Journal Articles
Permanent URI for this collection
Browse
Recent Submissions
1 - 5 of 94
-
PublicationMachine learning applications for a better demand controlled ventilation system experience in buildings: a review(Emerald, 2024-06)Purpose: At the beginning of the Corona Virus Disease 2019 (COVID-19) pandemic, a digitalized construction environments surfaced in the heating, ventilation and air conditioning (HVAC) systems in the form of a modern delivery system called demand controlled ventilation (DCV). Demand controlled ventilation has the potential to solve the building ventilation's biggest problem of managing indoor air quality (IAQ) for controlling COVID-19 transmission in indoor environments. However, the improper evaluation and information management of infection prevention on dense crowd activities such as measurement errors and volatile organic compound (VOC) generation failure rates, is fragmented so the aim of this research is to integrate this and explore potentials with machine learning algorithms (MLAs). Design/methodology/approach: The method used is a thorough systematic literature review (SLR) approach. The results of this research consist of a detailed description of the DCV system and digitalized construction process of its IAQ elements. Findings: The discussion revealed that DCV has a potential for being further integrated by perceiving it as a MLAs and hereby enabling the management of IAQ level from the perspective of health risk function mechanism (i.e. VOC and CO2) for maintaining a comfortable thermal environment and save energy of public and private buildings (PPBs). The appropriate MLA can also be selected in different occupancy patterns for seasonal variations, ventilation behavior, building type and locations, as well as current indoor air pollution control strategies. Furthermore, the conceptual framework showed that MLA application such as algorithm design/Model Predictive Control (MPC) integration can alleviate the high spread limitation of COVID-19 in the indoor environment. Originality/value: Finally, the research concludes that a large unexploited potential within integration and innovation is recognized in the DCV system and MLAs which can be improved to optimize level of IAQ from the perspective of health throughout the building sector DCV process systems. The requirements of CO2 based DCV along with VOC concentrations monitoring practice should be taken into consideration through further research and experience with adaption and implementation from the ventilation control initial stage of the DCV process.
-
PublicationWater absorption effect on tensile properties of Linear Low-Density Polyethylene (LLDPE) blend with Recycle Acrylonitrile Butadiene Rubber Gloves (NBRr) in saline water(Semarak Ilmu Publishing, 2024-07)Nowadays, waste acrylonitrile butadiene rubber glove (NBRr) has been studied for use in building materials. The primary limitation of using rubber in construction was its low water absorption capacity. The goal of this research is to investigate the effect of Linear Low-Density Polyethylene (LLDPE) on water absorption and the temperature differential (20 and 30 oC) of NBRr blender composite in distilled water (DW) and saline water (SW). The rubber glove has been blended with heated two-rolled mills at 120 °C. The substance had been compressed using a hot press. Five samples had already been created. The sample was formed like a dumbbell. The five samples were immersed in DS and SW for 30 days at temperatures of 25°C and 35°C, respectively. The result shown the tensile strength, elongation at break Eb and Young’s modulus of LLDPE/NBRr blend decrease. The tensile properties of saline water lower compare to distilled water due to corrosion and degradation. Saline water contains dissolved salts, which can contribute to corrosion and degradation of the materials. The presence of salts can accelerate chemical reactions, leading to the degradation of the polymer matrix and the rubber phase. This degradation can weaken the material and reduce its tensile strength. Moreover, as the temperature increases, the thermal energy also increases, causing the polymer chains to gain more kinetic energy. This increased energy disrupts the intermolecular forces and reduces the overall molecular interactions within the material. Weaker intermolecular interactions lead to a decrease in tensile strength. As conclusion, hydrothermal effect tensile properties when temperature increase tensile properties decrease. distilled water has better diffusion than saltwater. LLDPE/NBRr blend was discovered that both distilled and salt-water absorption resulted in a decrease in tensile properties.
-
PublicationComparative study of powdered activated carbon and granular activated carbon in metaldehyde adsorption: unraveling isotherm and kinetic insights(Wiley, 2024)Slug pellets contain metaldehyde, a potent substance that kills molluscs, which also harms the environment and human health by polluting water systems. Metaldehyde removal from wastewater has emerged as a possible treatment strategy utilizing activated carbon materials such as powdered activated carbon (PAC) and granular activated carbon (GAC). This study aims to compare and analyze the adsorption mechanisms and kinetics of these materials. SEM studies of PAC and GAC revealed unique morphological characteristics, with PAC having a finely textured surface and a well-defined porosity structure, and GAC having a granular structure with irregularly shaped particles. According to the BET analysis, PAC had a larger surface area and pore volume than GAC, which had a lower surface area and pore volume. The FTIR spectra of PAC and GAC samples reveal different types of bonds such as ―OH, O―H, C―H, C═O, C═C, and C―O which potentially affect their reactivity and interaction with metaldehyde. PAC performed better than GAC in removing metaldehyde from water, as it had a higher surface area and a wider range of pore sizes. The Langmuir isotherm and the pseudo-second-order kinetic model fit the experimental data well for this adsorption study.
-
PublicationThe mechanical properties of Crumb Rubber Steel Fiber Concrete (CRSFC)(Penerbit UTM Press, 2024)Waste materials can be used in concrete as part of replacement material. Currently, 4 billion tires are abandoned in landfills, with 1 billion generated annually and 1.2 billion dumped without treatment by 2030. The number of waste tires is continually increasing, because of the growing use of transport vehicles. Therefore, effectively reusing waste tires as crumb rubber in the mix of concrete can save energy and protect the environment, while the use of steel fiber in the concrete will help enhance its properties. The aim this research to producing the steel fiber crumb rubber concrete (CRSFC) and to balance the issues of strength loss and sustainability. Crumb rubber is used as sand replacement in the mix concrete in the following proportions: 0%, 5%, 10%, 15%, and 20%, while steel fiber is added in the following proportions: 0.5% by volume. Slump, compressive strength, dry density, water absorption, ultrasonic pulse velocity, and rebound hammer tests are performed on the concrete after curing. As the percentage of CRSFC increased, the slump value and dry density decreased while the water absorption increased. Steel fiber helps increase compressive strength by 33 % over normal concrete. The optimum percentage of crumb rubber in CRSFC as a sand replacement is approximately 5% to 10% by volume. In summary, incorporating crumb rubber and steel fibers into concrete can result in a more eco-friendly and resilient construction material.
-
PublicationIntelligent trapezoid and variable weight combination-based reconstructed GM model(Cell Press, 2024)The GM(1,1) model's prediction accuracy is significantly influenced by the accuracy of background value estimation. The traditional trapezoidal background value can only be applied to a specific data sequence. Therefore, this study proposes a GM(1,1) model background value reconstruction approach based on the combination of intelligent trapezoidal and variable weights in order to increase the model's application as well as its prediction accuracy. The trapezoidal background value function with slope and point position parameters is called model I. Then, a set of point position parameter sequences, with a new background value function is constructed, called model II. A genetic algorithm is utilized to seek for the values of the parameters to be determined in both models I and II. The results showed that for the exponential growth data series, model I and II have higher prediction accuracy compared to traditional models. For data sequences, taking the traffic volume series of a road from 2014 to 2023, the prediction accuracy of this paper's model I method can be improved by 0.3643 % and 0.2725 % compared with Deng's and Wang's models. The prediction accuracy of this paper's model II method has been further improved by 0.1075 % compared with that of model I.