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Intelligent structural design using generative adversarial networks (GANs) is a revolutionary design approach for building structures. Despite its far‐reaching capability, the data quantity and quality may have limited the performance of such a data‐driven network. This study proposes to enhance the objectiveness of training processes by innovatively introducing a surrogate model, Physics Estimator, that informs the generator by appraising the physical behavior of the generated design. Dual loss functions evaluated by a traditional data‐driven discriminator and the Physics Estimator collaboratively foster the physics‐enhanced GAN architecture. We further develop a structural mechanics model to train and optimize the inherent accuracy of the Physics Estimator. The comparative study suggests that the proposed physics‐enhanced GAN can generate structural designs from architectural drawings and specified design conditions 44% better than a data‐driven design method and 90 times faster than a competent engineer.

An efficient vibration control can reduce negative effects induced by environmental vibrations and thereby improve the performance of precision instruments and the qualities of manufacture. The performance of the widely used linear quadratic regulator control algorithm, a classical active control methodology, depends on the parameters of the control algorithm. Consequently, a set of fixed parameters cannot satisfy the demand for controlling various types of environmental vibrations. Therefore, this study proposes a vibration identification method based on a convolutional neural network. This method helps to optimize the linear quadratic regulator algorithm by selecting corresponding optimal parameters according to the identification results, thereby achieving the objective of optimal control subjected to various types of vibration inputs. Specifically, environmental vibration signals are collected, and the preliminary features of the vibrations (i.e. wavelet coefficient matrices or images) are adopted as input samples for the convolutional neural network. A genetic algorithm is used to optimize the parameters of the linear quadratic regulator algorithm for each type of vibration; subsequently, the trained convolutional neural network model with the best performance is used to identify the vibration and select the corresponding optimal parameters of the linear quadratic regulator algorithm under different types of vibration inputs. Case studies show that the performance of the improved linear quadratic regulator control method is significantly better than that of the conventional linear quadratic regulator algorithm with fixed parameters.

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