Engineering Journal (Faculty of Engineering, Chulalongkorn University, Bangkok)
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Durability Characteristics and Microstructure Analysis of Zeolite and Graphene Oxide induced Self-Compacting Concrete: An Experimental Study
Get PDFThis study aims to examine the influence of incorporating zeolite (Z) and graphene oxide (GO) on the efficiency of self-compacting concrete (SCC). Conventional tests are employed to assess the influence of the change on the microstructure, mechanical properties and durability of the alteration. There is a stronger focus on studying the long-lasting nature of waste expulsion. The chosen tests to investigate durability are the Rapid Chloride Penetration Test (RCPT), the rebound hammer test, the acid, alkaline and sulfate resistance test, the Ultrasonic Pulse Velocity (UPV) test, the SEM and XRD examinations of the mineral composition and microstructure. The identified optimum mix Z10G2 (Zeolite 10% and Graphene oxide 0.02%) mixture exhibited superior chemical resistance and mechanical integrity in comparison to conventional concrete (CC). This enhanced both the microscopic arrangement and the physical characteristics of the material. Based on these discoveries, it seems that identified mixes have the capacity to enhance the effectiveness and durability of concrete constructions. The overall findings indicate that inducing identified mix into concrete mixtures has the potential to enhance durability and performance in various environmental conditions. To accurately assess the potential benefits of enhancing the longevity of concrete structures, further investigation is needed to examine the long-term effects on these structures
Establishing a Management System and the Role of Civil Engineering Consultants for Early-Stage Geothermal Development in Japan
Get PDFJapanese civil engineering consultants are in general responsible for managing the entire development process of a project across multiple stages and they engage in various activities depending on their expertise. Previously, the relationship with the local government and the regions was of a form of waterfall style (“waterfall method”), where the clients prepare specifications and the civil engineering consultants implement their works according to these specifications. However, in cases of development of renewable energy to make it a major power source, this relationship has shifted to a form of a more agile style (“agile method”), where the civil engineering consultants are required to be diverse, responsive, and deployable to reflect the recent volatility, uncertainty, complexity, ambiguity (VUCA) era. This study therefore focuses on early-stage social acceptance of the geothermal development process in Japan, and the sixth and seventh editions of PMBOK are referred to examine the diverse activities that contribute to solving the problems faced by local governments and regions where renewable energy is being developed. Finally, upon such discussions, directions and specifics for future management systems are proposed
Demand Forecasting and Ordering Policy of Fast-Moving Consumer Goods with Promotional Sales in a Small Trading Firm
Get PDFThis research focuses on enhancing inventory management for fast-moving consumer goods (FMCGs) with promotional sales in a small trading company, particularly high-end items with fluctuating demand patterns. The analysis revealed that promotional campaigns led to an average demand increase of 60.44% for WM 85ML, and 161.76% for SW 85ML, highlighting the importance of including these variables in demand forecasting models. The research aims to determine an effective forecasting method for the company and develop an improved purchasing strategy. The methodology encompasses a comprehensive review of the existing system, problem investigation, solution proposal, and result analysis. Quantitative time-series forecasting methodologies specifically tailored to such luxury FMCGs were introduced including Exponential Smoothing and Holt-Winters’s Additive and Multiplicative forecasting. The application of these methods has led to a significant enhancement in forecast accuracy, with an approximate 90% improvement. The research's pivotal contribution is the development of a hybrid order policy named “Periodic Review with Safety Stocks and Reorder Point,” which merges a fixed-order quantity model with a fixed-time period model. This hybrid approach has practical implications for maintaining efficient inventory levels, enabling continuous promotional activities, and potentially reducing the company's inventory costs by approximately 30%
Correlation Between Compressive Strength and Ultrasonic Pulse Velocity (UPV) of Fly ash Cenosphere Concrete
