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为实现对粗骨料UHPC的抗压强度的预测和配合比设计方法的优化,搜集了国内外文献中168组粗骨料UHPC配合比和标准养护28 d抗压强度实测值,给出了各材料组分和抗压强度频数分布,并基于灰色关联分析法分析了各材料组分与抗压强度的关联关系,通过神经网络参数分析,建立了基于遗传算法的前馈神经网络,相比普通的BP神经网络具有更好的预测精度和泛化能力。最后基于建立的GA-BP神经网络给出了不同强度等级粗骨料UHPC配合比设计中粗骨料/胶凝材料、钢纤维体积掺量、砂胶比的建议取值范围。 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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分析了影响植生型多孔混凝土抗压强度的主要因素,选取目标孔隙率、水胶比、胶凝材料用量、粗骨料用量、水用量、粗骨料平均粒径、粗骨料比表面积、粗骨料堆积孔隙率及浆骨比作为植生型多孔混凝土抗压强度的影响指标,分别建立了BP多层前馈神经网络预测模型和采用遗传算法优化的BP神经网络预测模型(GA-BP).收集国内外文献中146组植生型多孔混凝土试验数据,以其中116组数据作为训练样本,并采用其余30组数据作为试验样本与BP、GA-BP神经网络模型预测值、线性回归方程抗压强度计算值进行比较分析,结果表明:BP、GA-BP神经网络模型计算精度与离散性更优,且较线性回归方程计算结果更接近于样本试验值,更能够准确地预测多孔混凝土的抗压强度值. 相似文献
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以水泥、粉煤灰和硅灰为原材料,利用修正的Andreasen-Andersen(MAA)模型指导超高性能混凝土(UHPC)配合比设计,研究了配合比、水胶比和养护方式对UHPC工作性能、抗压强度、表观密度和水化产物特性的影响,以残差平方和(RSS)作为堆积密实度指标,分析了UHPC抗压强度和水化产物特性.结果表明:硅灰对提升UHPC的堆积密实度有利;当UHPC的残差平方和达到最小值570.64时,标准养护28 d和蒸气养护3 d条件下的UHPC抗压强度分别可达到最大值140.4、153.9 MPa,说明基于MAA模型设计的UHPC配合比合理;通过研究UHPC水化产物特性,发现UHPC中水泥水化反应不完全,高水胶比和高水泥掺量可促进水化反应,粉煤灰与硅灰在碱性环境中反应会消耗氢氧化钙,形成水化硅酸钙(C-S-H)凝胶,降低了体系的钙硅摩尔比,改善了UHPC的显微结构,提升了UHPC的致密性与强度. 相似文献
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《工业建筑》2020,(3)
目前超高性能混凝土(UHPC)的配合比研究多集中在不含粗骨料的活性粉末混凝土(RPC)上,而在RPC中掺入粗骨料可以降低成本并且减少混凝土的收缩,但是关于含粗骨料的超高性能混凝土(CA-UHPC)的配合比的研究相对较少。以原材料、生产成本和生产工艺三方面为影响因素,对UHPC发展应用的影响进行探讨,并依此给出了较为经济合理的UHPC配合比设计,考虑施工现场环境条件并简化了养护工艺,制作了38组(共计114个) UHPC立方体试块,研究了水胶比、硅灰掺量、钢纤维掺量、粗骨料掺量以及养护条件等对UHPC抗压强度的影响规律并进行分析。基于试验结果给出了最优的钢纤维掺量及粗骨料掺量。 相似文献
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基于BP神经网络,建立了UHPC力学性能的预测模型,并利用相关试验数据验证模型的有效性.采用该模型系统研究了水胶比和聚羧酸减水剂掺量对于UHPC力学性能的影响,结果表明:在0.136~0.225的范围内,水胶比对UHPC力学性能的影响不显著,而且减水剂掺量越小,它的影响越不显著;聚羧酸减水剂掺量对UHPC力学性能的影响极为显著,掺量在0.7%左右的UHPC的力学性能最优.进一步通过试验验证了水胶比和聚羧酸减水剂掺量对UHPC力学性能的影响规律.对于今后UHPC的试配,在试验取得一定数据样本之后,能够通过该模型试验代替部分试配试验来减少试验工作量,同时为UHPC配合比设计和优化提供指导. 相似文献
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收集了502组混凝土配方作为训练数据,基于BP神经网络、遗传算法及粒子群算法,构建了一种混凝土配方设计模型,可用于控制混凝土成本和配方优化.所构建的模型考虑了混凝土的原材料成本以及影响混凝土抗压强度的多个关键因素,引入惩罚函数对粒子群算法的目标函数适应度值进行惩罚,解决了混凝土配方设计中非线性约束离散变量问题和连续变量问题,从而达到控制混凝土成本并优化配方的目标.按照构建模型输出27组降低成本后的混凝土配方,并进行抗压强度试验,结果表明:所得配方成本与目标成本的契合度接近97%;降低混凝土单方成本5、10、15元后,所输出的配方均能满足混凝土立方体抗压强度要求. 相似文献
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A Fuzzy Adaptive Resonance Theory‐Based Model for Mix Proportion Estimation of High‐Performance Concrete 下载免费PDF全文
Fei Ha Chiew Chee Khoon Ng Kok Chin Chai Kai Meng Tay 《Computer-Aided Civil and Infrastructure Engineering》2017,32(9):772-786
A new approach that adopts the use of fuzzy adaptive resonance theory (ART) neural network in estimating high‐performance concrete (HPC) mix proportion from experimental data is devised. The proposed model receives a set of desired concrete performances, searches for a set of mix proportions that is near to the desired concrete performances, classifies the mix proportions into clusters, measures the similarity between performances of deduced clusters with desired performances, and deduces a mix proportion. The proposed model was used to estimate the mix proportions of five batches of concrete based on the performance criteria of 7th and 28th day compressive strengths. The generated mix proportions were used in an experimental work and the errors were within 13% for 7th compressive strength; and 7% for the 28th day compressive strength, signifying the reliability of the fuzzy ART‐based model in estimating the mix proportion of HPC. This article contributes to an alternative method of mix proportion estimation of HPC by avoiding the use of complicated function approximation techniques. 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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应用MATLAB提供的神经网络工具箱作为BP神经网络训练和仿真的平台,并进行语言编程,通过采用不同隐函数节点数进行对比试验,采用精度与误差都合适的节点数进行训练与预测,观察预测的精度,并分析神经网络对抗压强度结果预测的可应用性,从而得出一些有益的结论。 相似文献
