Selection of the optimal diagrid patterns in tall buildings within a multi-response framework: Application of the desirability function

Scaramozzino, Domenico; Albitos, Bonierose; Lacidogna, Giuseppe; Carpinteri, Alberto

doi:10.1016/j.jobe.2022.104645

Cited by 7 publications

(4 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The response "max utilization (compression)", Figure 10e, depends instead on the diagrid angle, both at the top and at the bottom, and again but less importantly from the total façade area and the number of bottom sides. It seems that, for this response which involves the local output for the structural elements in the diagrid, the relative angle between the elements in the diagrid matters the most, as evidenced in other studies [9,10].…”

Section: Regression Resultsmentioning

confidence: 58%

“…The spotlight of this work is on tall structures, namely those incorporating outer diagrids [9,10]: the concept of transferring lateral load-bearing capabilities to façade elements (diagrids) not only minimizes the need for interior columns but also aligns with the necessity of ensuring structural efficiency. In other words, this design approach elegantly merges aesthetic design with structural efficacy.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence-Powered Computational Strategies in Selecting and Augmenting Data for Early Design of Tall Buildings with Outer Diagrids

Kazemi,

Ghisi,

Entezami

2024

Buildings

View full text Add to dashboard Cite

In the realm of architectural computing, this study explores the integration of parametric design with machine learning algorithms to advance the early design phase of tall buildings with outer diagrid systems. The success of such an endeavor relies heavily on a data-driven and artificial intelligence-enhanced workflow aimed at identifying key architectural and structural variables through a feature/response selection process within a supervised machine learning framework. By augmenting an initial dataset, which was notably limited, through four distinct techniques—namely Gaussian copula, conditional generative adversarial networks, Gaussian copula generative adversarial network, and variational autoencoder—this study demonstrates a methodical approach to data enhancement in architectural design. The results indicate a slight preference for the Gaussian copula method, attributed to its less complex hyperparameter tuning process. Evaluation through a random forest regressor revealed stable performance across various cross-validation techniques on synthetic data, although with an acceptable decrease in the coefficient of determination, from an original average score of 0.925 to an augmented score of 0.764. This investigation underscores the potential of artificial intelligence-powered computational tools to guide design decisions by pinpointing the variables with the most significant impact on relevant outputs, quantitatively assessing their influence through the accuracy of the employed machine learning methods.

show abstract

Section: Regression Resultsmentioning

confidence: 58%

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Powered Computational Strategies in Selecting and Augmenting Data for Early Design of Tall Buildings with Outer Diagrids

Kazemi,

Ghisi,

Entezami

2024

Buildings

View full text Add to dashboard Cite

show abstract

“…At present, the research on diagrid tubes mainly focuses on the theoretical calculation of the lateral stiffness [4,5], plastic development research, diagrid angle and plane form optimization, diagrid node research, and so on [6][7][8]. Moon [9] investigated the optimal configurations of today's prevalent structural systems for tall buildings to generate higher lateral stiffness and maximize their structural efficiency.…”

Section: Introductionmentioning

confidence: 99%

Sustainable Seismic Performance of Diagrid Core-Tube Structure with Replaceable Steel Coupling Beam

Liu,

Li,

et al. 2024

Sustainability

View full text Add to dashboard Cite

The diagrid core-tube structure has been widely used in high-rise buildings in recent years, but there are few studies on the sustainable energy dissipation measures and seismic performance improvement of such structural systems. Because the coupling beam is the element connecting the inner tube and the outer tube in the diagrid structure, it is the first seismic defense line and an important energy-dissipation member in the seismic design of the overall structure. Therefore, this paper replaces the traditional reinforced concrete coupling beam of the inner tube of the shear wall with a replaceable energy-dissipation steel coupling beam, and the strength, stiffness, and stability of the replaceable steel coupling beam are designed to improve the sustainability of the structure. By changing the position of the replaceable coupling beam, the relative stiffness of the inner and outer tubes of the diagrid tube structure, and the plane form of the structure, the static elastoplastic analysis and seismic response energy analysis of different diagrid tube structures are carried out, and the influence of the replaceable coupling beam on the sustainable seismic performance of the diagrid tube structure is studied. The results show that the replaceable coupling beams have little effect on the ultimate bearing capacity of the structure, but the ductility and sustainability of the structure are significantly improved, and the whole building layout is the optimal layout scheme. The setting of replaceable coupling beams makes the diagrid tube structure show hysteretic energy-dissipation earlier under the action of large earthquakes, and the proportion of hysteretic energy-dissipation is greatly improved, which reduces the inter-story drift ratios and the damage degree of the diagrid columns under the action of large earthquakes. When the relative stiffness of the outer tube of the diagrid tube structure is small or the plane form of the structure is a regular quadrilateral, the application of replaceable coupling beams is more effective in improving the ductility and sustainability of the structure and reducing the damage to the diagrid column under large earthquakes.

show abstract

“…Among these, the patterns in which the diagrid triangular module spans over twothree floors, corresponding to diagonal inclinations of about 55°-65°, have the highest desirability. Notably, because of the competition between the different responses, this optimal inclination is not found to increase as the building becomes taller [13].…”

mentioning

confidence: 96%

Diagrid Based Multistorey Building Design and Durability Forecasting

Soni¹,

Rajeev²

2022

IJRASET

View full text Add to dashboard Cite

The taller and higher structures results in the effects of lateral loading, lateral loads dominates when the height of building increases, lateral loads such as earthquake, wind causes the building larger displacement, to avoid larger displacement in high rise buildings lateral load resisting systems are deployed. Apart from other structural system, the diagrid and hexagrid system are adopted to improve structural performance of tall buildings. The various types of structural system in tall buildings have become obsolete and the new structural skeletons such as hexagrid and diagrid are being used

show abstract

Selection of the optimal diagrid patterns in tall buildings within a multi-response framework: Application of the desirability function

Cited by 7 publications

References 37 publications

Artificial Intelligence-Powered Computational Strategies in Selecting and Augmenting Data for Early Design of Tall Buildings with Outer Diagrids

Artificial Intelligence-Powered Computational Strategies in Selecting and Augmenting Data for Early Design of Tall Buildings with Outer Diagrids

Sustainable Seismic Performance of Diagrid Core-Tube Structure with Replaceable Steel Coupling Beam

Diagrid Based Multistorey Building Design and Durability Forecasting

Contact Info

Product

Resources

About