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Chaos is embedded to the he Charged System Search (CSS) to solve practical optimization problems. To improve the ability of global search, different chaotic maps are introduced and three chaotic-CSS methods are developed. A comparison of these variants and the standard CSS demonstrates the superiority and suitability of the selected variants for practical civil optimization problems.

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Tunnel structures are known as expensive infrastructures and determining optimum designs of these structures can play a great role in minimizing their cost. The formulation of optimum design of industrial tunnel sections as an optimization is considered in this paper and then the enhanced charged system search, as a recently developed meta-heuristic approach, has been applied to solve the problem. The results and comparisons based on numerical examples show the efficiency of the optimization algorithm.

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The emergence of Generative Artificial Intelligence (GenAI) presents new possibilities for transforming structural optimization processes in civil and structural engineering. Unlike traditional AI models focused on prediction or classification, GenAI models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models, and Large Language Models (LLMs), enable the generation of novel structural designs by learning complex patterns within design-performance data. This paper provides a comprehensive review of how GenAI can support tasks such as design generation, inverse design, data augmentation for surrogate modeling, and multi-objective trade-off exploration. It also examines key challenges, including constraint integration, model interpretability, and data scarcity. By evaluating recent applications and proposing hybrid frameworks that blend generative modeling with domain knowledge and optimization strategies, this study outlines a research roadmap for the responsible and effective use of GenAI in structural optimization. The findings emphasize the need for interdisciplinary collaboration to translate GenAI’s creative potential into physically valid, structurally sound, and engineering-relevant solutions.