Engineering Proceedings

The maritime industry is experiencing significant growth due to globalized trade, but this expansion has led to increasing environmental concerns. Studies project that shipping emissions could reach 90–130% of 2008 levels by 2050 without intervention potentially contributing up to 17% of global CO₂ emissions by 2050, thereby posing a major environmental challenge. Stringent environmental regulations from international organizations and government agencies necessitate the maritime industry to find effective solutions to reduce its greenhouse gas (GHG) emissions and improve energy efficiency. This research proposes a methodology for dynamically calculating optimal ship speed to enhance energy efficiency and reduce GHG emissions. By leveraging real-time environmental data (e.g., weather forecasts, sea state information) and operational parameters (e.g., ship characteristics, cargo load), the study utilizes an Adaptive Particle Swarm Optimization based on Velocity Information (APSO-VI) to predict optimal speed over ground (SOG) in real time. The study utilizes the Energy Efficiency Operational Index (EEOI) as a performance metric. EEOI is a widely employed measure in the maritime industry that quantifies the grams of CO₂ emitted per tonne-nautical mile (g CO₂/t nm) of transport work. The effectiveness of the proposed dynamic optimization model (APSO-VI) is assessed by comparing its performance with constant velocity models through extensive simulations, showing a 5–12% reduction in EEOI with the optimized speed model. The results demonstrate significant reductions in fuel consumption and emissions, supporting the adoption of such technologies for a more sustainable maritime industry. Future research may explore integrating machine learning techniques and advanced weather forecasting models for even more robust optimization strategies.

This paper focuses on a section of the state road D28, Bjelovar northern bypass, Republic of Croatia, which was opened to traffic in 2002. Following the expiration of the 20-year design service life, it was determined that the section required reconstruction, as visual inspections indicated a significant deterioration in ride comfort. To define the appropriate reconstruction strategy, specifically the strengthening of the pavement structure, reliable data on pavement bearing capacity were needed. Historical design thicknesses were compared with current measurements, and deflection data obtained using a Falling Weight Deflectometer (FWD) were analyzed for sections where visual assessments suggested reduced structural capacity. Based on the calculated modulus of elasticity, a pavement structure was designed and subsequently strengthened through the addition of an extra load-bearing layer composed of a cold-recycled mixture with foamed bitumen.

This study introduces the Deep Learning Assisted Composite Clock (DLACC), aiming to improve the robustness of the GNSS timescale. If traditional Kalman filter-based composite clocks are today used in systems like GPS and EGNOS, the non-linear, non-Gaussian, and non-stationary behavior of atomic clocks can impact the performance of such model-based filtering. DLACC, built from the KalmanNet approach, proposes to enhance Kalman filters by computing its gain through a neural network to better model clock dynamics and manage ensemble clock reconfigurations. In particular, this study evaluates this method’s performance against conventional filters, demonstrating its potential for more resilient and adaptive GNSS timescales.