The rising amount of waste generated worldwide is inducing issues of pollution, waste
management, and recycling, calling for new strategies to improve the waste ecosystem, such …

The application of machine learning contributes to intelligent and efficient management of
construction and demolition waste, leading to a reduction in waste generation and an …

Construction and demolition waste (DW) generation information has been recognized as a
tool for providing useful information for waste management. Recently, numerous …

Abstract Information on waste generation rate (WGR) is useful for waste management.
Recently, several studies have been conducted to predict WGR using artificial intelligence …

Identifying in situ hazardous materials can improve demolition waste recyclability and
reduce project uncertainties concerning cost overrun and delay. With the attempt to …

Artificial neural networks (ANN) exhibit excellent performance in complex problems and
have been increasingly applied in the research field of construction management (CM) over …

Forecasting municipal solid waste (MSW) generation and composition plays an essential
role in effective waste management, policy decision-making and the MSW treatment …

Abstract The integration of Artificial Intelligence (AI) technologies holds immense potential
for revolutionizing waste management systems in underserved communities across the …

Many technical, climatic, environmental, biological, financial, educational, and regulatory
factors are typically involved in solid waste management (SWM). Artificial Intelligence …

In this study, we introduce a novel denoising transformer-based neural network (DTNN)
model for predicting the remaining useful life (RUL) of lithium-ion batteries. The proposed …