共查询到19条相似文献,搜索用时 62 毫秒
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混凝土的抗压强度是衡量混凝土质量的重要指标之一,混凝土的抗压强度不仅受实验条件的影响,同时受到外加剂、水泥、水等比例的影响。传统的测定混凝土抗压强度的实验方法耗时长、材料消耗大,且经常得不到准确的结果。文章采用MATLAB软件进行BP神经网络模型训练,用训练好的模型进行混凝土抗压强度的预测工作,神经网络的输入变量为影响混凝土抗压强度的八个因素,混凝土的抗压强度值为输出层结果。对一组混凝土样本进行抗压强度预测,得到的预测值与实测值间的误差均小于3%,预测结果较为精确。 相似文献
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由于影响混凝土抗压强度的因素众多,且抗压强度与各影响因素之间的关系是一种复杂的非线性问题,采用了机器学习的方法较好地对混凝土抗压强度做出预测,研究采用BP和GA-BP两种神经网络分别对混凝土28 d抗压强度进行预测并进行分析,其中输入层的参数为水泥、炉渣、粉煤灰、水、减水剂、粗骨料和细骨料的用量。结果表明:与BP神经网络式相比,GA-BP神经网络预测值与实测值更为吻合,平均误差率减少了43%,有更好的预测能力。同时研究采用灰色关联算法对输入层进行敏感性分析,表明粗骨料用量的改变对28 d混凝土抗压强度的影响最大,并且在输入层删除敏感性较低的参数后,神经网络的预测效果有进一步提高。研究还通过GA-BP神经网络寻最优值对当混凝土强度达到最大值时,输入层各影响因素的数值进行了预测,为混凝土的抗压强度预测和配合比设计提供了分析方法且该神经网络对试验有较好的导向作用。 相似文献
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采用"造壳法"及无振荡分层压制方法制作了多孔混凝土,为满足工程应用和植物生长要求,对多孔混凝土厚度、矿物掺加量及各主要配合比进行了试验优化.结果表明,该法能够制作出植生型多孔混凝土,其中搅拌过程的预拌水量的控制是重要步骤.多孔混凝土的厚度宜控制在10~15 cm之间.矿渣的加入能提高多孔混凝土的性能,其掺合比宜控制在5... 相似文献
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为解决混凝土生产中抗压强度试验周期长及工程管理存在滞后性的问题,提出了一种基于混凝土拌和生产实时监控数据的BP神经网络混凝土抗压强度预测模型。以混凝土拌和生产中的8项物料生产称重数据和5项生产配比数据作为预测输入变量,建立200组混凝土拌和站生产监控数据和对应的抗压强度试验数据样本集,按照6∶2∶2比例划分为训练集、验证集和测试集;分别以C40配比混凝土拌和生产的8项物料称重数据和全部13项数据作为输入变量,进行混凝土28 d抗压强度预测,将预测结果与实际试验结果进行比较,验证所提出BP神经网络模型的预测效果。结果表明:所提出的BP神经网络混凝土强度预测模型能较好地实时预测混凝土28 d抗压强度,且相对误差优于利用7 d抗压强度试验数据估算值;8项物料称重数据作为输入变量的BP神经网络预测模型预测精度更好,平均绝对百分比误差为0.82%,均方根误差为0.52 MPa;利用不同拌和站C20配比、C30配比混凝土拌和生产监控数据对8项输入变量BP神经网络混凝土抗压强度预测模型进行适应性验证可知,其预测平均绝对误差均在0.5 MPa之内,平均绝对百分比误差均小于2%,与C40配比预测误差一致... 相似文献
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基于均匀设计与正交设计相结合的试验方法对植生型多孔混凝土配合比设计进行了分析研究,得出了水胶比、砂率、设计孔隙率和掺合料用量对植生型多孔混凝土性能的影响结果以及最终的配合比方案。试验结果表明:均匀设计与正交设计相结合的试验方法能够在较少试验次数下获得比较充分的数据信息。首先通过均匀试验确定了最佳配合比的范围:水胶比在0.3~0.35之间,设计孔隙率在26%以下,砂率在5%左右,掺合料的掺量为30%~38%。然后在均匀试验的研究基础之上设计正交试验对均匀设计结果验证分析,从而得到了28 d抗压强度为13.4 MPa的优化配合比设计方案。 相似文献
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《Construction and Building Materials》2006,20(9):769-775
High Strength Concrete (HSC) is defined as concrete that meets special combination of performance and uniformity requirements that cannot be achieved routinely using conventional constituents and normal mixing, placing, and curing procedures. HSC is a highly complex material, which makes modelling its behavior very difficult task. This paper aimed to show possible applicability of neural networks (NN) to predict the compressive strength and slump of HSC. A NN model is constructed, trained and tested using the available test data of 187 different concrete mix-designs of HSC gathered from the literature. The data used in NN model are arranged in a format of seven input parameters that cover the water to binder ratio, water content, fine aggregate ratio, fly ash content, air entraining agent, superplasticizer and silica fume replacement. The NN model, which performs in Matlab, predicts the compressive strength and slump values of HSC. The mean absolute percentage error was found to be less then 1,956,208% for compressive strength and 5,782,223% for slump values and R2 values to be about 99.93% for compressive strength and 99.34% for slump values for the test set. The results showed that NNs have strong potential as a feasible tool for predicting compressive strength and slump values. 相似文献
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Kai Li Lei Pan Xiaohui Guo Yuan Feng Wang 《Computer-Aided Civil and Infrastructure Engineering》2024,39(4):559-574
