基于BP网络的碳纤维混凝土强度研究

介绍了应用均匀设计理论设计碳纤维混凝土配方的方法,通过所得到的试验数据,运用人工神经网络(ANN)的方法预测碳纤维混凝土抗压强度和劈裂抗拉强度;阐述了采用BP算法建立碳纤维混凝土抗压强度神经网络模型的过程,仿真结果表明,BP网络可成功地建立非线性的强度模型,预测强度可达到较高精度。     

基于BP神经网络混凝土抗压强度预测

在阐述BP人工神经网络原理的基础上,针对影响强度的主要因素,建立了多因子混凝土抗压强度3层BP网络模型,以每立方混凝土中水泥、高炉矿渣粉、粉煤灰、水、减水剂、粗集料和细集料含量及置放天数作为模型输入参数,混凝土抗压强度值作为模型的输出,对混凝土抗压强度进行了预测.实验结果表明:所建BP神经网络混凝土抗压强度预测模型最大...     

基于神经网络的混凝土强度预测

在传统预测混凝土强度的基础上,提出一种基于人工智能的新的预测方法,建立了两种神经网络模型:BP神经网络和RBF神经网络,实现了从新拌混凝土成分及其特性到硬化后混凝土强度之间的复杂的非线性映射。通过对试验数据的学习,网络结构可以早期预测混凝土28d抗压强度。另外,还利用BP神经网络模拟分析了混凝土成分质和量的变化对抗压强度的影响,其结果符合已知的经典混凝土强度变化规律,表明神经网络模型具有较高的精度和较强的泛化能力。     

基于神经网络的温凝土强度预测

在传统预测混凝土强度的基础上,提出一种基于人工智能的新的预测方法,建立了两种神经网络模型:BP神经网络和RBF神经网络,实现了从新拌混凝土成分及其特性到硬化后混凝土强度之间的复杂的非线性映射.通过对试验数据的学习,网络结构可以早期预测混凝土28d抗压强度.另外,还利用BP神经网络模拟分析了混凝土成分质和量的变化对抗压强度的影响,其结果符合已知的经典混凝土强度变化规律,表明神经网络模型具有较高的精度和较强的泛化能力.     

基于神经网络的混凝土强度预测

在传统预测混凝土强度的基础上,提出一种基于人工智能的新的预测方法,建立了两种神经网络模型：BP神经网络和RBF神经网络,实现了从新拌混凝土成分及其特性到硬化后混凝土强度之间的复杂的非线性映射。通过对试验数据的学习,网络结构可以早期预测混凝土28d抗压强度。另外,还利用BP神经网络模拟分析了混凝土成分质和量的变化对抗压强度的影响,其结果符合已知的经典混凝土强度变化规律,表明神经网络模型具有较高的精度和较强的泛化能力。     

高强混凝土强度预测的支持向量机模型及应用

高强混凝土的强度受多种因素的影响,其强度的预测是一个动态性的系统工程。采用支持向量机理论,建立了高强混凝土的强度预测的支持向量机预测模型。并将该模型计算结果与实测混凝土28 d抗压强度、BP网络计算的强度、RBF径向基函数神经网络计算的强度、线性回归模型计算的强度、非线性回归模型计算的强度进行比较。研究表明:预测结果与实测结果吻合较好,较线性回归和神经网络预测精度高,为高强混凝土的强度预测提供了一条新途径。     

基于BP神经网络的绿色混凝土抗压强度预测模型

与普通混凝土相比,绿色混凝土具有成分复杂的特点,为了在多因素作用下更为准确地预测绿色混凝土的抗压强度,在分析三层BP神经网络原理的基础上,选择影响绿色混凝土抗压强度的7个指标,以66个抗压强度试验为示例,建立了三层BP神经网络抗压强度预测模型.验证样本的训练结果表明,该模型能够较准确地快速预测绿色混凝土的抗压强度,并通过对各指标的权重计算,确定了影响绿色混凝土抗压强度的主要因素.     

基于拌和生产数据的BP神经网络混凝土抗压强度预测

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...     

