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Abstract
Recent advances in construction Digital Technologies (DT) have renewed interest in Building Information Modeling (BIM). At the same time, concerns about the environmental impact of the building sector continue to grow. Yet, studies that link BIM with Water Efficiency Analysis (WEA) remain limited. Most available work offers only simplified assumptions, partial simulations, or narrow approaches focused on specific modeling tasks. As a result, many buildings still rely on oversized Water Networks (WN). These systems increase carbon and water footprints and pose sanitary risks due to long periods of water stagnation. This research seeks to address these issues by developing a method to improve WN through DT while reducing their environmental impact. The study adopts a mixed approach that integrates BIM, Metaheuristics, and Input–Output (IO) analysis. The first part of the study analyzed the influence of Peak Water Demand (PWD) in WN and introduced a procedure to estimate it using standardized information. The method was evaluated through a residential case study. The results showed that the proposed approach provides consistent PWD estimates and performs better than the methods currently used in practice. The predicted demand was significantly lower, with values that were about 2.6 times smaller than those obtained through traditional procedures. The method also produced results that were close to the measurements collected on site. Even so, its purpose is not to replicate the exact observed values. A perfect match could reduce the safety margin and lead to undersized systems that fail during unusual or high-demand conditions. The second stage evaluated a methodology to integrate WEA within a BIM environment. Autodesk Revit was chosen as the primary platform because it is widely adopted and can connect different digital tools through a single model. The three proposed domains showed consistent improvements in water savings and reductions in electrical power. Their structure and customization increase the modularity of the methodology. This allows the process to adapt to projects of different scales while keeping a clear and practical workflow. These features help designers and professionals identify relevant elements and parameters early in the design phase. This leads to better water use outcomes and improves the performance of the WN throughout the following stages of the project. The third stage focused on creating a BIM-Metaheuristics algorithm to optimize WN. This part of the research stands out because it requires low technological resources while maintaining high precision in the selection of optimal pipe diameters. The method incorporates environmental factors and hydraulic constraints in a single optimization process. The results indicate that the model can reduce pipe sizes by one nominal diameter in most cases. In more demanding scenarios, the reduction can reach two diameters. These adjustments are obtained while decreasing environmental impact, lowering costs, and minimizing computational demands. The approach is flexible and can be applied to a wide range of building contexts. It consistently produces optimal configurations for WN. This contributes to a shift in how environmental performance is evaluated in plumbing design. This stage also explores the blue and carbon footprint assessment-based BIM-Input Output (BIM-IO) using Multifunctional Analysis of Regions Through Input-Output (MARIO) tool. The proposed framework demonstrated simplicity and ease of use for assessing Blue and Green water footprints in buildings using MARIO and BIM. BIM’s applicability across various building environments enables extensive data extraction. This includes detailed information from systems such as structure, architecture, HVAC, and plumbing. The level of detail depends on the Level of Detail (LOD) used in the model. The outcomes from the third stage were analyzed to determine the environmental impact and development of new policies.
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https://orcid.org/0000-0001-6997-3415