An Integrated Decision Framework for Sustainable and Code-Compliant Building Design Using Building Information Modeling and Multi-Criteria Performance Assessment
This study proposes an integrated decision framework that combines Building Information Modeling (BIM), performance assessment, and multi-criteria decision analysis to simultaneously evaluate regulatory compliance, environmental performance, and construction feasibility for sustainable building design, demonstrated through a controlled illustrative dataset using existing benchmark resources.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Designing a building is rarely just about drawing walls and choosing a roof. It is a complex balancing act where architects must satisfy a web of often competing demands. They need to ensure the structure is safe and follows local laws, that it uses energy efficiently, that it keeps people comfortable inside, and that it can be built within a specific budget. For decades, these different goals were often checked separately. An engineer might verify the energy use, a lawyer might check the fire codes, and a cost estimator might review the budget, but these checks happened in isolation. This separation often led to conflicts, where a design that looked perfect for energy savings might fail a safety rule, or a cheap option might be uncomfortable to live in. The challenge has been to bring all these different pieces of information together into a single, clear picture so that designers can see the whole picture before construction begins.
A new study by Md Monasur Rahman proposes a way to solve this by creating a unified decision-making system. The approach relies on a digital technology called Building Information Modeling, which acts as a detailed digital twin of a building, containing every piece of information about its shape, materials, and systems. The study combines this digital model with a method for checking strict building rules and a scoring system that weighs different performance goals against each other. The goal is not to find a single perfect building, but to provide a clear, step-by-step process that helps designers choose the best possible option from a list of candidates, ensuring that the chosen design is both safe by law and high-performing in every other way.
The researchers built a framework that starts by gathering all the necessary data into one place. This includes the physical shape of the building, the materials used for the walls and windows, the heating and cooling systems, and the schedule of how people will use the space. From this central digital source, the system pulls out specific numbers to measure how the building will perform. It looks at how much energy the building will use, how much natural light will enter the rooms, how comfortable the temperature will feel for the people inside, and how much carbon is emitted during both the construction and the operation of the building. It also calculates the total cost of the building over its entire life, from the initial construction to decades of maintenance.
Once these performance numbers are gathered, the system applies a strict filter based on building codes. This is a crucial step that separates the study from other methods that might try to trade safety for savings. The system checks the design against six mandatory categories: fire safety, accessibility for people with disabilities, structural strength, zoning rules about height and size, emergency evacuation routes, and energy efficiency standards. If a design fails even one of these checks, it is immediately removed from consideration. No amount of energy savings or low cost can make up for a failure in fire safety or structural integrity. This ensures that only designs that are legally and physically safe move forward to the next stage.
For the designs that pass this safety filter, the system then uses a scoring method to rank them. It treats the different performance goals—like energy use, cost, and comfort—as a single list of criteria. In the specific example shown in the study, the researchers gave equal importance to all eight criteria to demonstrate how the system works. They took the raw numbers for each design and converted them into a standard score, so that a low energy bill could be compared fairly against a high comfort rating. The design with the highest total score across all these areas is identified as the best choice.
The study tested this framework using three different hypothetical building designs, labeled A, B, and C. These were not real buildings being constructed, but carefully created examples to show how the math works. Design A had the lowest energy use, the best natural light, the lowest carbon emissions, and the highest comfort scores, though it was not the cheapest to build. Design B had the lowest total cost over its lifetime but performed worse than Design A in almost every other category, including energy and comfort. Design C had a mix of average and poor numbers and, critically, failed three of the mandatory safety checks: it did not meet fire safety rules, zoning regulations, or energy code requirements.
When the system ran the numbers, the result was clear. Design C was rejected immediately because it failed the safety filter, proving that a design cannot be chosen if it breaks the law, no matter how good its other numbers look. Between the two remaining options, Design A was selected as the winner. Even though Design B was cheaper, Design A scored higher overall because it excelled in energy efficiency, environmental impact, and human comfort. The study shows that by separating the mandatory safety checks from the performance ranking, designers can avoid the confusion of trying to weigh a safety violation against a cost saving.
The researchers are careful to note that this work is a demonstration of a method rather than a final solution for every building. The numbers used for the three designs were illustrative values created to show the calculation process, not measurements from a real building or results from a new computer simulation run specifically for this paper. The study relies on existing public data and standard building references to define how the system should work. The author suggests that for the method to be fully proven, future work would need to apply it to real projects with actual simulation data and specific local laws. However, the framework itself offers a solid, logical path forward. It provides a way to stop the confusion of conflicting goals and gives designers a clear, consistent way to find the best possible building that is safe, sustainable, and comfortable for the people who will live and work inside.
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