# Concert Halls as Nearly Adaptive Spaces
## 3 Methodology  
In the digital transformation era, adaptive concert halls (ACHs) were introduced through digital twins in the architecture, engineering, and construction (AEC) industry as part of the level of process automation and control towards Construction 4.0. The proposed methodology allows for adaptive concert halls to incorporate the ability to detect and control all the main factors (explained in the previous section) that directly or indirectly influence the room acoustic quality. A dynamic process optimization to support the building lifecycle management (BLM) is applied, which is especially useful before and during a concert. Computerization and digitization are introduced in the presented methodology as they begin to have a significant impact on how physical/engineering properties can be handled during the life cycle of the building [31; 32]. The acquisition, exchange, use, and control of data and information throughout an asset's entire life are, in fact, considered to be among the most challenging aspects of implementing building information modelling, so-called BIM, in asset management [33]. Digital twins (DTs), and the Internet of Things (IoT), on the other hand, have drawn interest because of their synergistic and information-management functionalities [34]. In this methodology, it is assumed that the passive room acoustic quality of the physical space of the ACH is already defined in the design and construction phase according to the intended use, i.e., mainly for concerts requiring an almost natural acoustic response, while the DT is developed for visualization, modelling, simulation, and optimization of the room acoustic quality during the management phase, including concert execution. The DT contains three main components to create a practical loop: a physical entity, a virtual entity, and a data link [35]. For the dynamic mapping in the DT, temperature, humidity, and air velocity data are gathered in the physical world and subsequently transmitted to the virtual world for further analysis. Optimization controls are achieved in the virtual model by learning data from the multiple sensors. Acoustic parameters are collected through measurements in the commissioning phase; algorithms are implemented to offer prompt solutions to guide actual hall configurations in adaptation to changing contexts, including dynamic curtain positions and different occupancy rates. The first algorithm calculates the curtains' area in real time and the CLW coefficient, knowing the curtains' position and their banner length (information coming from the sensors) and the curtain width (input value). In the second algorithm, Equation (3) is solved, and is included in the estimation of m as an interpolation of the data from the literature, with air humidity conditions coming from specific sensors. The DT gathers and presents real-time data on the physical assets, utilizes these data to run various data-driven simulations, visualizes the 3D BIM, and introduces automation. Through the use of sensors, encoders, actuators, and software of the curtain system which lowers the drapes from above, the position and stop level of the curtain can be known in real time, allowing for the relative absorption area to be calculated immediately through the first simple algorithm. A bidirectional system that manages the behavior of physical assets becomes possible in the DT, following the musicians' requests and visualizing the reverberation time. The novelty of adaptive concert halls lies in that the DT allows for curtains to be moved during a performance, according to the composer's indications included in the score, introducing a natural dynamic acoustic response to the hall, which actively affects the performance of the music. A previous necessary technical requirement for the engine that moves the curtains was that its noise must be minimized. Another necessary technical requirement was that the engine velocity had to be fast enough to create variable acoustic effects suitable for the composer's needs.