A 3D, performance-driven generative design framework: automating the link from a 3D spatial grammar interpreter to structural finite element analysis and stochastic optimization

Zimmermann, Luca; Chen, Tian; Shea, Kristina

doi:10.1017/s0890060417000324

Cited by 14 publications

(10 citation statements)

References 35 publications

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“…On the other hand, in shape grammar design variations are generated by synthesizing different features into the initial design with the aid of geometric rules, which ensure the creation of feasible shapes [19]. Some common applications of shape grammars are architectural design [23], 2D automotive design [24], embroidery design [25], and wheel shapes with the integration of Finite Element Analysis (FEA) [26]. L-systems [27] are a variation of shape grammars, and utilize a set of production rules based on string rewriting mechanisms to formalize design alternatives.…”

Section: Generative Designmentioning

confidence: 99%

Evolving a Psycho-Physical Distance Metric for Generative Design Exploration of Diverse Shapes

Khan

Günpinar

Moriguchi

et al. 2019

Journal of Mechanical Design

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In this paper, a generative design approach is proposed that involves the users’ psychological aspect in the design space exploration stage to create distinct design alternatives. Users’ perceptual judgment about designs is extracted as a psycho-physical distance metric, which is then integrated into the design exploration step to generate design alternatives for the parametric computer-aided design (CAD) shapes. To do this, a CAD model is first parametrized by defining geometric parameters and determining ranges of these parameters. Initial design alternatives for the CAD model are generated using Euclidean distance-based sampling teaching–learning-based optimization (S-TLBO), which is recently proposed and can sample N space-filling design alternatives in the design space. Similar designs are then clustered, and a user study is conducted to capture the subjects’ perceptual response for the dissimilarities between the cluster pairs. In addition, a furthest-point-sorting technique is introduced to equalize the number of designs in the clusters, which are being compared by the subjects in the user study. Afterward, nonlinear regression analyses are carried out to construct a mathematical correlation between the subjects’ perceptual response and geometric parameters in the form of a psycho-physical distance metric. Finally, a psycho-physical distance metric obtained is utilized to explore distinct design alternatives for the CAD model. Another user study is designed to compare the diversification between the designs when the Euclidean and the suggested psycho-physical distance metrics are utilized. According to the user study, designs generated with the latter metric are more distinct.

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Section: Generative Designmentioning

confidence: 99%

Evolving a Psycho-Physical Distance Metric for Generative Design Exploration of Diverse Shapes

Khan

Günpinar

Moriguchi

et al. 2019

Journal of Mechanical Design

View full text Add to dashboard Cite

show abstract

“…As an alternative to single design outputs, generative design is a recent design process to produce multiple outputs rather than a single result to satisfy a given condition. Genetic algorithms have been used to produce many different designs [ 13 ]. The genetic algorithm tests many samples within the design space and iterates on the highest performing samples, adjusting the variables according to their performance until an optimal sample is found.…”

Section: Introductionmentioning

confidence: 99%

Integrating Geometric Data into Topology Optimization via Neural Style Transfer

et al. 2021

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This research proposes a novel topology optimization method using neural style transfer to simultaneously optimize both structural performance for a given loading condition and geometric similarity for a reference design. For the neural style transfer, the convolutional layers of a pre-trained neural network extract and quantify characteristic features from the reference and input designs for optimization. The optimization analysis is evaluated as a single weighted objective function with the ability for the user to control the influence of the neural style transfer with the structural performance. As seen in architecture and consumer-facing products, the visual appeal of a design contributes to its overall value along with mechanical performance metrics. Using this method, a designer allows the tool to find the ideal compromise of these metrics. Three case studies are included to demonstrate the capabilities of this method with various loading conditions and reference designs. The structural performances of the novel designs are within 10% of the baseline without geometric reference, and the designs incorporate features in the given reference such as member size or meshed features. The performance of the proposed optimizer is compared against other optimizers without the geometric similarity constraint.

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“…In addition, CPT is a commonly used soil exploration survey in engineering applications because it is quick, economic, and could continuously obtain physical soil parameters (Eslami, 1997; Abu-Farsakh and Titi, 2004). In recent years, some researchers developed concepts of advanced artificial intelligence (AI) in different engineering applications, such as engineering mechanics, design of energy-saving buildings, automation, and construction (Coello and Aguirre, 2002; Nariman-Zadeh et al ., 2003; Hanna, 2007; Padmini et al ., 2008; Rossi and Lanzetta, 2013; Hu et al ., 2014; Zeng et al ., 2014; Kontovourkis et al ., 2015; Salido et al ., 2015; Adeniran and El-Ferik, 2017; Persson and Ölvander, 2017; Thimmisetty et al ., 2017; Al-Refaie et al ., 2018; Keles, 2018; Lin and Huang, 2018; Mirabi and Seddighi, 2018; Zimmermann et al ., 2018). In recent years, applications of AI models have become widespread through civil engineering projects (e.g., Armaghani et al ., 2017, 2018; Koopialipoor et al ., 2018).…”

Section: Introductionmentioning

confidence: 99%

Developing a new hybrid soft computing technique in predicting ultimate pile bearing capacity using cone penetration test data

Harandizadeh

2020

AIEDAM

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This research intends to investigate a new hybrid artificial intelligence (AI) technique compared to some common CPT methods in estimating axial ultimate pile bearing capacity (UPBC) using cone penetration test (CPT) data in geotechnical engineering applications. A data series of 108 samples was collected in order to develop a new hybrid structure of an adaptive neuro-fuzzy inference system (ANFIS) network, and the group method of the data handling (GMDH) type neural network was optimized by applying the particle swarm optimization (PSO) algorithm over the hybrid ANFIS-GMDH topology, which leads to a new hybrid AI model called as ANFIS-GMDH-PSO. The derived database provides information related to pile load tests, in situ field CPT data, and soil–pile information for introducing the proposed hybrid neural system. The cross-section of the pile toe, average cone tip resistance along embedded pile length, and sleeve frictional resistance along the shaft had been considered as input parameters for the proposed network. The results of this research indicated that the proposed ANFIS-GMDH-PSO model predicted the UPBC with an acceptable precision compared to various CPT methods, including Schmertmann, De Kuiter & Bringen, and LPC/LPCT methods. Moreover, ANFIS-GMDH-PSO network model performance was compared to CPT-based models in terms of statistical criteria in order to achieve a best fitted model. From the statistical results, it was found that the developed ANFIS-GMDH-PSO model has achieved a higher accuracy level in terms of statistical indices compared to CPT-based empirical methods, such as Schmertmann model, De Kuiter & Beringen model, and Bustamante & Gianeselli for predicting driven pile ultimate bearing capacity.

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A 3D, performance-driven generative design framework: automating the link from a 3D spatial grammar interpreter to structural finite element analysis and stochastic optimization

Cited by 14 publications

References 35 publications

Evolving a Psycho-Physical Distance Metric for Generative Design Exploration of Diverse Shapes

Evolving a Psycho-Physical Distance Metric for Generative Design Exploration of Diverse Shapes

Integrating Geometric Data into Topology Optimization via Neural Style Transfer

Developing a new hybrid soft computing technique in predicting ultimate pile bearing capacity using cone penetration test data

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