Predictive Modeling for Carbon Footprint Optimization of Prestressed Road Flyovers
[EN] This study addresses the challenge of minimizing carbon emissions in designing prestressed road flyovers by comparing advanced predictive modeling techniques for surrogate-based optimization. The research develops a two-stage optimization approach. First, a response surface is generated using L...
| Autores: | , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/230024 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/230024 |
| Access Level: | acceso abierto |
| Palabra clave: | Carbon footprint Neural networks Kriging Sustainability Post-tensioned bridges 09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación |
| Sumario: | [EN] This study addresses the challenge of minimizing carbon emissions in designing prestressed road flyovers by comparing advanced predictive modeling techniques for surrogate-based optimization. The research develops a two-stage optimization approach. First, a response surface is generated using Latin-hypercube sampling. Second, that response surface is optimized to identify design configurations with the lowest CO2 emissions. The optimal configuration (deck #37)¿base width 3.40 m, deck depth 1.10 m, and concrete grade C-35 MPa¿achieved a carbon footprint of 386,515 kg CO2, representing a reduction of 12% compared to the reference bridge. Among the models tested, the artificial neural network (ANN) achieved the highest predictive accuracy (RMSE = 8372 kg, MAE = 7356 kg), closely followed by the Kriging 1 model (RMSE = 9235 kg, MAE = 7236 kg). Results indicate that emissions remain minimal for deck depths between 1.10 and 1.30 m, base widths between 3.20 and 3.80 m, and concrete grades of C-35 to C-40 MPa. This study provides practical guidelines for reducing the carbon footprint of prestressed bridges and highlights the value of robust surrogate models in sustainable structural optimization. |
|---|