Deciding how to optimally deploy sensors in a large, complex, and spatially extended structure is critical to ensure that the surface pressure field is accurately captured for subsequent analysis and design. In some cases, reconstruction of missing data is required in downstream tasks such as the development of digital twins. This paper presents a data-driven sparse sensor selection algorithm, aiming to provide the most information contents for reconstructing aerodynamic characteristics of wind pressures over tall building structures parsimoniously. The algorithm first fits a set of basis functions to the training data, then applies a computationally efficient QR algorithm that ranks existing pressure sensors in order of importance based on the state reconstruction to this tailored basis. The findings of this study show that the proposed algorithm successfully reconstructs the aerodynamic characteristics of tall buildings from sparse measurement locations, generating stable and optimal solutions across a range of conditions. As a result, this study serves as a promising first step toward leveraging the success of data-driven and machine learning algorithms to supplement traditional genetic algorithms currently used in wind engineering.

翻译：在大型、复杂且空间延展的结构中，如何最优地部署传感器对于准确捕捉表面压力场以进行后续分析与设计至关重要。在某些情况下，下游任务（如数字孪生开发）需要重构缺失数据。本文提出一种数据驱动的稀疏传感器选择算法，旨在以最简方式提供最丰富的信息含量，用于重构高层建筑结构表面风压的气动特性。该算法首先对训练数据拟合一组基函数，然后应用计算高效的QR算法，根据基于状态重构对此定制基的重要性程度，对现有压力传感器进行排序。研究结果表明，所提算法能够成功地从稀疏测量位置重构高层建筑的气动特性，并在多种条件下生成稳定且最优的解决方案。因此，本研究作为利用数据驱动与机器学习算法补充风工程中传统遗传算法的一个有前景的初步探索。