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针对建筑保温材料性能表征十分复杂、困难的情况,利用人工神经网络BP算法,建立了复合保温材料性能预测模型,模型由3层神经元组成,分别为输入层、隐含层和输出层。以炉渣复合材料性能与成分的关系为研究对象,采取108组实验数据对神经网络进行8 000次训练,神经网络输出值的平方平均误差为0.000 12。然后,选用18组实验数据对训练成熟的试验神经网络模型进行检测,并把检测样本的神经网络输出值和试验值进行比较。结果表明:所建立的网络能反映炉渣复合保温材料与材料性能之间的关系,为实验设计提供了新的思想,节省了时间和劳动力。 相似文献
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Faezehossadat Khademi Mahmoud Akbari Sayed Mohammadmehdi Jamal Mehdi Nikoo 《Frontiers of Structural and Civil Engineering》2017,11(1):90-99
Evaluating the in situ concrete compressive strength by means of cores cut from hardened concrete is acknowledged as the most ordinary method, however, it is very difficult to predict the compressive strength of concrete since it is affected by many factors such as different mix designs, methods of mixing, curing conditions, compaction, etc. In this paper, considering the experimental results, three different models of multiple linear regression model (MLR), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) are established, trained, and tested within the Matlab programming environment for predicting the 28 days compressive strength of concrete with 173 different mix designs. Finally, these three models are compared with each other and resulted in the fact that ANN and ANFIS models enables us to reliably evaluate the compressive strength of concrete with different mix designs, however, multiple linear regression model is not feasible enough in this area because of nonlinear relationship between the concrete mix parameters. Finally, the sensitivity analysis (SA) for two different sets of parameters on the concrete compressive strength prediction are carried out. 相似文献
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Mix proportions for HPC incorporating multi-cementitious composites using artificial neural networks 总被引:1,自引:0,他引:1
Mohammad Iqbal Khan 《Construction and Building Materials》2012,28(1):14-20
High performance concrete (HPC) is defined in terms of both strength and durability performance under anticipated environmental conditions. HPC can be manufactured involving up to 10 different ingredients whilst having to consider durability properties in addition to strength. The number of ingredients and the number of properties of HPC, which needs to be considered in its design, are more than those for ordinary concrete. Therefore, it is difficult to predict the mix proportions and other properties of this type of concrete using statistical empirical relationship. An alternative approach is to use an artificial neural network (ANN). Based on the experimentally obtained results, ANN has been used to establish its applicability to the prediction and optimization of mix proportioning for HPC. It was demonstrated that mix proportioning for HPC can be predicted using ANN. However, some trial mixes are necessary for better performance and elimination of material variability factors from place to place. ANN procedure provides guidelines to select appropriate material proportions for required strength and rheology of concrete mixes and will reduce the number of trial mixes. 相似文献
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超高性能混凝土(UHPC)是一种力学性能超高、耐久性能优异、体积稳定性优良的新型水泥基复合材料,本文介绍了这种新型复合材料基本制备原理,介绍采用水泥、石英砂、矿物掺合料等常用建筑原材料配制出超高性能的混凝土,并通过对比试验,研究了矿物掺和料种类、纤维掺量以及养护工艺对超高性能混凝土抗压、抗折强度的影响,确定了最佳配合比。实验结果表明:此超高性能混凝土(UHPC)流动性好,在高温环境养护下,试件抗压强度达到325MPa,抗折强度达54MPa;在自然条件下养护,试件30天抗压强度为187MPa,抗折强度为35MPa。本文继而探索该种超高性能混凝土在预应力结构工程方面的应用,将其替代钢制锚垫板和其它产品,采用其制备出的预应力构件,各项性能指标均满足技术要求,并且成本显著降低,为超高性能混凝土在预应力结构工程方面的推广应用奠定基础。 相似文献
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通过对比浇筑成型制备工艺、直接喷涂成型制备工艺和预混喷涂成型制备工艺成型的UHPC力学性能发现,直接喷涂成型制备工艺抗弯强度、抗压强度、抗拉强度下降明显,不满足UHPC性能要求,而预混喷涂成型制备工艺抗弯强度、抗拉强度、抗压强度虽有所降低,但满足UHPC性能要求。通过分析各10组直接喷涂成型制备工艺和预混喷涂成型制备工艺成型UHPC力学性能发现,预混喷涂成型制备工艺力学性能稳定,可重复性较好,可用于UHPC产品预制成型。通过分析3种不同工艺成型UHPC试块断面图像发现,制备工艺不同程度地影响UHPC的孔隙率和纤维分布。 相似文献