Materials · 2019 · 49 citations · 20 references
ArchitectureCivil EngineeringMaterials EngineeringBuilding MaterialsMachine LearningMaterials CharacterizationCementitious MaterialsConcrete MaterialsKernel MethodsMaterial PerformanceMaterial Waste ManagementSupport Vector MachinesCompressive StrengthEnvironmental PollutionIon PermeabilityLeast Squares Support Vector MachinesMortar DesignSlag-based Cementitious MaterialsSupport Vector ClassificationSupport Vector Machine Classification
There is a universally accepted view that environmental pollution should be controlled while improving cement mortar natural abilities. The purpose of this study is to develop a green cement mortar that has better compressive strength and anti-chloride ion permeability. Two industrial wastes, lithium-slag and slag, were added to cement mortar, and the role of lithium-slag was to activate slag. In addition, to save economic and time costs, this paper also used the least-squares support vector machine (LS-SVM) method to predict the property changes of cementitious-based materials. Then multiple natural abilities of samples, including compressive strength, anti-chloride ion permeability, and fluidity, were tested. In addition, LS-SVM and traditional support vector machine (SVM) were used to train and forecast the performance, including compressive strength. The results show that lithium-slag can activate slag to improve the compressive strength, anti-chloride ion permeability of mortar, and LS-SVM sharpens accuracy by 11% compared to SVM.
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