Classification of compressive strength grades for lightweight aggregate concrete with palm oil fuel ash (POFA) using kNearest Neighbour (k-NN)

Annually, a massive number of agricultural by-products of the palm oil extraction process including palm oil fuel ash (POFA) were generated which contributes towards ammonia pollution and emission of nitrogen compounds. Fortunately, both by-products can be utilised as mixing additives in lightweight aggregate concrete manufacturing. The utilisation leads to a more sustainable green environment. Traditional methods for classifying concrete grades in civil engineering are difficult due to the non-linear relationship between the composition of concrete and its strength and require a significant amount of time, material resources, and labour. To address these shortcomings, a technique to classify the compressive strength grades for lightweight aggregate concrete containing POFA using a machine learning algorithm has been developed. In terms of method, concrete mixtures consisting of POFA, cement, sand, superplasticizer and water were prepared and tested to determine the compressive strength. The data from this process were first transformed using min-max normalization and then, analysed using exploratory and descriptive analysis to discover patterns between input variables and concrete grades. Next, the grades of concrete were classified using a machine learning algorithm named k-Nearest Neighbour (k-NN). Lastly, a confusion matrix was used to assess the performance of the k-NN classifier. The results showed that k-NN can classify the grades of concrete with accuracies between 71% and 95% using five nearest neighbours. The accuracies are inversely proportional to the number of nearest neighbours. To conclude, the study succeeds in classifying the compressive strength grades for lightweight aggregate concrete with POFA using k-Nearest Neighbour. It can cut down a significant amount of time, material resources, and labour in determining the grades of compressive strength for POFA-based lightweight concrete

Determination of substantial chemical compounds of agarwood oil for quality grading

Agarwood is a resin saturated heartwood producing its ownessential oil. This oil comprises of a complex mixture of chromone derivatives, oxygenated sesquiterpenes and sesquiterpene hydrocarbons. This mixture has a heavy woody scentand is one of the contributors to the Agarwood oil quality. In this paper, a study that focuses on the approach to select the substantial chemical compounds for Agarwood quality grading was carried out. GC-MS analysis was used to extract the chemical compounds from the Agarwood oil. The data were then pre-processed using techniques such as missing values ratio, natural logarithm and min. max. normalization. Next, synthetic data were generated using MUNGE to fulfil the passing condition of sampling adequacy test. To determine the substantial compounds, PCA and Pearson’s correlation were used. This approach was successful in determining three substantial compounds namely β-agarofuran, α-agarofuran and 10-epi-γ-eudesmol. These substantial chemical compounds will be used later to predict the quality of Agarwood oil

Determination of agarwood oil’s significant chemical compounds using principal component analysis

Agarwood oil is considered a high market value oil and expensive commodity. It consists of a complex mixture of sesquiterpene hydrocarbons, oxygenated sesquiterpenes and chromone derivatives. These chemical compounds contribute to the determination of Agarwood oil quality. In this study, a statistical analysis concentrates on chemical compounds of Agarwood oil is conducted. The chemical compounds were analysed using Principal Components Analysis (PCA). Using GC-MS analysis, the chemical compounds were first identified. Then, PCA with correlation matrix was used to further analyse the data. Scree Plot was used to select valid principal components. To determine the significant chemical compounds, the data under these principal components were rotated using Varimax. 11 chemical compounds were found significant and they can be used in identifying Agarwood oil quality