Ductility Enhancement of Sustainable Fibrous-Reinforced High-Strength Lightweight Concrete

To limit the cross-sectional size of concrete structures, high-strength, lightweight concrete is preferred for the design and construction of structural elements. However, the main drawback of high-strength, lightweight concrete is its brittleness over normal-weight concrete. The ductility of concrete is a crucial factor, which plays an important role when the concrete structures are subjected to extreme situations, such as earthquakes and wind. This study aims to improve the ductility of high-strength, lightweight concrete by incorporating steel fibers. The palm oil clinker (POC)-based, high-strength, lightweight concrete specimens reinforced with steel fibers were prepared and their ductility was systematically examined. POC was used as aggregates and supplementary cementitious materials. Steel fibers from 0–1.50% (by volume), with an increment of 0.5%, were used in the concrete mix. Compression ductility, displacement ductility and energy ductility were used as indicators to evaluate the enhancement of ductility. Moreover, the compressive strength, flexural strength, strain–strain behavior, modulus of elasticity, load-displacement characteristics, energy absorption capacity and deformability of the concrete samples were investigated. The compression ductility, displacement ductility and energy ductility indexes were found to be increased by up to 472%, 140% and 568% compared to the control specimens (concrete with 0% steel fibers), respectively. Moreover, the deformability and energy absorption capacity of the concrete were increased by up to 566% and 125%, respectively. Therefore, POC-based, high-strength, fibrous, lightweight concrete could perform better than conventional concrete under extreme loading conditions as it showed significantly higher ductility

Investigation of correlation between chemical composition and properties of biodiesel using principal component analysis (PCA) and artificial neural network (ANN)

© 2020 Elsevier Ltd Biodiesel will provide a significant renewable energy source for transportation in the near future. In the present study, principal component analysis (PCA) has been used to understand the relationship between important properties of biodiesel and its chemical composition. Finally, several artificial intelligence-based models were developed to predict specific biodiesel properties based on their chemical composition. The experimental study was conducted in order to generate training data for the artificial neural network (ANN). Available (experimental) data from the literature was also employed for this modeling strategy. The analytical part of this study found a complex multi-dimensional correlation between chemical composition and biodiesel properties. Average numbers of double bonds in the chemical structure (representing the unsaturated component in biodiesel) and the poly-unsaturated component in biodiesel had a great impact on biodiesel properties. The simulation result in this study demonstrated that ANN is a useful tool for investigating the fuel properties from its chemical composition which eventually can replace the time consuming and costly experimental test

Investigation of structural characteristics of palm oil clinker based high-strength lightweight concrete comprising steel fibers

The application of waste materials in the concrete composite is a great contribution to preserving natural resources and enhancing sustainable greener development in the construction industry. This study used Palm Oil Clinker (POC) and steel fibers to manufacture high-strength lightweight concrete (HSLWC). The POC is basically a byproduct of a palm oil industry, which was utilized as coarse aggregates and supplementary cementitious materials in the concrete. The noteworthy brittleness and lower tensile strain and strength of HSLWC were overcome by adding hooked end steel fibers. A total of six different concrete mixes by varying the volume of hooked end steel fibers, e.g., 0, 0.25, 0.50, 0.75, 1.0 and 1.50% of the total volume of the concrete were prepared. The HSLWC with 0% steel fibers was used as a reference. The physical and mechanical characteristics of the HSLWC, such as workability, density, compressive and splitting tensile strength, modulus of rupture (MOR), modulus of elasticity (MOE), stress–strain characteristics along with UPV, sorptivity and water absorption test were conducted. Compared to the reference specimen, the HSLWC increased the compressive and tensile strength, MOR, and MOE by 19%, 172%, 176% and 40%, respectively. Besides, the HSLWC exhibited ductility and had enough energy absorption capacity before failure

Exploring Machine Learning for Predicting Cerebral Stroke: A Study in Discovery

Cerebral strokes, the abrupt cessation of blood flow to the brain, lead to a cascade of events, resulting in cellular damage due to oxygen and nutrient deprivation. Contemporary lifestyle factors, including high glucose levels, heart disease, obesity, and diabetes, heighten the risk of stroke. This research investigates the application of robust machine learning (ML) algorithms, including logistic regression (LR), random forest (RF), and K-nearest neighbor (KNN), to the prediction of cerebral strokes. Stroke data is collected from Harvard Dataverse Repository. The data includes—clinical, physiological, behavioral, demographic, and historical data. The Synthetic Minority Oversampling Technique (SMOTE), adaptive synthetic sampling (ADASYN), and the Random Oversampling Technique (ROSE) are used to address class imbalances to improve the accuracy of minority classes. To address the challenge of forecasting strokes from partial and imbalanced physiological data, this study introduces a novel hybrid ML approach by combining a machine learning method with an oversampling technique called ADASYN_RF. ADASYN is an oversampling technique used to resample the imbalanced dataset then RF is implemented on the resampled dataset. Also, other oversampling techniques and ML models are implemented to compare the results. Notably, the RF algorithm paired with ADASYN achieves an exceptional performance of 99% detection accuracy, exhibiting its dominance in stroke prediction. The proposed approach enables cost-effective, precise stroke prediction, providing a valuable tool for clinical diagnosis