仅用两种无损检测指标预测花岗岩无侧限抗压强度

IF 2.5 3区 工程技术 Q2 ENGINEERING, CIVIL Geomechanics and Engineering Pub Date : 2021-01-01 DOI:10.12989/GAE.2021.25.4.317
D. J. Armaghani, A. Mamou, Chrysanthos Maraveas, P. Roussis, Vassilis G. Siorikis, A. Skentou, P. G. Asteris
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引用次数: 32

摘要

本文报道了利用人工神经网络对花岗岩无侧限抗压强度进行预测的先进数据分析结果。编制了一个与数据无关的、与现场无关的无偏数据库,其中包括文献中报告的182个无损试验数据集,并用于训练和开发人工神经网络,以预测花岗岩的无侧限抗压强度。结果表明:本研究构建的最优人工网络对弱至强花岗岩(20.3 ~ 198.15 mpa)无侧限抗压强度的预测结果与70%试件的实验数据偏差小于±20%,显著优于文献中已有的许多模型。研究结果还提出了一些有趣的问题,即皮尔逊相关系数在评估模型预测精度方面的适用性。
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Predicting the unconfined compressive strength of granite using only two non-destructive test indexes
This paper reports the results of advanced data analysis involving artificial neural networks for the prediction of the unconfined compressive strength of granite using only two non-destructive test indexes. A data-independent site-independent unbiased database comprising 182 datasets from non-destructive tests reported in the literature was compiled and used to train and develop artificial neural networks for the prediction of the unconfined compressive strength of granite. The results show that the optimum artificial network developed in this research predicts the unconfined compressive strength of weak to very strong granites (20.3-198.15MPa) with less than ±20% deviation from the experimental data for 70% of the specimen and significantly outperforms a number of available models available in the literature. The results also raise interesting questions with regards to the suitability of the Pearson correlation coefficient in assessing the prediction accuracy of models.
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来源期刊
Geomechanics and Engineering
Geomechanics and Engineering ENGINEERING, CIVIL-ENGINEERING, GEOLOGICAL
CiteScore
5.20
自引率
25.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: The Geomechanics and Engineering aims at opening an easy access to the valuable source of information and providing an excellent publication channel for the global community of researchers in the geomechanics and its applications. Typical subjects covered by the journal include: - Analytical, computational, and experimental multiscale and interaction mechanics- Computational and Theoretical Geomechnics- Foundations- Tunneling- Earth Structures- Site Characterization- Soil-Structure Interactions
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