# Artificial Intelligence in the Construction Industry:
## 3 Results
### Main Path Analysis
#### 3.1.1 Key-Route Main Paths with Review Articles
[PERSON] and [PERSON] [23] proposed a GA-based optimization metaheuristic as a tool for generative and goal-directed design to evaluate the thermal and lighting performance of an office building. The simulation results can be visualized with an AutoLisp routine for window placement and size, and guide the search of the GA-based algorithm to find an optimal configuration under different climate conditions. The same approach can be applied to a variety of other architectural design problems, such as selecting construction materials, designing shading elements, or sizing a building's lighting and mechanical systems. [PERSON] et al. [24] presented a design method that combines parametric modeling and genetic algorithms to solve performance-based architectural design problems. This approach can automatically generate a wide range of alternative design solutions to explore architectural geometry, based on the performance evaluations in the early design phase. The two case studies have shown that this method also supports retrospective design investigations, improves vertical transformations within the conceptual design, and decomposes complex aspects into multiple levels of abstraction by achieving multi-level design solutions. Among the Phase I articles, [PERSON] [25] had the highest citation count of 366, making it the most notable in the network. The author reviewed and summarized 74 papers dealing with the application and improvement of computational optimization technologies, including direct search, evolutionary methods, and other bio-inspired algorithms, for sustainable building design problems, and pointed out that the application and evaluation of computational optimization technologies to architectural design problems is steadily increasing due to the ever-growing computational power, and many designers have realized its great potential, such as optimization technologies for all aspects of architectural design. [PERSON] and [PERSON] [26] proposed a design framework called Evolutionary Energy Performance Feedback for Design (EEPFD) for early-stage architectural designs, which enables rapid iterations with performance feedback through parameterization, automation, and multi-criteria optimization to support decision making in the early stage of architectural design. The four representative articles in the middle path reveal that the application of AI-based optimization algorithms deserves further exploration and quantification. In the remaining representative publications of Phase I, four articles appeared on the right stream of the key-route main paths, namely MichalekCP2002 [27], Malkawi2004 [28], LinG2014b [29], and DiazAMG2017 [30]. The right path highlights the application of different AI-based optimization models in architectural design from different perspectives. In the first article on the right path, [PERSON] et al. [27] proposed an optimization model for the quantifiable aspects of architectural floorplan layout design. In this article, [PERSON] et al. combined gradient-based algorithms with evolutionary algorithms to present a new approach for optimizing floorplan layouts. The next representative article on the right path is [PERSON] [28], which reviewed the developments in the field of environmental performance simulation in the context of architectural design. In this article, the author described several new optimization techniques for developing environmental performance simulation tools, as well as some of the challenges that exist in this research area. [PERSON] [28] stated that simulation is far from being fully integrated into the architectural design process and further research is needed. The two remaining representative articles on the right path explored the application of Multidisciplinary Design Optimization (MDO) with softwaretools in architectural design. [PERSON] and [PERSON] [29] presented an MDO framework to consider energy performance in the early stage of the architectural design process.