This study develops an autonomous design method for architectural shape sketches by a novel self‐sparse generative adversarial network (self‐sparse GAN), thereby overcoming the problems regarding excessive reliance on sufficient aesthetic knowledge and excessive time consumption in traditional human design. First, a new architectural shape dataset denoted “Sketch” is built by using the eXtended difference‐of‐Gaussians operator. Second, a self‐adaptive sparse transform module (SASTM) is designed following each deconvolution layer of the proposed self‐sparse GAN to utilize the sparsity of sketch images by the sparsity decomposition and feature‐map recombination. Third, the Frechet inception distance (FID) is adopted to evaluate the quality of the generated sketches by comparing the distribution of the real and generated datasets. Finally, two common image generation approaches, Wasserstein GAN with gradient penalty and self‐attention GAN, are compared with the proposed self‐sparse GAN, and results show the proposed method achieves the best performance with a relative decrease in the FID score of 11.87%. The proposed autonomous design method can give tens of thousands of sketches for a class of buildings in a few seconds using the trained network, which can help architects to choose the architectural form and/or inspire architects to consider unique schemes in the early stages of design.

Artificial intelligence (AI) provides advanced mathematical frameworks and algorithms for further innovation and vitality of classical civil engineering (CE). Plenty of complex, time-consuming, and laborious workloads of design, construction, and inspection can be enhanced and upgraded by emerging AI techniques. In addition, many unsolved issues and unknown laws in the field of CE can be addressed and discovered by physical machine learning via merging the data paradigm with physical laws. Intelligent science and technology in CE profoundly promote the current level of informatization, digitalization, autonomation, and intellectualization. To this end, this paper provides a systematic review and summarizes the state-of-the-art progress of AI in CE for the entire life cycle of civil structures and infrastructure, including intelligent architectural design, intelligent structural health diagnosis, intelligent disaster prevention and reduction. A series of examples for intelligent architectural art shape design, structural topology optimization, computer-vision-based structural damage recognition, correlation-pattern-based structural condition assessment, machine-learning-enhanced reliability analysis, vision-based earthquake disaster evaluation, and dense displacement monitoring of structures under wind and earthquake, are given. Finally, the prospects of intelligent science and technology in future CE are discussed.