Get PDFThe utilization of Fly Ash Cenosphere (FAC) as a substitute for sand in concrete presents a practical solution to address environmental concerns stemming from the disposal of fly ash. However, it's essential to recognize that the strength properties of FAC concrete differ from those of conventional M sand concrete. To effectively manage design and quality control in construction projects, it becomes necessary to explore the applicability of non-destructive testing methods for estimating the in-situ mechanical characteristics of FAC concrete. In this context, the current research study seeks to establish a connection between compressive strength and ultrasonic pulse velocity (UPV) as a means of predicting the compressive strength of FAC concrete using UPV testing. The study also investigates the influence of FAC replacement levels and curing duration. The experimental program involves a concrete mix proportion of 1:1.95:1.96 with a constant water-cement (w/c) ratio of 0.5. FAC replaces M sand in varying percentages from 10% to 50%, in increments of 10%, while the control mix maintains 100% M sand content. Concrete samples are cast, cured at ambient temperatures, and subsequently tested at curing ages of 7 days, 14 days, and 28 days. The consistent findings demonstrate that FAC concrete consistently exhibits lower UPV values and compressive strength compared to the control concrete across various replacement levels, curing durations, and mix compositions. Additionally, empirical relationships were established between compressive strength and UPV, displaying a strong exponential nature and a high correlation, ranging from 0.91 to 0.99. The effectiveness of these developed empirical equations for predicting compressive strength is validated through a comparison with actual test results
Developing an Algorithm for Real-Time 3D Identification of Images
Get PDFParticular methods exist to compare images based on comparing knowledge about images as a whole; however, for reliable operation of such algorithms, the image should have significant brightness jumps in most areas, heterogeneous scene details, and minimal distortions caused by affine transformations. This study aims to improve the efficiency of video information computing systems of machine vision in data processing by using methods and organization algorithms that allow real-time estimation of the moving object's location and 3D identification. For that, this paper solves a set of base theoretical problems, combining the choice of hardware for obtaining and processing information, determining the coordinate origin, defining the reference plane of the underlying surface, dividing images into levels to achieve higher processing speed, and determining the spatial coordinates of image points from the stereo system. This paper reviews existing image acquisition systems and considers correlation functions for the 3D identification problem. In developing an algorithm that performs real-time 3D identification, the problem at hand is formalized, and the number of levels of the image pyramid is selected, considering the problems arising in 3D identification
Virtual Reality of Labless Foundry and Heat Treatment for Next Industrial Training
Get PDFThis study presents an innovative Virtual Reality (VR) application tailored for educating users on Foundry and Heat Treatment operations. Its primary objectives encompass the creation of a secure and lifelike learning environment while ensuring accessibility. The development process was a meticulous fusion of technology, incorporating the Unity engine, 3D modeling, and seamless Salesforce integration, all grounded in extensive research. The VR application's structure strategically subdivides these industrial processes into four stations: Molding, Furnace, Workbench, and Heat Treatment, with each station's execution steps comprehensively outlined in corresponding tables. This detailed approach empowers users to engage directly in these operations, making it a valuable educational tool. The study's significance extends to the realm of virtual education, particularly within fields necessitating hands-on experience. The VR application offers a standardized, accessible, and immersive means of learning, transcending geographical constraints. Its adaptability for future enhancements, such as task execution accuracy improvements and scoring system configuration, positions it as a dynamic and enduring solution within the domain of virtual education. The application's 90% accuracy in simulating these operations further underscores its efficacy and reliability
Leveraging Partner Country Factors in Deep Learning for Thailand’s Forecasted Inflation Accuracy Enhancement
