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Nomography is considered a branch of mathematics introduced by Maurice d'Ocagne in 1884 in France. The past century saw nomography grow as a graphical computing method used by scientists and engineers wishing to solve complex problems to a practical precision. Even though nomography has declined with the introduction of calculators and computers, it still offers potential in an educational setting. The recent development of open-source software is helping promote the use of nomograms among scholars in engineering courses who are aware of nomography's capabilities. The main reason for this apparent and renewed interest in nomography is the capability of open-source software to generate customized and precise nomograms in seconds without the previously required mathematical background. In this work, we introduce Nomogen, a Python package able to build reliable and scalable 3-variable nomograms while avoiding past drawbacks such as manipulating determinants or manually drawing the scales. In this way, some nomograms generated by Nomogen have been tested on undergraduate and graduate students from different engineering backgrounds. Subsequently, a Likert scale survey was conducted, which showed that students had a great and renewed interest in nomography and found it helpful in the engineering learning process. Even though 78.4% of the respondent had never used nomograms, 86.5% believed that these analogical graphs allow a reasonable interpretation of the phenomenon when there are many variables, and, as a result, nomography with the assistance of open-source software, such as Nomogen or PyNomo, should be incorporated in the teaching process as part of their engineering education syllabus. [ABSTRACT FROM AUTHOR]

The design of bridges must balance sustainability and construction simplicity. A game-theory-based optimization method was applied in this research to find a sustainable steel–concrete composite bridge design. The sustainability was evaluated through cost and environmental and social impact using the Life Cycle Assessment method. The optimization process considered four criteria simultaneously, using a discrete version of the SCA algorithm and a transfer function for discretization. The preferred solutions were selected using the Minkowski distances approach. Results showed a decrease in slab reinforcement and an increase in the amount of steel in the cross-section, leading to only an 8.2‰ increase in cost compared to similar studies. Regarding the cross-section, the geometry obtained considers cells in the upper and lower parts of the webs to improve the bending resistance. The proposed method allows for the simultaneous optimization of multiple criteria and provides a sustainable yet simple bridge design solution. [ABSTRACT FROM AUTHOR]

The main objective of this study is to assess and contrast the efficacy of distinct spatial prediction methods in a simulation aimed at optimizing the embodied energy during the construction of prestressed slab bridge decks. A literature review and cross-sectional analysis have identified crucial design parameters that directly affect the design and construction of bridge decks. This analysis determines the critical design variables to improve the deck's energy efficiency, providing practical guidance for engineers and professionals in the field. The methods analyzed in this study are ordinary Kriging and a multilayer perceptron neural network. The methodology involves analyzing the predictive performance of both models through error analysis and assessing their ability to identify local optima on the response surface. The results show that both models generally overestimate the observed values. The Kriging model with second-order polynomials yields a 4% relative error at the local optimum, while the neural network achieves lower root mean square errors (RMSEs). Neither the Kriging model nor the neural network provides precise predictions but point to promising solution regions. Optimizing the response surface to find a local minimum is crucial. High slenderness ratios (around 1/28) and 40 MPa concrete grade are recommended to improve energy efficiency. [ABSTRACT FROM AUTHOR]

The structural design of prestressed arched trusses presents a complex challenge due to the need to balance multiple conflicting objectives such as structural performance, weight, and constructability. This complexity is further compounded by the interdependent nature of the structural elements, which necessitates a comprehensive optimization approach. Addressing this challenge is crucial for advancing construction practices and improving the efficiency and safety of structural designs. The integration of advanced optimization algorithms and decision-making techniques offers a promising avenue for enhancing the design process of prestressed arched trusses. This study proposes the use of three advanced multi-objective optimization algorithms: NSGA-III, CTAEA, and SMS-EMOA, to optimize the structural design of prestressed arched trusses. The performance of these algorithms was evaluated using generational distance and inverted generational distance metrics. Additionally, the non-dominated optimal designs generated by these algorithms were assessed and ranked using multiple multi-criteria decision-making techniques, including SAW, FUCA, TOPSIS, PROMETHEE, and VIKOR. This approach allowed for a robust comparison of the algorithms and provided insights into their effectiveness in balancing the different design objectives. The results of the study indicated that NSGA-III exhibited superior performance with a GD value of 0.215, reflecting a closer proximity of its solutions to the Pareto front, and an IGD value of 0.329, indicating a well-distributed set of solutions across the Pareto front. In comparison, CTAEA and SMS-EMOA showed higher GD values of 0.326 and 0.436, respectively, suggesting less convergence to the Pareto front. However, SMS-EMOA demonstrated a balanced performance in terms of constructability and structural weight, with an IGD value of 0.434. The statistical significance of these differences was confirmed by the Kruskal–Wallis test, with p-values of 2.50 × 10 − 15 for GD and 5.15 × 10 − 06 for IGD. These findings underscore the advantages and limitations of each algorithm, providing valuable insights for future applications in structural optimization. [ABSTRACT FROM AUTHOR]

This study aims to optimize the Environmental Life Cycle Assessment (LCA) and Life Cycle Cost (LCC) of NiZn batteries using Pareto Optimization (PO) and Multi-objective Particle Swarm Optimization (MOPSO), which combine Pareto optimization and genetic algorithms (GA). The optimization focuses on the raw material acquisition phase and the end-of-life phase of NiZn batteries to improve their sustainability Key Performance Indicators (KPIs). The optimization methodology, programmed in MATLAB, is based on a formulation model of LCC and the environmental LCA, using data available from the Ecoinvent database, the OpenLCA software (V1.11.0), and other public databases. Results provide insights about the best combination of countries for acquiring raw materials to manufacture NiZn and for disposing of the waste of NiZn batteries that cannot be recycled. These results were automatically linked to some sustainability KPIs, such as global warming and capital costs, being replicable in case of data updates or changes in production or recycling locations, which were initially considered at Paris (France) and Krefeld (Germany), respectively. These results provided by an AI model were validated by using a sensitivity analysis and the Analytical Hierarchy Process (AHP) through an expert panel. The sensitivity analysis ensures the robustness of mathematical parameters and future variations in the market; on the other hand, the AHP validates the Artificial Intelligence (AI) results with interactions of human factors. Further developments should also consider the manufacturing and use phases in the optimization model. [ABSTRACT FROM AUTHOR]