Environmental benchmarking of building typologies through BIM-based combinatorial case studies
| Autor: | Carmen Galán-Marín, Madelyn Marrero, Carlos Rivera-Gómez, Alejandro Martínez-Rocamora |
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| Přispěvatelé: | Universidad de Sevilla. Departamento de Construcciones Arquitectónicas II (ETSIE), Universidad de Sevilla. Departamento de Construcciones Arquitectónicas I (ETSA), Universidad de Sevilla. TEP172: Arquitectura: Diseño y Técnica, Universidad de Sevilla. TEP206: Sath Sostenibilidad en Arquitectura, Tecnología y Patrimonio: Materialidad y Sistemas Constructivos, Universidad de Sevilla |
| Rok vydání: | 2021 |
| Předmět: |
Impacto medioambiental
Architectural engineering Life-cycle assessment Computer science Ciclo de vida de edificación 1203.09 Diseño Con Ayuda del Ordenador Constructive 3305.01 Diseño Arquitectónico Prediction model Building Information Modeling (BIM) Machine learning Edificación residencial Environmental impact assessment Proyectos de edificación 3308.04 Ingeniería de la Contaminación Roof Civil and Structural Engineering Algoritmos business.industry Scale (chemistry) Impact categories Building and Construction Benchmarking Dwellings Sostenibilidad Random forest 3305.14 Viviendas Building information modeling 3311.02 Ingeniería de Control Control and Systems Engineering Building information modelling business |
| Zdroj: | idUS. Depósito de Investigación de la Universidad de Sevilla instname |
| Popis: | Integrated life-cycle assessment (LCA) tools have emerged as decision-making support for BIM practitioners during the design stage of sustainable projects. However, differences between methodologies applied for determining the environmental impact of buildings produce significant variations in the results obtained, making them difficult to be compared. In this study, a methodology is defined for generating environmental benchmarks for building typologies through a combination of BIM-based LCA tools and machine learning techniques. When applied to an 11-story residential building typology with 92 dwellings by varying the constructive solutions of façades, partitions, roof and thermal insulation materials, results fall within a range from 360 to 430 kgCO2eq/m2. The Random Forest (RF) algorithm is successfully applied for identifying the most decisive variables in the analysis (partitions and façades), and shows signs of being useful for predicting the environmental impact of future constructions and to be applied to the analysis of greater scale urban zones. © 2021 The Authors |
| Databáze: | OpenAIRE |
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