Structural preliminary design for architecture aided by artificial inteligence
DOI:
https://doi.org/10.11606/gtp.v17i3.185781Keywords:
Preliminary design, structures, machine learning, surrogate modelAbstract
Architectural structural preliminary design and structural members pre-sizing are a frequent topic in the literature associated with building design; since several decades ago. In the past, pre-sizing was carried out analogically with the aid of simplified mathematical expressions or abacuses, which often causes rework due to its imprecision and its laborious manual method features. These processes have been changing over time, and more recently, digital technologies have provided opportunities for sophistication, offering speed and precision in the responses of the structural element’s thicknesses. Parametric modeling, finite elements structural simulation, structural design standard algorithmic verification and artificial intelligence can offer a great contribution to the preliminary structural design and pre-dimensioning process. In this article it is proposed a demonstration of a framework using a database with a structural system solution space and its processing through a surrogate model using non-linear regression through artificial intelligence. Clustering technique using the k-means algorithm and supervised trained machine learning with were used to obtain the structural system generalized model that offers the structural members’ thickness from the input of the spans, loads and strengths of the material. The generalized model obtained was subsequently successfully validated by the finite element method and the structural design code algorithmic verification, exhibiting a satisfactory processing speed on response, meeting with the agility that the early stages architectural design process demands and the accessibility that this model can deliver to the performance-based design paradigm.
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