An Automated Design Method for Plane Trusses Based on User Preference Information

Abstract

Structural design is a complicated decision-making process involving multiple qualitative and quantitative factors. Currently, most automated design methods consider only quantitative objectives and constraints, ignoring the qualitative design information that is difficult define mathematically, such as the user preference for structural shapes. This limits the functionality and efficiency of such design methods. In this study, a design method named STSA-P is proposed for plane trusses to incorporate user preference into the automatic design process. Two main problems are addressed, i.e., how to quantify user preference information and how to coordinate it with other quantitative design objectives. A prediction model of user preference is developed for the first problem by generating the data set and selecting an appropriate machine learning (ML) algorithm. Specifically, a set of truss features quantitatively representing the structural shapes are identified for the truss sample population. Furthermore, an interactive system is developed for collecting user evaluation information as data labels. Strategies for reducing user fatigue are also considered during the evaluation process. A set of numerical experiments are conducted to select the suitable ML algorithm. Regarding the second problem, the physical programming method is modified to construct a new aggregate function which effectively coordinates user preference with other design objectives. A cost function is designed by considering the design constraints. On this basis, the prediction model is incorporated into the Structural Topology and Shape Annealing (STSA) method to form the STSA-P method. Two students are invited to perform a design case using the STSA-P method. It is demonstrated that the results verify the practicality and validity of the proposed method.

Selection Criterion of Reanalysis Methods for Plane Truss Optimization

“…After analyzing the flops and MS values of various reanalysis methods, the relevant results are summarized in Table 2. Please refer to [31] for detailed information.…”

“…To validate the effectiveness of the proposed selection criterion, two types of numerical case studies were conducted in this section. One type is an example of a truss design based on the STSA-P system [32]. As the DOFs for this type of structural design are generally low, another example type of truss structural optimization with higher DOFs was designed.…”

“…The STSA-P method [32] can convert the user's design preferences for structural appearance into design objectives, thus achieving the effect of guiding structural optimization based on user preferences. This method was applied to design a two-hinged arch truss structure with a span of 20 m. Figure 8 shows the initial structure and load conditions.…”

The Development of an Experimental Framework to Explore the Generative Design Preference of a Machine Learning-Assisted Residential Site Plan Layout

“…The current generative design, which is built on machine learning, has met the need for devising a great number of design ideas. However, in the age of big data, most automatic design methods only look at quantitative goals and constraints and ignore qualitative design information, which is hard to describe mathematically [3,4].…”