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This study article emphasises the Long Short-Term Memory (LSTM) model while utilising cutting-edge machine learning techniques to provide a thorough investigation into the subject of predicting the development of construction and demolition trash. For urban planners and environmentalists, the efficient management of building waste is a critical issue given global trends towards growing urbanisation. We carefully preprocessed the sizeable historical data sets from Shanghai and Hong Kong used in this study to ensure quality and representativeness. The study compares the LSTM model to machine learning techniques like Artificial Neural Networks (ANN), Random Forests (RF), and Support Vector Machines (SVM). When the model's ability to predict is thoroughly examined, it is clear that the LSTM routinely outperforms its competitors in terms of precision, F1 score, recall, and accuracy. Our findings also show how cutting-edge machine learning has the revolutionary potential to solve waste management and urban planning issues. LSTM and similar models provide a practical way to lessen environmental impact and lower waste disposal costs in urban settings by maximising resource allocation and promoting sustainable waste management practises. The ongoing discussion about garbage disposal in structures and sustainable urban development is furthered by this effort. It demonstrates the significance of LSTM and modern machine learning algorithms in transforming waste management paradigms and ultimately pointing us in the direction of a more environmentally friendly and sustainable future. [ABSTRACT FROM AUTHOR]

In-depth research on hybrid fiber-reinforced concrete (HyFRC), a substance with potential applications in structural engineering, is presented in this article. We examine the flexural characteristics of HyFRC, a composite made of steel, glass, and polypropylene fibres, with an eye towards how different fibre combinations affect the mechanical properties of concrete. To assess HyFRC's efficacy, a comprehensive experimental programme was run. At a total fibre volume fraction of 0.75%, the results demonstrated a noticeably higher compressive strength, splitting tensile strength, and flexural strength when steel and polypropylene fibres were combined. Effective fracture control allows the concrete to survive higher compressive loads, increasing compressive strength by up to 27% compared to non-fibrous control concrete. A crucial component of concrete performance, the splitting tensile strength in HyFRC was significantly increased thanks to the interaction between steel and polypropylene fibres. Along with better ductility, controlled fracture propagation, and greater strain capacity, HyFRC also shown these properties. The HyFRC also showed a significant increase in flexural strength, making it a suitable material for structural uses, particularly when combined with steel and polypropylene fibres. The features of the load-deflection demonstrated ductile behaviour, in stark contrast to the brittle failure seen in the control concrete. Concurrent with experimental study, an Artificial Neural Network (ANN) model with a remarkable total efficiency of 99.84% was developed, allowing precise predictions of flexural strength based on concrete composition. [ABSTRACT FROM AUTHOR]