Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Machine Learning. According to news reporting originating in Mawson Lakes, Australia, b y NewsRx journalists, research stated, "This study investigated the influence of input parameters on the shear strength of RC squat walls using machine learning (ML) models and finite element method (FEM) analysis. The analyses were conduct ed on the largest currently available dataset of 639 squat RC walls with a heigh t-to-length ratio of less than or equal to 2.0." The news reporters obtained a quote from the research from the University of Sou th Australia, "The findings suggest that ensemble learning models, specifically XGBoost, CatBoost, GBRT, and RF, are effective in predicting the shear strength of RC short shear walls and using Bayesian Optimization for hyperparameter tunin g improves their performance. The axial load had a greater influence on the shea r strength than reinforcement ratio, and longitudinal reinforcement had a more s ignificant impact compared to horizontal and vertical reinforcement. The perform ance of XGBoost model significantly outperforms traditional design models such a s ACI 318-19, ASCE/SEI 43-05, and Wood 1990. Additionally, reducing the number o f input features from 13 to 10, 8, or 6 still yields reliable predictions with h igh accuracy. The finding suggests that the use of XGBoost models provides not o nly comparable accuracy to FEM simulations with non-linear pushover analysis but also the first one can predict the lateral strength in the case of incomplete d ata which could not be done by FEM."
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