Integration of Environmental Costs in Ontario’s Pavement Management Systems

This study aims to quantify the health and environmental damages of emissions released by pavement management activities in Ontario. The construction, maintenance, and rehabilitation of pavement results in greenhouse gases and pollutants which have significant impacts on human health and the environment. Traditional lifecycle costing methods used in pavement management systems do not account for the cost of these impacts. Marginal damages which relate atmospheric releases to economic cost can be applied by decision-makers to understand the damages of activities (such as pavement management) but require careful consideration of underlying factors. Marginal damages from various methods across the literature were adjusted for application in this study. The present work quantified environmental costs for the construction and lifecycle maintenance of five pavement design alternatives based on emissions of carbon dioxide and four air pollutants. Concrete roads were found to have the highest environmental costs (equivalent to 77% of agency costs) whereas asphalt roads rehabilitated with Cold-in-Place recycling had the lowest environmental costs due to the reduction in raw materials used. For the asphalt road alternatives, environmental costs were equivalent to 35% of agency costs. Future work will address limitations in data availability and additional design types. These findings provide insight for further integration of externalities in pavement management systems including of noise, user costs, and use phase emissions

Sustainability Evaluation of Pavement Technologies through Multicriteria Decision Techniques

This paper presents findings of a recent study that was conducted in Canada on the quantification of pavement sustainability. The ultimate goal of this study was to develop a framework and explore the use of multicriteria decision-making techniques to formally assess the sustainability of pavement engineering alternatives. While sustainability is of increasing concern in pavement engineering, environmental performance is rarely used by pavement managers to select maintenance practices. There is therefore a need to develop a framework for the practical consideration of environmental effects in pavement management. This paper aims to provide a better understanding on the use of multicriteria decision-making techniques based on hierarchy process (AHP) and choosing by advantages (CBA) for the integration of sustainable aspects in the decision-making process of pavement management. A case study comparing pavement maintenance technologies using cold-in-place recycling and traditional solutions based on mill and overlay is analyzed for illustrative purposes. Results obtained using both multicriteria techniques are compared, including a sensitivity analysis on the importance of sustainability criteria in the evaluation of maintenance alternatives. Results obtained from this case study show that AHP and CBA provide consistent recommendations in which cold-in-place technologies are preferred over traditional alternatives. However, CBA presents the advantage of separating cost from the analysis, letting the agency to decide whether they are willing to pay more to use more sustainable alternatives. This finding has significant implications for engineering practice, given that AHP is widely used not only in the pavement field but in infrastructure management. Further research is needed to incorporate social aspects and existing barriers for the implementation of sustainable technologies in the proposed sustainability evaluation.</p

Machine Learning Approach on Cyberstalking Detection in Social Media Using Naive Bayes and Decision Tree

Social media has increased the chance to communicate through many things such as video calls and can be connected globally. But there is also a growth in the vulnerability of the system. With this advancement, some threat is bound to happen. Cyberbullying is one of the social issues that users deliberately and tenaciously misuse social media. It became an issue because most cases affect the victim's mental health. Before, detecting these crimes only has traditionally used linguistic features, but cyberbullying on social media has more than that. Therefore, technologies today may play an important role in detecting cyberstalking on social media by using Machine Learning (ML). In this paper, cyberbullying detection will use the ML algorithm, which is Naïve Bayes and Decision Tree, and compare which algorithm is better to detect. ML has a wide range of methods that allow systems to quickly access the data and learn from it to make decisions for complicated problems. Cyberstalking has been concerning as it psychologically affects the victims. An experimental result indicates that Naïve Bayes algorithms achieve the best accuracy, which is 0.958

Forecasting solid waste generation in Negeri Sembilan and Melaka

Solid waste management is vital to ensure the cleanliness of the country and keeping the good health of the people. In Malaysia, the solid waste management system is highly dependent on landfills to manage waste. However, landfill sites in Malaysia are in dire state and constructing new landfills become impossible due to land scarcity. On top of that, the practice of recycling among the public are critically lacking which contributes to rapid increase in the volume of solid waste generated. Thus, forecasting solid waste generation is crucial to avoid overflow of waste. In this study, the solid waste produced in Negeri Sembilan and Melaka is forecasted to one year ahead and to see whether the landfills in both states are still able to accommodate the solid waste produced. Secondary data of the solid waste generated in Negeri Sembilan and Melaka from January 2017 to August 2020 is used in this study. The error measures of several univariate and ARIMA models are evaluated using the Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) to choose the best model in forecasting the solid waste generation in both states. The results revealed that ARMA (2,2) and ARMA (3,1) is the best model to forecast the solid waste generation in Negeri Sembilan and Melaka respectively. Besides, the estimated solid waste generation for both states also is approaching the maximum landfill capacity and this issue should be taken seriously so that environmental damage can be reduced