Advanced and Sustainable Technologies (ASET)

Principle of Common Lab and In-Situ Testing With the Quality Control and the Case Studies

Jun-Jian Koo — 2024-06-03

Testing building materials is essential for ensuring quality control in the construction field. In order to meet the necessary standards, codes, and specifications, it should adhere to the rules in the appropriate way. Soil, aggregate, and concrete are typically the materials tested. Aggregate testing, which is covered by BS 812, may also be tested in accordance with BS 1377, which regulates soil testing, to determine the particle size distribution. In accordance with the size of the soil, BS 1377 also provides standard guidelines on the test procedure for Proctor compaction. The Archimedes principle is used to determine the density by measuring the displacement of water or a related material, such as paraffin oil or sand. Moreover, the determination of the compressive strength of the concrete is regulated by BS EN 12390. The sampling procedure, standard procedure of the Proctor compaction test, visual evaluation of the compressive strength test failure modes, and the in-situ core should all be examined as described in this paper in order to guarantee the testing's high quality. The last section includes four common case studies in construction connected by the faulty cube-making process, the inconsistent compressive strength result, and the lack of coordination of construction activities. In conclusion, construction material testing is an integral part of quality assurance in construction, but it should be conducted in accordance with the standards to ensure that the materials have the necessary characteristics and properties to perform as intended.

Investigation of Inline Curing System Using Halogen Lamp on PET Substrate

Muhammad Najmi bin Zainal — 2024-06-03

Halogen lamps have been seen producing faster high temperatures, which potentially reduces the curing time even further, but the current application was performed offline, thus causing the increase in manufacturing time and inaccuracy of the dimension of the ink track printed. Furthermore, the sample may also have been cured by surrounding temperature rather than the selected designated curing process. The inline curing process of conductive ink is required to prevent air cured and simultaneously reduce the manufacturing time. This study aims to evaluate the performance of the inline curing process through a halogen lamp on a PET substrate. The important curing parameters were determined, and the effect on manufacturing time, dispersion rate, and resistance were analyzed. The relationship between the significant parameters, including curing time and temperature, has also been established in which the resistance of the ink track is proportionated to the length of curing time and how high the temperature is applied. The conductive properties were increased when the temperature was elevated, reducing the curing time. However, increasing the time taken to cure the ink caused the substrate to wrap, deform a bit, and slightly damage the sample. It was also observed that the hardness and adhesion level became harder and stronger as the temperature increased. This concludes that the inline curing process using a halogen lamp is feasible on a PET substrate.

Plant Disease Classification Using Image Processing Technique

Shahrul Fazly Man — 2024-06-03

Agriculture remains pivotal to our economy, with farming playing a central role in revenue generation. Challenges such as pests, plant diseases, and evolving climate patterns pose threats to crop yield and production. Addressing these challenges, timely and accurate detection of plant diseases emerges as imperative. Manual detection, however, remains resource-intensive and often lags. Addressing this gap, this project proposes an innovative image processing-based system for rapidly detecting plant diseases. The system proficiently identifies specific diseases by analyzing images of plant leaves against a curated dataset. The emphasis of this study was on three major diseases: Bacterial Blight (with an accuracy of 98.6%), Alternaria Alternata (98.5714%), and Cercospora Leaf Spot (97.5%). The compelling results underline the system's capacity to swiftly and effectively categorize diseases, offering monoculture farmers an indispensable tool for obtaining prompt, disease-specific insights.

IoT Enabled Mushroom Farm Automation with Machine Learning

Shafie Omar — 2024-06-03

Mushroom farming has gained prominence due to its significant contribution to the global market. One major challenge for mushroom cultivation is maintaining optimal environmental conditions, specifically temperature and humidity. Traditional farming methods, prevalent in many parts of the world, lack precise control over these parameters, often leading to poor yield. This paper presents an innovative approach combining the Internet of Things (IoT) and Machine Learning (ML) for mushroom farm automation. The proposed system employs the ESP8266 microcontroller with specific agricultural sensors for smart monitoring. To regulate the farm's environmental conditions, ML algorithms predict mushroom farm weather states: mild, normal, and hot. The ensemble ML model, comprising five classifiers – Decision Tree, Logistic Regression, K-nearest neighbor, Support Vector Machine, and Random Forest – delivers a commendable accuracy of 100% when combining predictions, surpassing the performance of individual classifiers. This integrated IoT and ML approach promises to revolutionize real-time automation and cultivation practices in the mushroom industry.

AI Assisted and IOT Based Fertilizer Mixing System

Wan Mohd Faizal Wan Nik — 2024-06-03

Agriculture techniques, particularly fertilizer mixing, have significant impacts on crop productivity. Introducing IoT technology to agriculture can enhance productivity, and machine learning offers a mechanism to gain insights from data, making agricultural practices more efficient. This research aims to design an AI-assisted and IoT-based fertilizer mixing system for greenhouses. This system utilizes sensor data and AI algorithms, specifically the Support Vector Machine (SVM), to optimize fertilizer application. Results from the SVM classifier showed a 100% accuracy rate for temperature and humidity, 65% accuracy for phosphorus, 86% for nitrogen, and 100% for potassium. These findings demonstrate the potential of the proposed system to improve fertilizer efficiency while reducing labor and resource waste.

