Comparison Between Conventional Method and Cradle-to-Cradle Method of Waste Management Method

Population growth, rapid development and Covid-19 outbreak contributes to the waste generation. Sustainable waste management procedures limit untreated debris disposal where this renewable method will benefit to the environment. Main objective of this study is to determine the best method of waste management that can be proposed to apply in Malaysia. Conventional method and cradle-to-cradle (C2C) method have advantages also disadvantages that will be considered to provide an overall comparison. This study’s methodology includes reviewing previous studies, obtaining data from project conducted at Solid Waste Research Center (SWRC), UTHM and afterwards analyzing the findings. Three elements such as environmental impact, process in producing new product and cost expenditures also considered to be discussed. This research concludes based on the results in comparison, Malaysia should practice the C2C waste management method as it is the best waste management technique due to the waste prevention at the end of any product\u27s life cycle as a key

Comparison between Conventional Method and Cradle-to-Cradle Method of Waste Management Method

 Concrete is a widely used material in the construction industry. Rice huskash and coal bottom ash are by-product materials chosen to alternate the use of cementand sand in concrete production. Concerns about the long-term performance ofseawater concrete structures incorporated with RHA-CBA have been obstacles to thedeveloping uses of sustainable concrete due to the scarcity of knowledge and test data.Thus, the long-term performance of seawater concrete consisting of RHA as cementreplacement and CBA as sand replacement on compressive strength and carbonationwas evaluated in this study. To accomplish the study\u27s goal, existing specimens aged1 year and 4 months, and 2 years with different mixture series were used. In themixture, seawater was utilized to fully replace freshwater, along with 10% RHA usedto replace cement. Additionally, 10% to 100% CBA replaced the sand with a 10%increment. The specimens consisted of 68 cylindrical concretes in 100 100 mm and100 200 mm exposed to indoor environmental conditions. Compressive strengthand carbonation tests were performed to assess the strength and durability of theconcrete. The results indicate that the concretes\u27 strength grows insignificantly in theatmospheric environment within exposure age. After a prolonged period, thespecimen\u27s strength development reduces as the amount of CBA in the concretemixture increases. Meanwhile, test results revealed that RHA-seawater concretecontaining CBA exhibited minimal carbonation after 2 years of aging. Higherincorporation of CBA in the concrete mixture leads to a greater carbonation rate,reducing the concrete’s alkalinity. The findings of this study contribute to theunderstanding and advancement of long-term performances of sustainable concretematerials for construction practices

Forecasting low-cost housing demand in Pahang, Malaysia using Artificial Neural Networks

Low cost housing is one of the government main agenda in fulfilling nation’s housing need. Thus, it is very crucial to forecast the housing demand because of economic implication to national interest. Neural Networks (ANN) is one of the tools that can predict the demand. This paper presents a work on developing   a model to forecast low-cost housing demand in Pahang, Malaysia using Artificial Neural Networks approach. The actual and forecasted data are compared and validate using Mean Absolute Percentage Error (MAPE). It was found that the best NN model to forecast low-cost housing in state of Pahang is 1-22-1 with 0.7 learning rate and 0.4 momentum rate. The MAPE value for the comparison between the actual and forecasted data is 2.63%. This model is helpful to the related agencies such as developer or any other relevant government agencies in making their development planning for low cost housing demand in Pahang

FORECASTING LOW COST HOUSING DEMAND IN MALAYSIA: COMPARISON BETWEEN ANN AND ARIMA METHOD

One of Malaysias longstanding development objectives is the provision of affordable housing for Malaysian, with a focus on lower-income groups. It is very crucial to predict low-cost housing demand to match the demand and supply so that the government can plan the allocation of low cost housing based on the demand. Thus the aim of this study is to forecast low-cost housing demand in Johor, Malaysia using ARIMA model. Time series data on low-cost housing demand have been converted to Ln before develop the model. Three ARIMA model were used; ARIMA (1,0,1); ARIMA (1,0,0) and ARIMA (2,0,0). The performance of models was validated using Mean Absolute Percentage Error (MAPE). The results show that ARIMA (1,0,1) is the best model with MAPE value 3.9%. It can be conclude that ARIMA method can forecast low cost housing demand in Johor slightly better than ANN

Forecasting low-cost housing demand in Pahang, Malaysia using Artificial Neural Networks

Low cost housing is one of the government main agenda in fulfilling nation’s housing need. Thus, it is very crucial to forecast the housing demand because of economic implication to national interest. Neural Networks (ANN) is one of the tools that can predict the demand. This paper presents a work on developing   a model to forecast low-cost housing demand in Pahang, Malaysia using Artificial Neural Networks approach. The actual and forecasted data are compared and validate using Mean Absolute Percentage Error (MAPE). It was found that the best NN model to forecast low-cost housing in state of Pahang is 1-22-1 with 0.7 learning rate and 0.4 momentum rate. The MAPE value for the comparison between the actual and forecasted data is 2.63%. This model is helpful to the related agencies such as developer or any other relevant government agencies in making their development planning for low cost housing demand in Pahang

SIGNIFICANT INDICATORS OF LOW COST HOUSING DEMAND: COMPARISON BETWEEN RESULTS OBTAINED FROM PRINCIPAL COMPONENT ANALYSIS, BACK ELIMINATION AND REGRESSION ANALYSIS METHODS

