Exploring the realm of extreme weather events is indispensable for various engineering disciplines and plays a pivotal role in understanding climate change phenomena. In this study, we examine the ability of 10 probability distribution functions—including exponential, normal, two- and three-parameter log-normal, gamma, Gumbel, log-Gumbel, Pearson type III, log-Pearson type III, and SQRT-ET max distributions—to assess annual maximum 24 h rainfall series obtained over a long period (1972–2022) from three nearby meteorological stations. Goodness-of-fit analyses including Kolmogorov–Smirnov and chi-square tests reveal the inadequacy of exponential and normal distributions in capturing the complexity of the data sets. Subsequent frequency analysis and multi-criteria assessment enable us to discern optimal functions for various scenarios, including hydraulic engineering and sediment yield estimation. Notably, the log-Gumbel and three-parameter log-normal distributions exhibit superior performance for high return periods, while the Gumbel and three-parameter log-normal distributions excel for lower return periods. However, caution is advised regarding the overuse of log-Gumbel, due to its high sensitivity. Moreover, as our study considers the application of mathematical and statistical methods for the detection of extreme events, it also provides insights into climate change indicators, highlighting trends in the probability distribution of annual maximum 24 h rainfall. As a novelty in the field of functional analysis, the log-Gumbel distribution with a finite sample size is utilised for the assessment of extreme events, for which no previous work seems to have been conducted. These findings offer critical perspectives on extreme rainfall modelling and the impacts of climate change, enabling informed decision making for sustainable development and resilience.

This study presents the application of the finite element method integrated with Terzaghi’s principle. The definition of a model in oedometric or confinement conditions for settlement estimation of a building after the construction of a tunnel, including the effect of Terzaghi’s principle, is an unresolved problem. The objectives of this work include the demonstration of the need for a minimum of three methodological states to estimate said settlement. For this, a specific methodology is applied to a case study, with eight load steps and four types of coarse-grained soils. In the studied case, two layers of 50 m and 5 m with different degrees of saturation are overlaying an assumed impermeable rock layer. The excavation of a tunnel of 15 m in diameter at a depth of 30 m with drainage lining inside the tunnel is assumed. The minimum distance from the tunnel’s outline to the mat foundation is 15.8 m. It is determined that the settlement, according to Terzaghi’s principle, is around 11% of the total settlement for the most compacted soil types, reaching 35% for the loose soil type, from the tunnel’s outline. In the mat foundation, it implies an increase in the differential settlement of up to 12%. It shows a nonlinear relationship between some of the variables in the analysis. To detect the collapse due to uplifting the tunnel invert, it was determined that it was not appropriate to model in oedometric conditions. The novelty of the investigation relies on identifying and determining the need for a minimum of three states for methodological purposes for a proper quantification of the total settlement: (i) before the construction of the tunnel, (ii) immediately after the excavation of the tunnel, but without groundwater inflow into the tunnel, and (iii) after the tunnelling, with stabilised groundwater inflow into the tunnel.

The use of microturbines in irrigation applications represents a great opportunity for increasing sustainable energy generation. Irrigation systems have water flow that can be used to generate electricity based on microturbines that are acceptably configure such, that efficiency in crop irrigation is not affected. This research validates this use of microturbines through a system designed specifically for the characterization of microturbine generation technology. This system includes a closed water pumping circuit capable of working under, different water flow settings, as well as flow, pressure, and electricity generation sensors. For this system, the production range of the microturbines and the pressure loss associated with the various proposed configurations are characterized and specifically quantified for the best performance. After design and characterization of a scalable microturbine system, the feasibility and benefits of this application to supporting most relevant crops supplied by localized irrigation are analysed. The experiments demonstrate the greatest benefit with the implementation of 15 series microturbines each at 80 V, alongside non-Citrus fruit, where a favourable balance is achieved for the amortization period in vineyards and citrus fruit. The results validate a profitable and sustainable design for electricity generation, with return on investment rates of up to 53%. Therefore, this research offers real and extensive applications, while being scalable to rural, residential, urban and industrial settings.

Tidal energy represents a clean and sustainable source of energy generation that can address renewable energy challenges, especially the global challenge of optimizing alternatives for stable supply. Although tidal stream energy extraction technology is in the early stages of development, it shows great potential compared to other renewable energy sources. The main objective of this research is to provide a digital tool for the optimization of the installation of turbines through fuzzy logic. The methodology in this study includes the design and development of a fuzzy-logic-based tool for this purpose. Design criteria included parameters such as salinity, temperature, currents, depth, and water viscosity, which affect the performance of tidal turbines. These parameters are obtained from the geographic location of the installation. A decision-making system is provided to support the tool. The designed fuzzy logic system evaluates the suitability of different turbine locations and presents the results through graphics and probability of success percentages. The results indicate that currents and temperatures are the most limiting factors in terms of potential turbine locations. The program provides a practical and efficient tool for optimizing the selection of tidal turbines and generating energy from ocean currents. This tool is evaluated and validated through different cases. With this approach, the aim is to encourage the development of tidal energy and its adoption worldwide.

This study presents a detailed comparative analysis of different methods for evaluating seismic response in structures, focusing on maximum displacements and collapse assessment. The results obtained through modal spectral analysis, non-linear dynamic analysis, and the incremental pushover analysis applied to a specific structure are compared. It has been found that the choice of time step and the consideration of ductility are critical for obtaining accurate predictions. The results of the non-linear dynamic analysis of the building’s response indicate that an earthquake equivalent to the one that affected the city of Lorca (southeast Iberian Peninsula) in 2011 would have a devastating impact on the studied structure, highlighting the importance of the finite element method modelling in predicting the formation of plastic hinges and assessing structural safety. These findings highlight the importance of utilising multiple analysis approaches and detailed modelling to fully understand the seismic behaviour of structures and ensure adequate resistance and stability to extreme events.