Of course, the selection should be done randomly. Pozzolanic and hydraulic activity of calcareous fly ash. As was mentioned in Section 3.
The obtained classification accuracy was equal to The chloride migration coefficient in concrete specimens with different contents of high calcium fly ash was experimentally measured. The obtained results are collected in Table 7. Reports of the Machine Learning and Inference Laboratory. The obtained results are collected in Table 7. In both cases, the CfsSubsetEvaluator , provided by Weka, was used to assess the predictive ability of each attribute individually and the degree of redundancy among them, preferring sets of attributes that are highly correlated with the class, but have low inter-correlation.
The following sections present penetration equations for concrete and steel structures for using the tornado missile steel pipe and rod penetration database. Keywords: chloride penetration, concrete, durability, high calcium fly ash, The rules are generated using selected attributes from a database.
The random selection repeated many times can be treated as the basis of a statistical technique called cross-validation. When we have only one database consisting of a very small number of records, the estimation of classification accuracy the measure of the overall performance of the classifier can be done using the n -fold cross-validation, where n is the number of examples in the database. Abstract The aim of the study was to generate rules for the prediction of the chloride resistance of concrete modified with high calcium fly ash using machine learning methods.
Regardless of the representation, both classification rules and decision trees algorithms create hypotheses. Introduction to Machine Learning Determining the relationship between material composition and the chloride resistance of concrete is a difficult and time-consuming process, even in the case of a small dataset, as presented in Table 4. It is clear that for database with a few dozens of instances, this number of attributes is too large. The obtained decision rules determine the conditions concretes have to fulfill to provide appropriate resistance against chloride penetration. However machine learning or, more general, statistical algorithms can support the knowledge discovery at different stages from outlier detection and attribute features selection to knowledge modeling and model validation. Classifier Evaluation So as to evaluate the classifier, i. The random selection repeated many times can be treated as the basis of a statistical technique called cross-validation.
The aim of the study was to generate rules for the prediction of the chloride resistance of concrete modified with high calcium fly ash using machine learning methods. The results of the performed tests were used as the training set to generate rules describing the relation between material composition and the chloride resistance. Multiple methods for rule generation were Concrete penetration database and compared. The rules generated by algorithm J48 from the Weka workbench provided the means for adequate classification of plain concretes and concretes modified with high calcium fly ash as materials of good, acceptable or unacceptable resistance to chloride penetration. The increased use of high calcium fly ash HCFA for partial replacement of Portland cement in concrete could result in a number of environmental benefits reduced consumption of cement clinker, reduced CO 2 emissions during cement production, saving natural resources, Concrete penetration database landfill space and Concrete penetration database costs. The resources of high calcium fly ash are large, it is produced as a by-product of power generation in brown coal burning plants.