This research focuses on harnessing artificial neural networks (ANNs) to enhance the design of steel structures. The design process encompasses various stages, including defining the building’s geometry, estimating loads, selecting an appropriate structural system, sizing components, and creating detailed plans. Optimizing the weight of these structures is vital for reducing costs, improving efficiency, and minimizing environmental impact. This study specifically investigates multilayer perceptron (MLP) neural networks to optimize steel structure design. It evaluates different ANN configurations with varying numbers of hidden layers and neurons to find the most effective arrangement. Additionally, the performance of MLP networks is compared to that of logistic regression. The results demonstrate that MLP networks deliver superior accuracy in optimizing the design of steel structures compared to logistic regression. The process of designing steel structures at an early stage can reduce the consumption of energy and raw materials before the production of the structures themselves begins. This is important from an economic point of view because some costs can be reduced during the design process. When designing steel structures, it is also possible to take into account changing conditions, such as the growing share of renewable energy sources in the total energy balance in many countries.

Growing interest in reducing the environmental impacts of pavement preservation materials has increased the need for evaluations of emulsified asphalt. This study applies a cradle-to-gate Life Cycle Assessment (LCA) to quantify and compare the environmental profiles of four widely used cationic emulsions in the United States: CSS 1, CSS 1H, CRS 2, and CRS 2P. The assessment incorporates primary data collected from 17 manufacturing plants across different regions, supported by information from suppliers and secondary datasets from the 2023 GaBi database. Impact calculations were performed using GaBi software, and sensitivity analyses were conducted to determine which formulation components most influence the results. Environmental impact categories in the study include global warming potential (GWP), particulate matter (PM_2.5), photochemical ozone creation potential (POCP), and renewable and nonrenewable primary energy demand. The findings show that rapid-setting emulsions (CRS 2 and CRS 2P) generally exhibit lower environmental burdens than slow-setting emulsions (CSS 1 and CSS 1H). Differences in asphalt binder content, emulsifier dosage, and latex additions were identified as major contributors to environmental performance. By identifying key drivers of environmental impact, this study supports more sustainable material choices in pavement preservation. The results provide updated industry-specific LCA benchmarks for emulsified asphalt and highlight opportunities for environmental improvement through targeted adjustments to product formulations.