## 1 Introduction  
Artificial intelligence (AI) has become an integral part of the construction industry, sparking a recent increase in related research in the field. Numerous studies are currently underway that focus on construction optimization, drawing on the principles of AI, data, and optimization theories. They are subdivided into numerous specialized fields, including topology optimization, computer-aided engineering (CAE), simulations, and generative design [1, 2]. However, these studies have gradually evolved into research centered on generative design, a concept in which AI autonomously creates design alternatives, a process that is time-consuming for humans. Generative design, in essence, is a technology that creates a variety of optimal designs that match the design objectives, constraint classifications, and their respective levels, specified by the designer or engineer [3, 4]. Generative design is a technology-driven process that uses algorithmic or rule-based systems to generate design outputs. In the construction domain, this approach provides more efficient, innovative, and customized solutions [5, 6]. In 2020, the construction industry encountered a severe crisis due to decreasing productivity and an aging workforce [7]. Consequently, a number of solutions were proposed. First, from a technological standpoint, construction automation initiativesexploiting the possibilities of the Fourth Industrial Revolution and AI were implemented. In particular, fundamental research was conducted in the field of design automation. Second, from an economic perspective, progress was made in increasing productivity in construction by creating a national-level research foundation for the advancement of construction software. Finally, from a societal perspective, efforts have been made to replace low-value, repetitive tasks with automated solutions to address the problems of an aging workforce and the avoidance of construction employment. In addition, the intersection of AI with various domains has the potential to reduce conventional theories and redefine innovation. For example, AlphaGo, an AI program developed by Google DeepMind for the Go game, broke new ground in 2016 as the first AI program to defeat a professional Go player [8]. This victory was a result of the AI's game-changing strategy, which significantly challenged conventional Go theories, prompted efforts to debunk them, shattered preconceptions, and prompted a comprehensive reevaluation of human Go strategies. Similarly, if AI-driven design automation techniques are cultivated in the construction industry, they have the potential to transform existing frameworks and significantly increase productivity in construction [9]. Generative design approaches are not only gaining traction in building and architectural practices, but they also represent a significant scientific contribution to these fields, as shown by the increasing research literature [1,2]. This research addresses a critical gap by developing a sophisticated algorithm that generates numerous design alternatives, taking into account a comprehensive set of conditions--design, regulatory, and evaluative--early in the design phase. The novelty and scientific contribution of this work lies in its holistic exploration of generative design's ability to boost productivity in the construction industry and reduce the repetitive, non-essential tasks that burden the aging workforce. We present an approach that begins with an exhaustive review of the existing literature, leading to a systematic categorization of generative design methods based on our extensive analysis. Moreover, this study breaks new ground by applying a generative design framework to the architectural design process, aiming to yield a varied portfolio of layout options and to evaluate a subset of them to identify optimal solutions. Our research delivers pivotal insights into the evolution of AI and AI-assisted design automation, spotlighting the advancements in generative design within the construction industry.