This study explores the integration of expert design intuition and parametric data analysis. While traditional professional design expertise helps to rapidly frame relevant aspects of the design problem and produce viable solutions, it has limitations in addressing multi-criteria design problems with conflicting objectives. On the other hand, parametric analysis, in combination with data analysis methods, helps to construct and analyze large design spaces of potential design solutions and tradeoffs, within a given frame. We explore a process whereby expert design teams propose a design using their current intuitive and analytical methods. That design is then further optimized using parametric analysis. This study specifically explores the specification of geometric and material properties of building envelopes for two typically conflicting objectives: daylight quality and energy consumption. We compare performance of the design after initial professional design exploration, and after parametric analysis, showing consistently significant performance improvement after the second process. The study explores synergies between intuitive and systematic design approaches, demonstrating how alignment can help expert teams efficiently and significantly improve project performance.

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Parametric analysis is emerging as an important approach to building performance evaluation in architectural practice. Since architectural performance has many competing metrics multi-criteria analysis is required to deal effectively with the complexity. However, multi-criteria parametric analysis involves large design spaces that are expensive to compute. Machine learning is emerging as an important design space reduction method for multi-criteria analysis. However, there are many types of machine learning algorithms and architects can benefit from understanding which algorithms perform well on which tasks. Using a mid-rise commercial residential tower project this paper investigates three common machine learning algorithms for performance against three common performance metrics. The algorithms are multi-layer perceptrons, support vector machines, and random forests, while the metrics are site energy, illuminance, and a value function that combines them both. In addition, we seek to understand what factors are most impactful in improving algorithm performance. We investigate four impact factors namely sample size, sensitivity analysis, feature selection, and hyperparameters. We find that multi-layer perceptrons perform best for all three performance metrics. We also find that hyperparameter tuning is the most impactful factor affecting multi-layer perceptron performance.

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There is a lack of impactful tools or guidance for assessing water consumption or where there can be considerable savings from water capture. While there are many online calculators, spreadsheets, and models for considering predicted building water use, none of them introduce learning through motivation in the process. Gamification has been recognized in the literature to support motivation using design elements to promote behavior change and performance to a specific activity. The authors propose the use of simple design elements within an modified building performance methodology that can drive awareness and inform the decision-making process relative to the program.