Growth Responses of Okra (Abelmoschus esculentus L. Moench) to Selected Plant Growth Regulators

F. Abdullah — 2024-06-03

This study was conducted to evaluate the effects of two types of plant growth regulators (PGRs) which are gibberellins (GA₃) and Paclobutrazol (PBZ) on the growth and photosynthetic pigment (chlorophyll) of Okra (Abelmoschus esculentus L. Moench) plants. Exogenous applications of GA₃ and PBZ with different concentrations (i.e. 20, 40, 80 and 100 mg/L) were sprayed on two-week-old Okra plants under the nursery stage. The control plants were only treated with distilled water. The stem diameter (mm) of treated and control plants was measured weekly. At the end of the experimental period, data on growth characteristics such as plant height (cm), leaf area (cm²) and number of leaves were recorded. The estimation of chlorophyll was measured using the SPAD-502 Chlorophyll Meter. Results showed that the plant morphological characteristics of Okra plants were significantly affected by the application of GA₃ and PBZ (P<0.0001). In addition, stem growth (expressed as stem cross-sectional area- mm²) of Okra plants was significantly increased with increasing GA₃ concentrations. In contrast, applying PBZ reduced Okra plants' stem growth. This study highlighted the major effects of GA₃ and PBZ on the growth of Okra plants when planted under tropical climate conditions.

Effects of Race Car's Speed on the Aerodynamic Aspect Using Computational Fluid Dynamics Analysis

Yik Pey Tang — 2024-06-03

This research employs Computational Fluid Dynamics (CFD) methods to investigate the intricate relationship between race car speed and external aerodynamics during high-performance racing competitions. The primary objectives encompass the application of CFD in pre-processing and analyzing external aerodynamic aspects, coupled with a comprehensive examination of the external flow around a race car for a nuanced understanding of its aerodynamic performance. Various car speeds were considered with the RANS (k-ω SST) turbulent model. The results unveiled a direct correlation between inlet velocity and the maximum velocity attained by the race car. The aerodynamic design intricately directs the airflow, leading to higher velocities predominantly along the upper part of the car body. Noteworthy is the revelation that the highest recorded maximum velocity of 231.06 m/s coincides with a peak inlet velocity of 200 m/s, suggesting a consistent increase in maximum velocity with rising inlet velocity. This research emphasizes the pivotal role of inlet velocity in achieving peak car speed performance. It sheds light on the significance of turbulent model selection in capturing the complexities of external flow dynamics. This knowledge contributes to optimizing the external aerodynamics of race car body design, ultimately enhancing performance and competitiveness in the dynamic world of Formula 1 racing.

Computational Fluid Dynamics Analysis on the Road Bike Using Different Flow Models under Extreme Inlet Velocity

Keat Ming Tan — 2024-06-03

At high velocities, the aerodynamic forces acting on the road bike and rider become more pronounced, potentially affecting stability and control. Riders might experience increased resistance, requiring more effort to maintain balance and direction. This research employs Computational Fluid Dynamics (CFD) to thoroughly examine the external aerodynamics of road bikes, focusing on pre-processing techniques and their impact on overall aerodynamic performance. The research applies CFD methods for geometry preparation, meshing, and material property definition within a structured workflow using a road bike model representative of the cycling industry via SimFlow software. Through systematic variations in extreme inlet velocities (40, 70, and 100 m/s) and the utilization of diverse turbulent models, k-ω Shear-Stress Transport (SST) and Reynolds-Averaged Navier-Stokes (RANS) with k-ε and k-ω, the study reveals intricate airflow patterns around the road bike. The results explain the complicated connection between turbulent models and inlet velocities and provide new information on critical aerodynamic parameters, such as pressure and maximum velocity of the road bike model.

Analysis of Tesla CyberTruck Speed on the Velocity and Pressure Distribution Using SimFlow Software

Shankarsana Paramasivan — 2024-06-03

This work presents a comprehensive Computational Fluid Dynamics (CFD) analysis of the external flow around the Tesla CyberTruck, focusing on its aerodynamic characteristics under varying conditions. The primary objectives are to study velocity and pressure distributions. The simulation considers two independent parameters: vehicle speed (20, 40, and 80 m/s) and the type of turbulent flow (k-ω SST and k-ε). The simulation provides insights into complex flow patterns through meticulous meshing, boundary condition setup, and solver configuration, highlighting areas of interest such as flow separation, recirculation, and turbulence. Parametric variations are analyzed to determine how turbulent flow type and speed affect critical parameters like pressure and velocity. The results of this CFD analysis offer valuable information about the vehicle's aerodynamic performance, contributing to design optimization, handling and stability enhancements, and improved fuel efficiency. The findings from this study are expected to enhance visualization and understanding of the aerodynamic aspects of the Tesla CyberTruck.

Airflow Analysis of the Bus Under Various Velocities Using CFD Simulation

Muhammad Aiman Hakim Samsuddin — 2024-06-03

This project investigated the aerodynamic effects of various airflow velocities around a bus. Key parameters included airflow velocity and turbulence models, with dependent variables being the velocity and pressure on the bus body. The bus model, sourced from an existing GrabCAD file, was imported into SimFlow 4.0 for analysis. A steady-state simulation was employed, incorporating symmetry conditions, turbulence modeling, boundary conditions, and a baseline post-processing method to visualize the velocity and pressure distributions around the bus. Mesh refinement was carefully adjusted to accommodate the bus's large size and accurately capture the flow gradients. The simulation results were analyzed using Paraview post-processing to evaluate the final velocity and pressure contours. The results showed that the airflow velocities crucially affect the velocity and pressure distribution around the bus. This study yielded significant and insightful results by varying the independent parameters, advancing the understanding of bus aerodynamics under different airflow velocities.