Get PDFThis paper focuses on improving the accuracy of headline inflation forecasts in Thailand. By evaluating the performance of deep learning models, time series forecasting models, and hybrid models in 1-, 3-, 6-, and 12-month advance forecast periods are investigated. In addition, the efficacy of including partner countries' inflation variables in the model is evaluated. There is a comparative analysis of various models, including ANN, RNN, LSTM, VAR, the hybrid model (VAR-ANN), and the BOTMM benchmark model of the Bank of Thailand. This study aimed to identify the most efficient model and demonstrate the impact of including partner countries' inflation on forecast accuracy. The results reveal that the hybrid model (VAR-ANN) consistently outperforms other models over several forecast periods, showing its superiority in capturing inflation trends. Specifically, the hybrid model (VAR-ANN) shows an average RMSE improvement of 50.36% over the BOTMM benchmark model from 2020 to 2022, with performance improvements of 52.94% in 2020, 56.56% in 2021, and 47.25% in 2022. In addition, the inclusion of partner countries' inflation significantly increases the accuracy of the predictions. These results are helpful for policymakers and practitioners working on inflation forecasts and emphasize the practical advantages of the hybrid model for enhancing prediction accuracy for Thailand's economic indicators
Shortening the Cycle Time of the Fiber Ribbon Orientation Process for Wavelength Selective Switch Production using Design for Assembly and Disassembly Concepts
Get PDFThe primary objective of the research was to shorten the cycle time of a particular process used in producing a new Wavelength Selective Switch (WSS) product by a multinational electronic manufacturing corporation. In recent years, the case study company has encountered difficulties with process cycle time exceeding predefined takt time when establishing a new production process for the freshly launched item. To identify areas for improvement, the study leveraged industrial engineering techniques, such as the Yamazumi Chart, line balancing (workload leveling) analysis, and method time measurement. After the production process data was thoroughly analysed, cycle time reduction opportunities emerged. After that, the jig design used in the present investigation was developed based on the highly effective and widely recognized mechanical engineering concepts of Design for Assembly (DFA) and Design for Disassembly (DFD). The aim was to confidently eliminate non-value-added processes in the fiber ribbon orientation step, resulting in increased efficiency and improved outcomes. The study reported a significant reduction of 87% in the cycle time required. The results also demonstrated that implementing certain methodologies could reduce the cycle time. In addition, this finding held significant importance for the industry, as it could lead to increased efficiency and productivity, ultimately leading to cost savings of 12% of its total production
Numerical Simulation and Prediction of Groundwater in Bac Ninh urban area, Red River Delta: Balancing Urban Expansion with Sustainable Resource Management
Get PDFThis study utilizes the FEFLOW model to simulate groundwater dynamics in the Bac Ninh urban area, located in the Red River Delta, to assess the impact of increased water extraction due to urbanization and industrialization. Two scenarios were analyzed: Scenario 1, which maintains current extraction rates, and Scenario 2, which includes four new groundwater supply stations. The results of Scenario 1 show a gradual decline in groundwater levels, particularly in areas distant from rivers, but levels remain within allowable limits. Wells near rivers exhibit more stable groundwater levels due to natural recharge. Scenario 2 results in a larger decline, especially in distant areas, but the strategic placement of new wells near rivers helps mitigate the impact by enhancing natural recharge. The study concludes that future water demands, projected to increase by 12,500 m³/day by 2025 and 23,000 m³/day by 2030, can be met without exceeding the allowable depletion levels, provided that groundwater resources are managed effectively. The use of the FEFLOW model highlights the importance of optimizing well placement and extraction rates to ensure groundwater sustainability in Bac Ninh
Introducing a Novel Double Hybrid Algorithm (DHA) and Developing Its Application for Predicting Air Temperature Under Climate Change Conditions (A Case Study of Iran Country)
Get PDFIn the present study, a novel double hybrid algorithm (DHA) based on the least squares support vector machine (LSSVM) and hybrid Aquila optimization-particle swarm optimization (AO-PSO) algorithms, namely LSSVM-AO-PSO for minimum, mean, and maximum monthly air temperature (AT) prediction under climate change for periods (2020-2047) at 30 meteorological stations in Iran is presented. For this purpose, first, four benchmark data sets (BDSs) are used to prove the performance of the DHA. Then, modeling of minimum, mean and maximum AT in different time delays are performed, and the best time delay is selected. The technique for order of preference by similarity to the ideal solution (TOPSIS) method showed reasonable results of the DHA compared to LSSVM, LSSVM-PSO, and LSSVM-AO in modeling AT (in the best time delay). Results of changes in AT parameters under two models, ACCESS-ESM1 and CanESM5 global climate models (GCMs), and SSP scenarios in the future period (2020-2047) showed that AT was increased in all regions of Iran in a pessimistic state, and in an optimistic state were decreased in the northeast of Iran and were increased in other regions