Kàtia Gaspar
Biographic Data
| ID | 6276242 |
|---|---|
| NAME | Kàtia Gaspar |
| GIVEN NAMES | Kàtia |
| FAMILY NAME | Gaspar |
| SIGNATURE | GÁSPÁR K |
| AFFILIATIONS | Universitat Politècnica de Catalunya |
| ORCID | 0000-0003-3842-1401 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 1 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Context-adaptive deep learning for sustainable product recommendation
The increasing availability of sustainability and circularity data for construction products contrasts with the absence of automated multi-criteria decision-making tools, forcing practitioners to rely on cost and availability heuristics that systematically underutilise sustainable alternatives. This study presents a context-adaptive deep learning model that scores and ranks construction products, with concrete serving as the application domain. A…
A product-level framework for prioritizing circular and sustainable construction in building design
The construction sector is a major contributor to climate change, driven largely by its design and construction processes. Early-stage decisions are critical for achieving sustainability, yet these considerations are often introduced too late in projects. While existing tools such as multi-criteria decision-making methods and LCA-BIM integration support material selection, they typically operate at building scale or require complete datasets, lim…
Large-scale analysis of environmental and circularity metrics in construction products through automated data extraction using large language models
The selection of construction products significantly impacts building practices as well as environmental and biodiversity outcomes. However, current Environmental Product Declarations (EPDs) lack standardized circularity metrics and contain highly heterogeneous, manufacturer-dependent data, which severely limits sector-wide assessments and data-driven decision-making. Existing methods fail to systematically extract and structure this fragmented i…
Life-cycle environmental and cost-effective energy retrofitting solutions for office stock
The European Green Deal establishes the need to renovate buildings in an energy efficient way, to address climate and environmental challenges. The purpose of this study was to devise a model for identifying environmental, cost-effective retrofitting measures by assessing their energy, economic and environmental impact when they are applied to the entire office stock. The methodology builds upon the energy performance certificate scheme to identi…
Large-scale analysis of environmental and circularity metrics in construction products through automated data extraction using large language models
The selection of construction products significantly impacts building practices as well as environmental and biodiversity outcomes. However, current Environmental Product Declarations (EPDs) lack standardized circularity metrics and contain highly heterogeneous, manufacturer-dependent data, which severely limits sector-wide assessments and data-driven decision-making. Existing methods fail to systematically extract and structure this fragmented i…
Life-cycle environmental and cost-effective energy retrofitting solutions for office stock
The European Green Deal establishes the need to renovate buildings in an energy efficient way, to address climate and environmental challenges. The purpose of this study was to devise a model for identifying environmental, cost-effective retrofitting measures by assessing their energy, economic and environmental impact when they are applied to the entire office stock. The methodology builds upon the energy performance certificate scheme to identi…
Context-adaptive deep learning for sustainable product recommendation
The increasing availability of sustainability and circularity data for construction products contrasts with the absence of automated multi-criteria decision-making tools, forcing practitioners to rely on cost and availability heuristics that systematically underutilise sustainable alternatives. This study presents a context-adaptive deep learning model that scores and ranks construction products, with concrete serving as the application domain. A…
A product-level framework for prioritizing circular and sustainable construction in building design
The construction sector is a major contributor to climate change, driven largely by its design and construction processes. Early-stage decisions are critical for achieving sustainability, yet these considerations are often introduced too late in projects. While existing tools such as multi-criteria decision-making methods and LCA-BIM integration support material selection, they typically operate at building scale or require complete datasets, lim…
Large-scale analysis of environmental and circularity metrics in construction products through automated data extraction using large language models
The selection of construction products significantly impacts building practices as well as environmental and biodiversity outcomes. However, current Environmental Product Declarations (EPDs) lack standardized circularity metrics and contain highly heterogeneous, manufacturer-dependent data, which severely limits sector-wide assessments and data-driven decision-making. Existing methods fail to systematically extract and structure this fragmented i…
Sustainability (2 works) · Sustainable Building Design and Assessment (2 works) · Sustainable development (2 works) · Architectural engineering (1 work) · Artificial neural network (1 work) · Benchmarking (1 work) · BIM and Construction Integration (1 work) · Building construction (1 work) · Building Design (1 work) · Building Energy and Comfort Optimization (1 work)