基于BP神经网络的再生保温混凝土抗压强度预测

通过研究再生粗骨料取代率、水灰比对再生保温混凝土抗压强度的影响,建立了以再生粗骨料取代率、水灰比以及混凝土表观密度为因子的BP神经网络预测模型,旨在通过这三种因子的测量对再生保温混凝土28 d抗压强度进行预测。试验研究表明,当再生粗骨料取代率为50%时,再生保温混凝土抗压强度与混凝土拌合物表观密度近似成线性关系,抗压强度随着水灰比的增大而降低;当取代率为100%时,抗压强度与表观密度为非线性关系,抗压强度随表观密度的增大而增大,随水灰比的增加而增加。建立的三因子BP神经网络模型的预测值与实际值的误差在3%以内,可用于再生保温混凝土的抗压强度预测。     

三种神经网络在混凝土强度预测中的应用

通过建立改进的BP和RBF两种神经网络模型，对混凝土的强度进行预测，将预测值与常规BP神经网络模型预测结果进行了比较，研究表明，改进的BP和RBF的神经网络模型能够充分考虑影响混凝土强度的各种因素，在强度预测中具有广泛的应用前景。     

Ⅱ级灰对配制大掺量粉煤灰混凝土的性能影响

采用微观分析技术,研究了江西省当地产的Ⅱ级粉煤灰的性状,并选用当地典型原材料,采用超量取代的方法,选取5种不同掺量的粉煤灰,配制中等强度的大掺量粉煤灰混凝土(High Fly-ash ContentConcrete,简称HFCC),综合分析和探讨了大掺量粉煤灰混凝土的力学性能和耐久性。结果表明,与普通混凝土相比,采用江西省Ⅱ级灰配制的大掺量粉煤灰混凝土后期强度增幅显著,56d强度甚至能超过普通混凝土,弹性模量-强度比和抗渗性均高于普通混凝土,而抗冻性和抗碳化能力均有所下降。     

高掺量粉煤灰混凝土的性能研究—渗透性能及其影响因素

本文研究了高掺量粉煤灰混凝土(HFCC)的渗透性能,通过从粉煤灰的品质,粉煤灰在混凝土中的掺入量等方面来分析讨论粉煤灰时混凝土渗透性能的影响。     

Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN

An artificial neural network (ANN) is presented to predict a 28-day compressive strength of a normal and high strength self compacting concrete (SCC) and high performance concrete (HPC) with high volume fly ash. The ANN is trained by the data available in literature on normal volume fly ash because data on SCC with high volume fly ash is not available in sufficient quantity. Further, while predicting the strength of HPC the same data meant for SCC has been used to train in order to economise on computational effort. The compressive strengths of SCC and HPC as well as slump flow of SCC estimated by the proposed neural network are validated by experimental results.     

Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks

This study focuses on studying the effects of fly ash and silica fume replacement content on the strength of concrete cured for a long-term period of time by neural networks (NNs). Applicability of NNs to evaluate the effects of FA and SF for a long period of time is investigated. The investigations covered concrete mixes at different water cementitious materials ratio, which contained low and high volumes of FA, and with or without the additional small amount of SF. 24 different mixes with 144 different samples were gathered form the literature for this purpose. These samples consist concretes that were cured for 3, 7, 28, 56 and 180 days. A NN model is constructed trained and tested using these data. The data used in the NN model are arranged in a format of eight input parameters that cover the fly ash replacement ratio (FA), silica fume replacement ratio (SF), total cementitious material (TCM), fine aggregate (ssa), coarse aggregate (ca), water content (W), high rate water reducing agent (HRWRA) and age of samples (AS) and an output parameter which is compressive strength of concrete (f_c). A NN program was devised in MATLAB and the NN model was constructed in this program. The results showed that NNs have strong potential as a feasible tool for evaluation of the effect of cementitious material on the compressive strength of concrete. It was found that FA content contributed little at early ages but much at later ages to the strength of concrete. It can also be concluded that the enhancement effect of low content of SF on compressive strength was not significant.     

原状粉煤灰-铁尾矿泡沫混凝土的性能研究

采用Design-Expert软件研究了原状粉煤灰及铁尾矿砂对泡沫混凝土干密度、吸水率及抗压强度的影响,并观察了泡沫混凝土断面的细观结构.结果表明:原状粉煤灰掺量的增加会使泡沫混凝土的干密度和吸水率降低,当原状粉煤灰掺量占胶凝材料的40％时,泡沫混凝土的抗压强度最高;固定粉煤灰掺量为40％,铁尾矿砂掺量在0～50％范围...     

Predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives using artificial neural network

In this study, an artificial neural networks study was carried out to predict the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives. This study is based on the determination of the variation of core compressive strength, water absorption and unit weight in curtain wall elements. One conventional concrete (vibrated concrete) and six different self-compacting concrete (SCC) mixtures with mineral additives were prepared. SCC mixtures were produced as control concrete (without mineral additives), moreover fly ash and limestone powder were used with two different replacement ratios (15% and 30%) of cement and marble powder was used with 15% replacement ratio of cement. SCC mixtures were compared to conventional concrete according to the variation of compressive strength, water absorption and unit weight. It can be seen from this study, self-compacting concretes consolidated by its own weight homogeneously in the narrow reinforcement construction elements. Experimental results were also obtained by building models according to artificial neural network (ANN) to predict the core compressive strength. ANN model is constructed, trained and tested using these data. The results showed that ANN can be an alternative approach for the predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives.     

C50再生骨料混凝土的试验

试验采用正交设计的方法,利用再生骨料配制高强(C50)混凝土,同时研究水胶比、再生骨料取代量、硅粉和粉煤灰掺量对再生骨料混凝土和易性和抗压强度的影响。试验结果表明:坍落度随着水胶比的增大、再生骨料取代量和硅粉掺量的减小而增大;粉煤灰掺量对坍落度的影响较小;再生骨料混凝土的抗压强度随着水胶比和再生骨料取代量的降低、硅粉掺量的增加而提高,粉煤灰对抗压强度的影响较小。随着水化的发展,再生骨料对混凝土抗压强度的不利影响将逐渐减小。     

兰州市粉煤灰混凝土的应用研究

分析了兰州市普通混凝土的缺点,提出解决的办法是根据兰州市混凝土的发展,使普通混凝土向绿色高性能混凝土化。提出增大粉煤灰掺量、降低水泥用量是绿色高性能化的途径。对混凝土配合比进行优化设计,并予以应用。通过对优化配比的粉煤灰混凝土的抗压强度试验和早期环形约束收缩试验,以及经济性分析,得出结论,该混凝土具有价格优势,通过使用本地的材料能够实现混凝土绿色高性能化,这对甘肃省发展绿色材料有非常重要的意义。     

耐碱玻纤和粉煤灰对轻质混凝土强度及冻融耐久性的影响研究

以陶粒为粗骨料制备了轻质混凝土试件,研究了耐碱玻纤、粉煤灰增强材料对轻质混凝土的力学性能及冻融耐久性的影响。结果表明,随着耐碱玻纤掺量的增加,同一龄期轻质混凝土试件的抗压强度、抗拉强度先增大后减小;过高的耐碱玻纤掺量不利于强度的增长,且耐碱玻纤对试件抗拉强度的影响大于抗压强度,其最优掺量为0.6 kg/m^3;掺入适量的粉煤灰(≤15%)能提高轻质混凝土的强度,提升幅度与掺量成正比,但掺量较大时对强度不利;与未掺耐碱玻纤的试件相比,当耐碱玻纤掺量低于0.6 kg/m^3和1.0 kg/m^3时,能分别提升试件的相对动弹性模量和降低质量损失率,改善幅度与耐碱玻纤的掺量正相关;粉煤灰掺量低于15%时有利于提高试件的冻融耐久性,但掺量较高(≥20%)则会降低试件的冻融耐久性指标。     

Influence of fly ash on strength and sorption characteristics of cold-bonded fly ash aggregate concrete

Cold-bonded fly ash aggregate concrete with fly ash as part of binder or fine aggregate facilitates high volume utilization of fly ash in concrete with minimum energy consumption. This paper investigates the influence of fly ash on strength and sorption behaviour of cold-bonded fly ash aggregate concrete due to partial replacement of cement and also as replacement material for sand. While cement replacement must be restricted based on the compressive strength requirement at desired age, replacement of sand with fly ash appears to be advantageous from early days onwards with higher enhancement in strength and higher utilization of fly ash in mixes of lower cement content. Microstructure of concrete was examined under BSEI mode. Replacement of sand with fly ash is effective in reducing water absorption and sorptivity attributable to the densification of both matrix and matrix–aggregate interfacial bond. Cold-bonded fly ash aggregate concrete with a cement content of 250 kg/m³, results in compressive strength of about 45 MPa, with a total inclusion of around 0.6 m³ of fly ash in unit volume of concrete.