# An Automated Design Method for Plane Trusses Based on User Preference Information
## 2 Related Work  
### Research on User Design Preference  
User design preference is one of the typical qualitative factors. It plays a significant role in the design process. Considerable studies have been devoted to studying the effects of user preference, including research on modeling and quantifying the preference information. In earlier research, user preference was abstracted as some simple rules such as balance, rhythm, and proportionality [4]. Among these rules, the golden ratio is the best-known one and has been widely applied in architectural design. In the field of industrial design, the Kansei Engineering method [5; 6] is a popular design method that assesses user preference via questionnaires, interviews, etc., and utilizes the information to meet the user's expectations in the product design. This method provides a reference for the quantitative research of subjective information in other fields.  
The research on user preference in structural design can be grossly divided into two categories according to the application form of the information. Some researchers believe that the preference information is almost impossible to quantify. Thus, they integrate the user into the optimization system to replace the fitness function. The preference information is expressed through the user's judgments and choices during the optimization process. Interactive evolutionary computation (IEC) is a typical method of this category, which combines human subjective evaluation with an evolutionary optimization algorithm [7]. [PERSON] et al. [8; 9] performed an interactive design for discrete structures with a genetic algorithm (GA). During the process, a user can select some preferred solutions in every generation as the parents for the subsequent generation until the optimal design is achieved. [PERSON] et al. [10; 11] extended this method to the design of rigid frame structures. Other than GA, other heuristic algorithms were also used. [PERSON] et al. [12] proposed an interactive framework for truss design based on the particle swarm optimization (PSO) algorithm. Within the framework, a user can use NURBS curves to draw the initial design and evaluate the solutions iteratively through an interface until the final design is completed [12; 13; 14].  
Other researchers tend to use user preference as fundamental information and quantify it into formulas or models before the search process. [PERSON] et al. [15] developed a supporting system for the aesthetic design of dam structures. They used the analytic hierarchy process (AHP) method to represent the user preference in a fitness function. [PERSON] et al. [16] simulated user aesthetic evaluation information in the design of concrete retaining walls based on neural networks. For the design of discrete structures, a remarkable work was conducted by [PERSON] and [PERSON] [17]. The authors modeled the user preference based on neural networks and combined it with the GA method for generating Pareto-optimal designs.  
There are some disadvantages to the interactive design research. Firstly, user preference is not quantified nor modeled, thus the preference information cannot be reused. Secondly, the lengthy interaction can cause user fatigue problems, which reduce the design quality and efficiency [18]. By contrast, the quantitative research can avoid these problems.  
But how to ensure the accuracy of the user preference represented by the formulas or models is still challenging. Therefore, it seems viable to develop a model that can accurately predict user preference and then incorporate the preference information into the structural design process.