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Prismatic Beams: Problem Solving01:15

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In the design of a supported timber beam subjected to a distributed load, both the beam's physical dimensions and the timber's characteristics, such as its grade and species, are critical. These factors determine the allowable stress values, which are crucial for calculating the necessary beam depth to ensure structural integrity and safety.
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AI-assisted optimization design of seismic performance parameters for timber structures.

Dongqi Wei1, Yuqiang Ding1, Feng Zhou1

  • 1School of Architectural Engineering and Art Design, Liuzhou City Vocational College, Liuzhou, Guangxi, China.

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Summary

A new Gradient Boosted Random Forest Machine with Scalable Cheetah Optimizer (GBRF-SCO) framework enhances seismic performance prediction for timber buildings. This AI-driven approach optimizes designs for earthquake resilience and sustainability.

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Area of Science:

  • Structural Engineering
  • Artificial Intelligence
  • Sustainable Architecture

Background:

  • Timber multi-story buildings offer sustainability and seismic resilience advantages.
  • Optimizing seismic parameters like inter-story drift and roof displacement in timber structures using AI remains a challenge.
  • Existing frameworks lack integrated AI solutions for comprehensive seismic performance optimization.

Purpose of the Study:

  • To introduce an AI-based framework, the Gradient Boosted Random Forest Machine with Scalable Cheetah Optimizer (GBRF-SCO), for optimizing seismic parameters in timber buildings.
  • To improve prediction accuracy for seismic response metrics and guide engineers in selecting robust structural designs.
  • To enhance the seismic resilience and sustainability of multi-story timber constructions.

Main Methods:

  • Utilized a dataset of 4,000 timber building samples from the Timber Seismic Performance Dataset.
  • Applied data pre-processing techniques including Robust Scaling and Isolation Forest for normalization and outlier detection.
  • Employed t-Distributed Stochastic Neighbor Embedding (t-SNE) for exploratory data analysis and visualization of feature relationships.
  • Implemented the GBRF model for seismic response prediction and the SCO for hyperparameter optimization.
  • Used Multiple Linear Regression (MLR) to analyze the influence of structural and seismic elements on roof displacement.

Main Results:

  • The GBRF-SCO framework achieved a high prediction accuracy of 0.949 for classifying roof displacement levels (low, medium, high) under seismic conditions.
  • Demonstrated superior performance compared to conventional regression and ensemble methods.
  • Provided insights into key structural and seismic factors influencing timber building seismic performance.
  • Successfully optimized seismic performance characteristics, enabling informed design decisions.

Conclusions:

  • The GBRF-SCO framework offers a robust and intelligent method for optimizing the seismic performance of timber structures.
  • This AI-driven approach significantly improves the design of sustainable and earthquake-resilient multi-story timber buildings.
  • The study highlights the potential of integrated AI frameworks in advancing structural engineering for enhanced safety and environmental benefits.