Earthquake-Resistant Architectural Design Using a Hybrid Neural Network Approach for Optimizing ADAS Dampers in Steel Buildings

Authors

https://doi.org/10.48314/adb.v2i4.43

Abstract

Earthquake-resistant architectural design is one of the key approaches to improving the safety and sustainability of high-rise buildings. The use of modern technologies such as artificial intelligence and energy dissipation devices enables the simultaneous enhancement of structural performance and the quality of architectural design. In this study, a hybrid method based on Artificial Neural Networks (ANNs) is proposed to optimize the performance of Added Damping And Stiffness (ADAS) dampers in steel buildings. To evaluate the effectiveness of this approach, a 15-story steel structure with a braced system was modeled in four different retrofitting configurations and analyzed using nonlinear  Incremental Dynamic Analysis (IDA) with ten earthquake acceleration records. The initial design was conducted using ETABS, while the analysis and optimization processes were carried out with OpenSees and MATLAB. The results indicate that the application of ADAS dampers increases stiffness, reduces inter-story drifts, and improves the overall seismic behavior of the building. Ultimately, the study demonstrates that integrating intelligent methods into architectural and structural design can provide an effective pathway toward developing earthquake-resistant and resilient architecture.

Keywords:

Earthquake-resistant architecture, Added damping and stiffness damper, Artificial neural network, Seismic optimization, Steel building, Intelligent architectural design

References

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Published

2025-06-08

Issue

Vol. 2 No. 4 (2025): Architectural Dimensions and Beyond

Section

Articles

How to Cite

Rahimi, S. ., & Fazeli, A. . (2025). Earthquake-Resistant Architectural Design Using a Hybrid Neural Network Approach for Optimizing ADAS Dampers in Steel Buildings. Architectural Dimensions and Beyond, 2(4), 257-276. https://doi.org/10.48314/adb.v2i4.43

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