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Neural network-aided prediction of post-cracking tensile strength of fibre-reinforced concrete

機(jī)譯：纖維增強(qiáng)混凝土后開裂拉伸強(qiáng)度的神經(jīng)網(wǎng)絡(luò)輔助預(yù)測(cè)

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摘要

Structural fibres are an effective method to improve concrete post-cracking tensile strength (f(ctR)). Currently, the characterization of this property is mainly performed experimentally. This is a source of uncertainties at design stages, which hinders the development of new fibre type and/or optimization of those currently existing. This paper presents a multilayer perceptron neural network to predict f(ctR) of fibre-reinforced concrete (FRC) subjected to the Barcelona Test. The optimal architecture of the predictor is obtained by evaluating 9216 configurations of input dimension and number of hidden layers and neurons. The generalization performance is assessed using repeated random sub-sampling validation with 50 iterations. The final model can predict with high accuracy the f(ctR) of FRC for different cracking stages. A parametric analysis is performed to prove coherence between the results predicted by the model and the established understanding of the FRC behaviour. Finally, numerical expressions are provided as an alternative tool to traditional testing to predict the residual strength of the Barcelona Test for pre-design and quality control purposes based on fibre dosage, concrete strength, specimen type and height and fibre geometric characteristics. These type of approaches are found to be necessary for boosting the development of the FRC technology. (C) 2021 The Author(s). Published by Elsevier Ltd.
機(jī)譯：結(jié)構(gòu)纖維是改善混凝土后裂解拉伸強(qiáng)度（F（CTR））的有效方法。目前，該屬性的特征主要是通過(guò)實(shí)驗(yàn)進(jìn)行的。這是設(shè)計(jì)階段的不確定因素來(lái)源，其阻礙了新的光纖類型和/或優(yōu)化目前存在的開發(fā)。本文介紹了一個(gè)多層的感知神經(jīng)網(wǎng)絡(luò)，以預(yù)測(cè)經(jīng)過(guò)巴塞羅那試驗(yàn)的纖維增強(qiáng)混凝土（FRC）的F（CTR）。通過(guò)評(píng)估輸入維度和隱藏層數(shù)和神經(jīng)元數(shù)的9216配置來(lái)獲得預(yù)測(cè)器的最佳架構(gòu)。使用具有50個(gè)迭代的重復(fù)隨機(jī)子采樣驗(yàn)證來(lái)評(píng)估泛化性能。最終模型可以高精度地預(yù)測(cè)FRC的F（CTR），用于不同的裂化階段。執(zhí)行參數(shù)分析以證明模型預(yù)測(cè)的結(jié)果與對(duì)FRC行為的既定理解之間的相干性。最后，提供了數(shù)值表達(dá)式作為傳統(tǒng)測(cè)試的替代工具，以預(yù)測(cè)基于纖維劑量，混凝土強(qiáng)度，樣品類型和高度和纖維幾何特性的預(yù)設(shè)計(jì)和質(zhì)量控制目的的巴塞羅那試驗(yàn)的剩余強(qiáng)度。發(fā)現(xiàn)這些類型的方法是推動(dòng)FRC技術(shù)的發(fā)展所必需的。 （c）2021提交人。 elsevier有限公司出版

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  • 來(lái)源
    《Computers & Structures》 |2021年第11期|106640.1-106640.16|共16頁(yè)
  • 作者單位

    Smart Engn Ltd C Jordi Girona 1-3 Parc UPC K2M Barcelona 08034 Spain|Tech Univ Catalonia BarcelonaTech Sch Ind Engn Barcelona ETSEIB Dept Project & Construct Engn Av Diagonal 647 Barcelona 08028 Spain;

    Univ Politecn Cataluna Dept Civil & Environm Engn UPC Barcelona Tech Jordi Girona 1-3 Barcelona 08034 Spain;

    Tech Univ Catalonia BarcelonaTech Sch Ind Engn Barcelona ETSEIB Dept Project & Construct Engn Av Diagonal 647 Barcelona 08028 Spain;

    Univ Politecn Cataluna Dept Civil & Environm Engn UPC Barcelona Tech Jordi Girona 1-3 Barcelona 08034 Spain;

  • 收錄信息 美國(guó)《科學(xué)引文索引》(SCI);美國(guó)《工程索引》(EI);
  • 原文格式 PDF
  • 正文語(yǔ)種 eng
  • 中圖分類
  • 關(guān)鍵詞

    Artificial neural network; Fibre-reinforced concrete; Residual strength; Tensile strength;

    機(jī)譯：人工神經(jīng)網(wǎng)絡(luò);纖維鋼筋混凝土;剩余強(qiáng)度;拉伸強(qiáng)度;

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