The use of the plastic waste material to stabilize soil is step in the right direction making the construction industry a suitable candidate with its high consumption capacity. The method would be a reliable alternative to traditional ground improvement technique. It would be cost effective, sustainable and environmentally friendly. In this review paper, the behaviour of reinforced soil with plastic waste would be discussed including its suitability for pavement and road construction. The previous literature highlighted by authors will be reviewed in focus on type of treated soil, nature, shape, geometry, and amount of the added plastic waste. The aim of this review is to investigate the effect of granules and fibre plastic waste materials on the soil properties in terms of microstructure, Atterberg’s limits, compaction characteristics, unconfined compressive strength, shear strength parameter, permeability, consolidation characteristics, California Bearing Ratio value, resilient modulus and swelling index. Findings by reviewed literature stated that the general behaviour of the composite plastic-soil system would be varied from that of the virgin soil. In addition, the characteristics of soil is significantly enhanced via reinforcing with plastic waste as additives. Also, the results promote sustainable engineering applications particularly in pavement road and reduction in cost and contribution to environmental sustainability by reducing plastic waste.
Clean energy applications widely recognize Proton Exchange Membrane Fuel Cells (PEMFCs) for their high efficiency and environmental compatibility. Accurate parameter identification of PEMFC models is essential for enhancing system performance and reliability, particularly under dynamic operating conditions. This paper presents a novel optimization-based approach called Heterogeneous Comprehensive Learning-Bald Eagle Search (HCLBES) with enhanced exploration and exploitation capabilities for the effective modeling of PEMFC. The algorithm combines the exploration strength of the Bald Eagle Search with comprehensive learning and heterogeneity mechanisms to achieve a balanced global and local search space. In this algorithm, the number
... Show MoreThe complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra
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