In this paper, the Camp Nou stadium is used as a testbed for City Physiology, a theoretical framework for urban digital twins. With this case study, the modularity and adaptability of the framework, originally intended for city-scale simulations, are tested on a large facility venue. As a proof of concept, several statistical techniques and an agent-based simulation platform are coupled to simulate a crowd in the stadium, and a process of four steps is followed to build the case study. Both the conceptual (interdomain) and technical (domain specific) layers of the digital twin are defined and connected in a nonlinear process so that they represent the complexity of the object to be simulated. The result obtained is a strategy to build a digital twin from the domain point of view, paving the way for more complex, more ambitious simulators.

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The majority of current visual-algorithmic architecture is constricted to specific parameters that are gradient related, keeping their parts’ relation fixed within the algorithm, far away from a truly parametric modeling with a flexible topology. Recent findings around genetics and certain genes capable of shape conditioning (development) have succeeded in recovering the science of embryology as a valid field that connects and affects the evolutionary ecosystem, showing the existence of universal mechanisms that are present in living species, thus describing powerful strategies for generation and emergence. Therefore, a new dual discipline is justified: Evolutionary developmental biology science. Authors propose the convergence of genetics algorithms and simulated features from evolutionary developmental biology into a single data-flow that will prove itself capable of generating great diversity through a simple and flexible structure of data, commands, and polygonal geometry. For that matter, a case study through visual-algorithmic software deals with the hypothesis that for obtaining a greater emergence and design space, a simpler and more flexible approach might only be required, prioritizing hierarchical levels over complex and detailed operations.

The experiments analyzed in this paper focus their research on the use of Evolutionary Computation (EC) applied to a parametrized urban tissue. Through the application of EC, it is possible to develop a design under a single model that addresses multiple conflicting objectives. The experiments presented are based on Cerdà’s master plan in Barcelona, specifically on the iconic Eixample block which is grouped into a 4 × 4 urban Superblock. The proposal aims to reach the existing high density of the city while reclaiming the block relations proposed by Cerdà’s original plan. Generating and ranking multiple individuals in a population through several generations ensures a flexible solution rather than a single “optimal” one. Final results in the Pareto front show a successful and diverse set of solutions that approximate Cerdà’s and the existing Barcelona’s Eixample states. Further analysis proposes different methodologies and considerations to choose appropriate individuals within the front depending on design requirements.

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