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Multiclass stand-alone and ensemble machine learning algorithms utilised to classify soils based on their physico-chemical characteristics

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Journal of Rock Mechanics and Geotechnical Engineering 2022年第2期14卷 603-615页

作者： Eyo Eyo samuel abbey Faculty of Environment and Technology Department of Geography and Environmental ManagementCivil Engineering ClusterUniversity of the West of EnglandBristolBS161QYUK Fugro GB Marine Geotechnical Services Limited WallingfordOX109RBUK

This study has provided an approach to classify soil using machine *** elements of stand-alone machine learning algorithms(*** regression(LR)and artificial neural network(ANN)),decision tree ensembles(*** forest(DF)and decision jungle(DJ)),and meta-ensemble models(*** ensemble(SE)and voting ensemble(VE))were used to classify soils based on their intrinsic physico-chemical ***,the multiclass prediction was carried out across multiple cross-validation(CV)methods,*** validation split(TVS),k-fold cross-validation(KFCV),and Monte Carlo cross-validation(MCCV).Results indicated that the soils’clay fraction(CF)had the most influence on the multiclass prediction of natural soils’plasticity while specific surface and carbonate content(CC)possessed the least within the nature of the dataset used in this ***-alone machine learning models(LR and ANN)produced relatively less accurate predictive performance(accuracy of 0.45,average precision of 0.5,and average recall of 0.44)compared to tree-based models(accuracy of 0.68,average precision of 0.71,and recall rate of 0.68),while the meta-ensembles(SE and VE)outperformed(accuracy of 0.75,average precision of 0.74,and average recall rate of 0.72)all the models utilised for multiclass *** analysis of the meta-ensembles proved their capacities to discriminate between soil classes across the methods of CV *** learning training and validation using MCCV and KFCV methods enabled better prediction while also ensuring that the dataset was not overfitted by the machine learning *** confirmation of this phenomenon was depicted by the continuous rise of the cumulative lift curve(LC)of the best performing models when using the MCCV ***,this study demonstrated that soil’s physico-chemical properties do have a direct influence on plastic behaviour and,therefore,can be relied upon to classify soils.

关键词： Soil classification Physico-chemistry Soil plasticity Machine learning Logistic regression(LR) Machine learning ensembles Artificial neural network(ANN)

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The use of fine portions from construction and demolition waste for expansive soil stabilization: A review

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Frontiers of Structural and Civil Engineering 2022年第7期16卷 803-816页

作者： Mgboawaji Claude UJILE samuel Jonah abbey Civil Engineering Faculty of Environment and TechnologyUniversity of the West of EnglandBristol BS161QYUK Geotechnical Engineering Faculty of Environment and TechnologyUniversity of the West of EnglandBristol BS161QYUK

Construction and demolition waste(CDW)are the largest waste products in the world today and competes as a viable recycled additive material in place of natural *** to the increase in compressive strength of different mix proportions of CDW,it is also considered for reuse in concrete and subbase *** study shows the effect of CDW in expansive soil *** chemical and mechanical properties of these materials have shown that they are capable of developing compressive strength properties for replacement of cement with significant reduction in carbon *** inherent compositional properties of recycled CDW compared in this review suggests that CDW have good filler properties in highly expansive *** of crushed brick and recycled aggregates characterised based on chemical properties of different replacement ratios suggests that CDW of good-quality aggregates reduces swell potential of expansive soils and increased mechanical strength in pavement construction.

关键词： mixed fine portions construction and demolition waste expansive soil soil stabilization

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