Construction methods, materials, and management, including sustainable construction, building information modeling, construction safety, project management, and innovations in building technology
3 papersThis review examines the limitations of Building Information Modeling (BIM) during construction and explores the potential of semantic Construction Digital Twins (CDTs) to address these limitations. It proposes a three-tiered framework for progressively developing CDTs, emphasizing the integration of IoT, AI, and linked data to enable smarter construction services.
The paper introduces GMAN, a graph multi-attention network that predicts traffic conditions (volume and speed) on road networks. GMAN uses spatial and temporal attention mechanisms with gated fusion to model complex correlations and a transform attention layer to reduce error propagation, achieving state-of-the-art results on two real-world datasets, especially for long-term predictions.
This study uses machine learning, specifically the xgBoost model, to predict the flexural capacity of reinforced concrete beams strengthened with fabric-reinforced cementitious matrix (FRCM). The xgBoost model outperformed existing analytical models and a SHAP analysis identified the area of tensile steel reinforcement, width of the beam, FRCM area, and effective beam depth as the most influential factors.