Plastic bag wastes (PE) are used to improve the workability of concrete and expected to reduce the plastic wastes in our environment. Polymer products such as synthetic fibers, plastics and rubbers are belonged to petrochemical compound and considered as non-biodegradable materials. One way of reducing the plastic wastes is by utilizing the plastic wastes in the production of concrete. This study investigates the dry density properties of concrete that utilize plastic wastes and polymer fiber to replace the cement, followed by finding the compressive, tensile and flexural strength of the concrete and finally, to compare the performance of concrete that utilize plastic wastes and polymer fiber vs. concrete with plastic wastes only. An extensive experimental study has been performed by utilizing the plastic wastes in concrete using a percentage of 10%, 20% and 30% and polymer fiber with percentage of 2%, 4% and 6% respectively. Results have showed the tendency of lower density in the polymer modified concrete. The utilization of waste polymer in the replacement of cement reduces compressive and flexural strength of concrete. This is probably due to bridging action provided by the fibers which absorbed more energy and prevent the sudden failure of the concrete. On the other hand, lower mechanical properties of the modified concrete that used plastic wastes and polymer fibers have been recorded in the study.
Waste generation in smart cities is a critical issue, and the interim steps towards its management were not that effective. But at present, the challenge of meeting recycling requirements due to the practical difficulty involved in waste sorting decelerates smart city CE vision. In this paper, a digital model that automatically sorts the generated waste and classifies the type of waste as per the recycling requirements based on an artificial neural network (ANN) and features fusion techniques is proposed. In the proposed model, various features extracted using image processing are combined to develop a sophisticated classifier. Based on the different features, different models are built, and each model produces a single decision. Besides, the kind of class is determined using machine learning. The model is validated by extracting relevant information from the dataset containing 2400 images of possible waste types recycled across three categories. Based on the analysis, it is observed that the proposed model achieved an accuracy of 91.7%, proving its ability to sort and classify the waste as per the recycling requirements automatically. Overall, this analysis suggests that a digital-enabled CE vision could improve the waste sorting services and recycling decisions across the value chain in smart cities.
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