This paper has reported comparison between Principal Component Analysis (PCA), Back Elimination Method (BEM) and Regression Method. These techniques were applied by using statistical software package SPSS 13.0. For the purpose of comparison, all the methods were tested on nine prime indicators of low cost housing demand which include population growth, birth rate, mortality rate, inflation rate, unemployment rate, GDP (gross domestic product), housing stock, household income and poverty rate. Data for the indicators was obtained from ministry of housing for low cost housing demand in Gombak District. From analysis it was found that PCA method had identified three significant indicators for low cost housing demand that is GDP/Capita in Selangor, housing stock and mortality baby rate. BEM had identified four significant indicators that is inflation rate, GDP/Capita in Selangor, Poverty Rate and Housing Stock. While, regression method identified only one significant indicator that is poverty rate. From these findings it can be concluded that BEM is the best method in determining significant indicators as compared to PCA and regression method. These finding will help the researcher in adopting suitable method for determining significant indicators in any field

Automated low cost house demand forecasting for urban area

Historically, urbanization was the product of industrial expansion and rapid economic growth. In developing country however, the process has been characterized by rapid urban growth without corresponding economic growth of the cities. This resulted in the emergemve of the informal sector and squatter settlements. In Malaysia, urbanization is expexted to continue, with the Department of Statistics projecting the urban population at 64% of total population by the year 2020

General risks for tunnelling projects: an overview

Tunnels are indispensable when installing new infrastructure as well as when enhancing the quality of existing urban living due to their unique characteristics and potential applications. Over the past few decades, there has been a significant increase in the building of tunnels, world-wide. Tunnelling projects are complex endeavors, and risk assessment for tunnelling projects is likewise a complex process. Risk events are often interrelated. Occurrence of a technical risk usually carries cost and schedule consequences. Schedule risks typically impact cost escalation and project overhead. One must carefully consider the likelihood of a risk’s occurrence and its impact in the context of a specific set of project conditions and circumstances. A project’s goals, organization, and environment impacts in the context of a specific set of project conditions and circumstances. Some projects are primarily schedule driven; other projects are primarily cost or quality driven. Whether a specific risk event is perceived fundamentally as a cost risk or a schedule risk is governed by the project-specific context. Many researchers have pointed out the significance of recognition and control of the complexity, and risks of tunnelling projects. Although all general information on a project such as estimated duration, estimated cost, and stakeholders can be obtained, it is still quite difficult to accurately understand, predict and control the overall situation and development trends of the project, leading to the risks of tunnelling projects. This paper reviews all the key risks for tunnelling projects from several case studies that have been carried out by other researchers. These risks have been identified and reviewed in this paper. As a result, the current risk management plan in tunnelling projects can be enhanced by including all these reviewed risks as key information

Web-based Risk Assessment Technique for Time and Cost Overrun (WRATTCO) – A Framework

AbstractControlling time and cost overrun of construction projects is very crucial in achieving successful completion of any projects. Unfortunately, construction industry today is facing a major risk in achieving completion of project within estimated time and cost. This risk is caused by various factors. Aiming to treat this problem, this study presents a framework for web-based expert and decision support system in order to assess the risk level of causative factors of time and cost overrun on project success throughout the lifecycle of construction process. It will be integrated with project schedule to estimate the consequences of these factors and forecast the loss of time and cost if the risk factors are not controlled. This will be achieving by implanting the technique of neural network. The program will also be able to suggest the corrective actions in order to control the identified risk factors. Finally, various reports can be generated in presenting the associated problems of the factors and their relative impact of project performance

Decomposing Process of Food Waste using Black Soldier Fly Larvae (BSFL): Case Study in Taman Pura Kencana, Johor

Food waste is one of the critical issues which has been discussed in many countries including Malaysia. Apart from that, about 50% of food waste usually being dumped at landfill sites and incinerators which root to more problems towards the environment, economic and society. In this research, Taman Pura Kencana, Johor was chosen to identify the average total of food waste generation from selected households in 3 months from April 2021 to June 2021 and to determine decomposing days of food waste using BSFL. Raosoft Sample Size Calculator was used to calculate sample respondents bringing 48 households in this research. Hence, the average total of food waste generation from households per month was 1056.82 kg The collection of food waste from selected households in Taman Pura Kencana during the normal month of June was the highest compared to the fasting month in April and Eid month in May. 25 samples of food waste were examined to identify duration of decomposing days using BSFL based on pH, temperature and moisture content. The results showed that, the food waste successfully decompose after 10 days compare to control sample which was taking the longest time to fully decompose at about 90 days. The range of average temperature of the food waste using BSFL were 27℃ to 34℃. The initial range of average pH value for the food waste samples were 3.22 to 4.00 while on the last decomposing days of the food waste samples were 7.06 to 7.12. Moisture content for food waste samples is 51% except for control sample. Inconclusion, BSFL were the ideal insects to decrease amount of food waste in landfills and incinerators as they could accelerate the time of composting food waste and could reduce the negative impacts towards the environment, economic and society.This research’s findings will help create awareness and a better understanding of how much food waste could be generated from residential area, as this contributes to many negative issues when discarding them through landfills and incinerators