摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-一项关于机器学习的新研究现在可以获得。根据来自越南岘港的新闻，NewsRx Correspondents的研究表明：“钢筋混凝土(RC)平板，由于其柔性，在建筑中很受欢迎，容易受到突然和脆性的冲切破坏。现有的设计方法往往表现出明显的BIA和变异性。”这项研究的财政支持来自首尔国立科技大学(SeoulTech)-首尔国立科技大学(SeoulTech)。新闻记者引用了东甲大学的一篇研究文章:“准确估算钢筋混凝土平板冲切强度对于有效的混凝土结构设计和管理至关重要。本文介绍了一种新的计算方法——水母-最小二乘支持向量机(JSLSV R)混合模型,将机器学习(LSSVR)与水母群(JS)智能相结合,预测钢筋混凝土平板冲切强度。”该混合模型保证了预测的准确性和可靠性。该模型的开发利用了真实世界的经验数据集。与人工蜂群(ABC)、差分进化(DE)、遗传算法(GA)等7个已建立的优化器以及现有的基于机器学习(ML)的模型和设计代码进行了比较,验证了JS-LSVR混合模型的优越性。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Machine Learning is now available. According to news originating from Da Nang, Vietnam, by NewsRx corre spondents, research stated, "Reinforced concrete (RC) flat slabs, a popular choi ce in construction due to their flexibility, are susceptible to sudden and britt le punching shear failure. Existing design methods often exhibit significant bia s and variability." Financial support for this research came from Seoul National University of Scien ce and Technology (SeoulTech) - Seoul National University of Science and Technol ogy (SeoulTech). Our news journalists obtained a quote from the research from Dong-A University, "Accurate estimation of punching shear strength in RC flat slabs is crucial for effective concrete structure design and management. This study introduces a nove l computation method, the jellyfish-least square support vector machine (JSLSSV R) hybrid model, to predict punching shear strength. By combining machine learni ng (LSSVR) with jellyfish swarm (JS) intelligence, this hybrid model ensures pre cise and reliable predictions. The model's development utilizes a real-world exp erimental data set. Comparison with seven established optimizers, including arti ficial bee colony (ABC), differential evolution (DE), genetic algorithm (GA), an d others, as well as existing machine learning (ML)-based models and design code s, validates the superiority of the JS-LSSVR hybrid model."
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