Numerous experimental studies have shown the type and gradation of coarse aggregates effect on the mechanical properties of concrete. The type and gradation of coarse aggregates have not been taken into account in the available machine learning prediction models. In this study, a two-dimensional concrete microscopic image was generated by using a random aggregate model (RAM), and the coarse aggregate and other concrete ingredients were represented innovatively using polygons and trichromatic chromaticity values in the RAM images. The RAM image set was created by applying this method to represent 1110 sets of different concrete mixes. Then based on the Bayesian optimization algorithm and the image set, a compressive strength prediction model considering the effect of coarse aggregate types and gradations was developed utilizing a convolutional neural network (CNN) model. Meanwhile, an artificial neural network (ANN) compressive strength prediction model was developed using 1110 sets of mix ratio data. The results show that the proposed RAM image generation method has the capability to represent different concrete mix ratios collected in this study. The prediction performance of the CNN compressive strength model considering aggregate types and gradations is better than that of the ANN model. The method can provide a new perspective for predicting other concrete mechanical properties and technically support performance-based intelligent concrete mix design. 相似文献
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《Construction and Building Materials》2010,24(5):709-718
No-slump concrete (NSC) is defined as concrete having either very low or zero slump that traditionally used for prefabrication purposes. The sensitivity of NSC to its constituents, mixture proportion, compaction, etc., enforce some difficulties in the prediction of the compressive strength. In this paper, by considering concrete constituents as input variables, several regression, neural networks (NNT) and ANFIS models are constructed, trained and tested to predict the 28-days compressive strength of no-slump concrete (28-CSNSC). Comparing the results indicate that NNT and ANFIS models are more feasible in predicting the 28-CSNSC than the proposed traditional regression models. 相似文献
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如何在进行自密实混凝土配合比设计前对其工作性能和强度进行有效预测,为配合比设计提供指导,是一大难点。本文利用BP神经网络,对自密实混凝土的工作性能(坍落度和扩展度)和28d强度进行预测。结果表明,利用大量试验数据样本训练的BP网络可以预测不同情况下的自密实混凝土的坍落度、扩展度和28d强度,预测精度高。 相似文献
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Mustafa Sarıdemir İlker Bekir Topçu Fatih Özcan Metin Hakan Severcan 《Construction and Building Materials》2009,23(3):1279-1286
In this study, artificial neural networks and fuzzy logic models for prediction of long-term effects of ground granulated blast furnace slag on compressive strength of concrete under wet curing conditions have been developed. For purpose of constructing these models, 44 different mixes with 284 experimental data were gathered from the literature. The data used in the artificial neural networks and fuzzy logic models are arranged in a format of five input parameters that cover the age of specimen, Portland cement, ground granulated blast furnace slag, water and aggregate, and output parameter which is 3, 7, 14, 28, 63, 90, 119, 180 and 365-day compressive strength. In the models of the training and testing results have shown that artificial neural networks and fuzzy logic systems have strong potential for prediction of long-term effects of ground granulated blast furnace slag on compressive strength of concrete. 相似文献