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Featured Application: This work focuses on modelling stress concentration in the internal stiffeners of ship-to-shore quay crane legs, which is critical when these structures undergo heightening modifications. The research aims to improve the structural integrity and safety of cranes during retrofitting or extension processes, where stress distribution can become uneven due to design changes using a new modelling procedure. Potential applications include enhancing the lifespan of quay cranes used in ports and ensuring their reliability under increased operational loads following heightening procedures. This paper presents a study on the modelling and estimation of stress concentration at the tips of leg stiffeners in ship-to-shore (STS) quay cranes, which is intensified in those on the sea-side leg extensions, which are more prone to crack formation, notably following structural heightening of the cranes. A computer-simulated database was generated, incorporating mechanical parameters and geometric features that impact stress concentration. These variables can then be integrated as inputs into a multiple linear regression model (MLR). This methodology offers an alternative to the finite element method (FEM) for the computation of stress concentration and deformations. At the same time, the statistical significance of the parameters influencing this scenario is determined, ensuring a comprehensive assessment of their impact on the studied phenomenon. The research underscores the importance of incorporating stress concentration and structural geometry considerations into crane design or modification, given their crucial role in preserving the remaining lifecycle of the structure. Crack initiation is significantly intensified in regions characterised by high stress concentrations, particularly in areas where there are geometric changes at the tips of the stiffeners, where local stiffness is altered. All of this is in combination with work cycles under the supported loads. [ABSTRACT FROM AUTHOR]

In recent years, despite a decline in international trade and disruptions in the supply chain caused by COVID-19, the main container terminals in Latin America and the Caribbean (LAC) have increased their container volumes. This growth has necessitated significant adaptations by seaports and their authorities to meet new demands. Consequently, there has been a focused analysis on the performance, efficiency, and competitiveness, particularly their most relevant logistical aspects. In this paper, a multi-objective hybrid approach was employed. The Principal Component Analysis (PCA) technique was combined with the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) to rank LAC container terminals and identify operational criteria affecting efficiency. The analysis considered all input variables (berth/quay length, quay draught, yard area, number of quay cranes (portainer), number of yard cranes (trastainer), reachstacker, multicranes, daily montainer movement capacity, number of station reefer container type, number of terminals, and distance to the Panama Canal) and output variable (port performance expressed in TEUs from 2014 to 2023). The results revealed noteworthy findings for several terminals, particularly Colón, Santos, or Cartagena, which stands out as the main container port in LAC not only in annual TEUs throughput, but also in resource utilization. [ABSTRACT FROM AUTHOR]

Predicting the levels of a pollutant in a given area is an open problem, mainly because historical data are typically available at certain locations, where monitoring stations are located, but not at all locations in the area. This work presents an approach based on developing predictions at each of the points where an immission station is available; in this case, based on shallow Artificial Neural Networks, ANNs, and then using a simple geostatistical interpolation algorithm (Inverse Distance Weighted, IDW), a pollutant map is constructed over the entire study area, thus providing predictions at each point in the plane. The ANN models are designed to make 1 h ahead and 4 h ahead predictions, using an autoregressive scheme as inputs (in the case of 4 h ahead as a jumping strategy). The results are then compared using the Friedman and Bonferroni tests to select the best model at each location, and predictions are made with all the best models. In general, to the 1 h ahead prediction models, the optimal models typically have fewer neurons and require minimal historical data. For instance, the best model in Algeciras has an R of almost 0.89 and consists of 1 hidden neuron and 3 to 5 lags, similar to Colegio Los Barrios. In the case of 4h ahead prediction, Colegio Carteya station shows the best model, with an R of almost 0.89 and a MSE of less than 240, including 5 hidden neurons and different lags from the past. The results are sufficiently adequate, especially in the case of predictions 4 h into the future. The aim is to integrate the models into a tool for citizens and administrations to make decisions. [ABSTRACT FROM AUTHOR]