Optimizing flexural strength of RC beams with recycled aggregates and CFRP using machine learning models.

IF 3.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Thanh-Hung Nguyen, Hoang-Thach Vuong, Jim Shiau, Trung Nguyen-Thoi, Dinh-Hung Nguyen, Tan Nguyen
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引用次数: 0

Abstract

This paper investigates the flexural bearing behavior of reinforced concrete beams through experimental analysis and advanced machine learning predictive models. The primary problem centers around understanding how varying compositions of construction materials, particularly the inclusion of recycled aggregates and carbon fiber-reinforced polymer (CFRP), affect the structural performance of concrete beams. Eight beams, including those with natural aggregates, recycled aggregates, fly ash, and CFRP, were tested. The study employs state-of-the-art machine learning frameworks, including Random Forest Regressor (RFR), XGBoost (XGB), and LightGBM (LGBM). The formation of these models involved data acquisition from experiments, preprocessing of key input features (such as rebars area, cement portion, recycled and natural aggregate masses, silica fume, fly ash, compressive strength, and CFRP presence), model selection, and hyperparameter tuning using Pareto optimization. The models were then evaluated using performance metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2). Outputs focus on load-induced deflection and mid-span displacement. With a dataset of 4851 samples, the optimized models demonstrated excellent performance. The experimental results revealed substantial enhancements in both compressive strength and load-bearing capacity, notably observed in beams incorporating 70% recycled aggregate and 10% silica fume. These beams exhibited a remarkable increase in compressive strength of up to 53.03% and a 7% boost in load-bearing capacity compared to those without recycled aggregate. By integrating experimental analysis with advanced computational techniques, this study advances the understanding of eco-friendly construction materials and their performance, shedding light on the intricate interactions between sustainable construction materials and the flexural bearing behavior of beams.

利用机器学习模型优化使用再生骨料和 CFRP 的 RC 梁的抗弯强度。
本文通过实验分析和先进的机器学习预测模型研究钢筋混凝土梁的抗弯承载行为。主要问题集中在了解不同的建筑材料成分,尤其是再生骨料和碳纤维增强聚合物(CFRP)的加入如何影响混凝土梁的结构性能。对八种梁进行了测试,包括天然骨料、再生骨料、粉煤灰和碳纤维增强聚合物。研究采用了最先进的机器学习框架,包括随机森林回归器(RFR)、XGBoost(XGB)和 LightGBM(LGBM)。这些模型的形成包括从实验中获取数据、预处理关键输入特征(如钢筋面积、水泥比例、再生骨料和天然骨料质量、硅灰、粉煤灰、抗压强度和 CFRP 存在)、模型选择以及使用帕累托优化法进行超参数调整。然后使用平均平方误差 (MSE)、平均绝对误差 (MAE) 和判定系数 (R2) 等性能指标对模型进行评估。输出主要集中在荷载引起的挠度和跨中位移。在 4851 个样本的数据集上,优化模型表现出了卓越的性能。实验结果表明,抗压强度和承载能力都有显著提高,尤其是在含有 70% 再生骨料和 10% 硅灰的梁中。与不含再生骨料的梁相比,这些梁的抗压强度显著提高了 53.03%,承载能力提高了 7%。通过将实验分析与先进的计算技术相结合,本研究加深了人们对环保建筑材料及其性能的理解,揭示了可持续建筑材料与梁的抗弯承载行为之间错综复杂的相互作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Scientific Reports
Scientific Reports Natural Science Disciplines-
CiteScore
7.50
自引率
4.30%
发文量
19567
审稿时长
3.9 months
期刊介绍: We publish original research from all areas of the natural sciences, psychology, medicine and engineering. You can learn more about what we publish by browsing our specific scientific subject areas below or explore Scientific Reports by browsing all articles and collections. Scientific Reports has a 2-year impact factor: 4.380 (2021), and is the 6th most-cited journal in the world, with more than 540,000 citations in 2020 (Clarivate Analytics, 2021). •Engineering Engineering covers all aspects of engineering, technology, and applied science. It plays a crucial role in the development of technologies to address some of the world''s biggest challenges, helping to save lives and improve the way we live. •Physical sciences Physical sciences are those academic disciplines that aim to uncover the underlying laws of nature — often written in the language of mathematics. It is a collective term for areas of study including astronomy, chemistry, materials science and physics. •Earth and environmental sciences Earth and environmental sciences cover all aspects of Earth and planetary science and broadly encompass solid Earth processes, surface and atmospheric dynamics, Earth system history, climate and climate change, marine and freshwater systems, and ecology. It also considers the interactions between humans and these systems. •Biological sciences Biological sciences encompass all the divisions of natural sciences examining various aspects of vital processes. The concept includes anatomy, physiology, cell biology, biochemistry and biophysics, and covers all organisms from microorganisms, animals to plants. •Health sciences The health sciences study health, disease and healthcare. This field of study aims to develop knowledge, interventions and technology for use in healthcare to improve the treatment of patients.
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