## 3 Results
### Mapping Key Research Streams Through the Most Cited Contributions  
The bibliographic analysis subsequently focused on the 20 most-cited articles [15; 16; 18] addressing the application of AI and Digital Twins (DT) in sustainable waste management. These publications, presented in Table 6, represent foundational scientific contributions in the field. They provide a comprehensive overview of evolving research trends, influential authors and journals, and thematic priorities addressed in recent years [16; 18]. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Title** & **References** & **No. of Citations** & **Main Focus** & **Research Trends** \\ \hline Development of machine learning-based models to forecast solid waste generation in residential areas: A case study from Vietnam & [35] & 296 & Forecasting urban solid waste generation using ML & Improving predictive accuracy and integrating socio-economic data \\ \hline Mixture optimization for environmental, economical, and mechanical objectives in silica future concrete & [36] & 283 & Optimization of concrete mixtures with silica future using ML & Multi-objective applications for sustainability and mechanical performance \\ \hline Modeling the mechanical properties of recycled aggregate concrete using hybrid machine learning algorithms & [37] & 269 & Mechanical properties of recycled concrete through hybrid ML models & Use of SHAP/PDP for explainability in predictive models \\ \hline Prediction of carbonation depth for recycled aggregate concrete using ANN hybridized with swarm intelligence algorithms & [38] & 257 & Carbonation depth in recycled concrete using hybrid ANN models & Robust ML models for durability and environmental degradation \\ \hline Prediction of thermo-mechanical properties of rubber-modified recycled aggregate concrete & [39] & 247 & Thermo-mechanical properties of rubber-modified recycled concrete & Predicting performance at high temperatures using ML \\ \hline Evaluating compressive strength of concrete made with recycled concrete aggregates using machine-learning approach & [40] & 228 & Estimating compressive strength of recycled concrete & Hybrid ML models for assessing concrete quality \\ \hline A strength prediction model using artificial intelligence for recycling waste tailings as cemented paste backfill & [41] & 199 & Strength prediction of mine waste-based backfill & Applying BRT + PSO to reduce experimental testing \\ \hline \hline \end{tabular}
\end{table} 
Table 6: State-of-the-art analysis. Our elaboration. The decision to analyze the most-cited rather than the most recent papers was driven by the intention to avoid imposing two consecutive temporal constraints on the bibliometric  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
analysis--the first stemming from the predefined search period in Scopus and the second which would have resulted from selecting only the most recent publications. Thus, the analysis focuses on contributions that have had the greatest scientific impact, capturing well-established theoretical and methodological foundations in the field [15; 17; 18]. The analysis of the 20 most-cited articles highlights the emergence of a multidimensional and strongly interdisciplinary research landscape. Intelligent technologies are redefining how waste management challenges are addressed, with applications ranging from waste generation forecasting and recycled material design to environmental simulation and industrial optimization within the circular economy framework. The first key research area involves the use of AI for waste generation prediction and management. [PERSON] et al., 2021 [35] developed machine learning models to accurately estimate urban solid waste generation in residential areas, emphasizing the integration of socio-economic data.