) of Expansive Subgrade Soil publication-title: Arab. J. Geosci. doi: 10.1007/s12517-023-11470-6 – volume: 8 start-page: 15436 year: 2022 ident: ref_50 article-title: Optimized Long Short-Term Memory (LSTM) Network for Performance Prediction in Unconventional Reservoirs publication-title: Energy Rep. doi: 10.1016/j.egyr.2022.11.130 – ident: ref_44 doi: 10.1038/s41598-018-38361-1 – volume: 15 start-page: 949 year: 2017 ident: ref_16 article-title: Effect of Added Waste Rubber on the Properties and Failure Mode of Kaolinite Clay publication-title: Int. J. Civ. Eng. doi: 10.1007/s40999-016-0057-7 – volume: 35 start-page: 1051 year: 2017 ident: ref_12 article-title: Strength and Compressibility Characteristics of a Soft Clay Subjected to Ground Treatment publication-title: Geotech. Geol. Eng. doi: 10.1007/s10706-017-0161-8 – volume: 141 start-page: 301 year: 2019 ident: ref_26 article-title: Estimating the Resilient Modulus of Crushed Recycled Pavement Materials Containing Crumb Rubber Using the Clegg Impact Value publication-title: Resour. Conserv. Recycl. doi: 10.1016/j.resconrec.2018.10.042 – volume: 95 start-page: 525 year: 2015 ident: ref_23 article-title: Effects of Spheroid and Fiber-like Waste-Tire Rubbers on Interrelation of Strength-to-Porosity in Rubberized Cement and Mortars publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2015.07.166 – volume: 158 start-page: 103035 year: 2023 ident: ref_32 article-title: A Novel Approach for Assessing Flood Risk with Machine Learning and Multi-Criteria Decision-Making Methods publication-title: Appl. Geogr. doi: 10.1016/j.apgeog.2023.103035 – volume: 66 start-page: 546 year: 2016 ident: ref_10 article-title: Enzyme-Induced Carbonate Mineral Precipitation for Fugitive Dust Control publication-title: Géotechnique doi: 10.1680/jgeot.15.P.168 – volume: 294 start-page: 106374 year: 2021 ident: ref_4 article-title: Multiple-Phase Enzyme-Induced Carbonate Precipitation (EICP) Method for Soil Improvement publication-title: Eng. Geol. doi: 10.1016/j.enggeo.2021.106374 – volume: 147 start-page: 06021001 year: 2021 ident: ref_9 article-title: Variability in the Unconfined Compressive Strength of EICP-Treated “Standard” Sand publication-title: J. Geotech. Geoenviron. Eng. doi: 10.1061/(ASCE)GT.1943-5606.0002480 – volume: 25 start-page: 1366 year: 2013 ident: ref_22 article-title: Shear and Compressibility Behavior of Sand–Tire Crumb Mixtures publication-title: J. Mater. Civ. Eng. doi: 10.1061/(ASCE)MT.1943-5533.0000696 – volume: 16 start-page: 899 year: 2023 ident: ref_35 article-title: Machine Learning Algorithm for the Shear Strength Prediction of Basalt-Driven Lateritic Soil publication-title: Earth Sci. Inform. doi: 10.1007/s12145-023-00950-8 – volume: 35 start-page: 1135 year: 2017 ident: ref_3 article-title: Review of Ground Improvement Using Microbial Induced Carbonate Precipitation (MICP) publication-title: Mar. Georesources Geotechnol. doi: 10.1080/1064119X.2017.1297877 – volume: 25 start-page: 304 year: 2018 ident: ref_18 article-title: Rubber Powder–Polymer Combined Stabilization of South Australian Expansive Soils publication-title: Geosynth. Int. doi: 10.1680/jgein.18.00009 – volume: 88 start-page: 110 year: 2006 ident: ref_17 article-title: Geotechnical Properties of Tire-Cohesive Clayey Soil Mixtures as a Fill Material publication-title: Eng. Geol. doi: 10.1016/j.enggeo.2006.09.002 – volume: 323 start-page: 126526 year: 2022 ident: ref_54 article-title: Surface Improvement of Scrap Rubber by Microbially Induced Carbonate Precipitation and Its Effect on Mechanical Behavior of Rubberised Mortar publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2022.126526 – volume: 17 start-page: e01416 year: 2022 ident: ref_52 article-title: Study on the Strength Mechanism of Red Clay Improved by Waste Tire Rubber Powder publication-title: Case Stud. Constr. Mater. – volume: 55 start-page: 447 year: 2015 ident: ref_6 article-title: Distribution of Mineralized Carbonate and Its Quantification Method in Enzyme Mediated Calcite Precipitation Technique publication-title: Soils Found. doi: 10.1016/j.sandf.2015.02.018 – volume: 29 start-page: 10332 year: 2022 ident: ref_8 article-title: Mechanical Properties of Na-Montmorillonite-Modified EICP-Treated Silty Sand publication-title: Environ. Sci. Pollut. Res. doi: 10.1007/s11356-021-16442-5 – volume: 29 start-page: 04016263 year: 2017 ident: ref_7 article-title: Effect of Soil Type on the Enzymatic Calcium Carbonate Precipitation Process Used for Soil Improvement publication-title: J. Mater. Civ. Eng. doi: 10.1061/(ASCE)MT.1943-5533.0001804 |
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| SubjectTerms | Algorithms Artificial neural networks Calcium carbonate Civil engineering Clay Data mining Defense mechanisms Enzymes Geotechnical engineering Geotechnology Machine learning Mechanical properties Microorganisms Neural networks Optimization Particle size Performance evaluation Prediction models Rubber Shear strength |
| Title | Predictive Modeling of Shear Strength of Enzyme-Induced Calcium Carbonate Precipitation (EICP)-Solidified Rubber–Clay Mixtures Using Machine Learning Algorithms |
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