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.     

基于神经网络的植生型多孔混凝土抗压强度预测模型

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.     

Predicting the compressive strength and slump of high strength concrete using neural network

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.     

Multiple linear regression,artificial neural network,and fuzzy logic prediction of 28 days compressive strength of concrete

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.     

双掺粉煤灰和矿渣混凝土强度的BP网络预测模型

双掺粉煤灰和矿渣混凝土的强度发展机理复杂,不能用传统的水灰比线性函数来预测,利用BP神经网络模型来预测其3、28和56d的抗压强度.结果表明:BP神经网络具有较强的非线性映射能力,预测结果比较理想,可以指导实际工程;早龄期的混凝土强度预测值与实测值之间的误差较大,随着粉煤灰和矿渣的二次水化反应逐渐充分,强度发展趋于规律化,预测误差相应变小.     

Hybrid random aggregation model and Bayesian optimization-based convolutional neural network for estimating the concrete compressive strength

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.     

高强混凝土抗压强度的试验研究

随着建筑业的快速发展,混凝土的强度提高是世界各国土木工程界普遍关注的问题。掺有硅灰的混凝土的耐久性得到了研究。研究水胶比和硅灰掺量等参数对高强混凝土的抗压强度的影响.通过试验的方法,找出不同情况下,高强混凝土的最优配合比设计。     

Prediction of density and compressive strength for rubberized concrete blocks

The influence of rubber content within the range of 5–50% as the replacement for sand volume and water/cement (w/c) ratio (0.45–0.55) on the density and compressive strength of concrete blocks was investigated. All the mixtures were proportioned with a fixed aggregate/cement ratio of 5.6. A total of 50% of the total aggregate was fine aggregate. Based on the experimental results, the density and strength reduction factors for rubberized concrete blocks were calculated by considering the dependent factors of rubber content and w/c ratio. Linear and logarithm equations derived, based on the results from experimental work are proposed to predict the density and compressive strength of rubberized concrete blocks.     

Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models

The safety control of large dams is based on the measurement of some important quantities that characterize their behaviour (like absolute and relative displacements, strains and stresses in the concrete, discharges through the foundation, etc.) and on visual inspections of the structures. In the more important dams, the analysis of the measured data and their comparison with results of mathematical or physical models is determinant in the structural safety assessment.In its lifetime, a dam can be exposed to significant water level variations and seasonal environmental temperature changes. The use of statistical models, such as multiple linear regression (MLR) models, in the analysis of a structural dam’s behaviour has been well known in dam engineering since the 1950s. Nowadays, artificial neural network (NN) models can also contribute in characterizing the normal structural behaviour for the actions to which the structure is subject using the past history of the structural behaviour. In this work, one important aspect of NN models is discussed: the parallel processing of the information.This study shows a comparison between MLR and NN models for the characterization of dam behaviour under environment loads. As an example, the horizontal displacement recorded by a pendulum is studied in a large Portuguese arch dam. The results of this study show that NN models can be a powerful tool to be included in assessments of existing concrete dam behaviour.     

Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network and support vector machine

Plastic concrete is an engineering material, which is commonly used for construction of cut-off walls to prevent water seepage under the dam. This paper aims to explore two machine learning algorithms including artificial neural network (ANN) and support vector machine (SVM) to predict the compressive strength of bentonite/sepiolite plastic concretes. For this purpose, two unique sets of 72 data for compressive strength of bentonite and sepiolite plastic concrete samples (totally 144 data) were prepared by conducting an experimental study. The results confirm the ability of ANN and SVM models in prediction processes. Also, Sensitivity analysis of the best obtained model indicated that cement and silty clay have the maximum and minimum influences on the compressive strength, respectively. In addition, investigation of the effect of measurement error of input variables showed that change in the sand content (amount) and curing time will have the maximum and minimum effects on the output mean absolute percent error (MAPE) of model, respectively. Finally, the influence of different variables on the plastic concrete compressive strength values was evaluated by conducting parametric studies.     

Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic

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.     

石灰石粉混凝土抗压强度试验研究

使用3种粒径,4种掺量的石灰石粉等质量取代部分水泥,设计了石灰石粉混凝土,通过对C30混凝土拌合物进行抗压强度试验研究,找出了在不同粒径、不同掺量条件下,石灰石粉对混凝土抗压强度的影响规律。     

废橡胶混凝土抗压强度试验研究

本文研究废轮胎橡胶粉碎料作为一种添加成分取代部分粗骨料的混凝土的抗压强度。抗压强度的测试方法同时参照ASTM和普通混凝土力学性能试验方法标准,其中还制备了100mm×100mm×100mm的试件,以分析不同测试方法和尺寸效应的影响。试件中采用的橡胶分别为橡胶粉和橡胶块,掺量为粗骨料体积的15%,30%,45%,以考察橡胶粒度和掺量变化对抗压强度的影响。试件制备时,还测试了坍落度与密度等参数。     

人工神经网络在掺活化煤矸石粉混凝土强度预测中的应用

人工神经网络技术综合考虑了掺活化煤矸石混凝土强度的各种影响因素,可用于预测混凝土强度.选取了掺活化煤矸石粉混凝土配料中7个主要因素作为输入值,混凝土28d强度作为输出值,建立起混凝土强度预测BP网络模型,进而对掺活化煤矸石配合比强度试验数据进行分析预测,效果良好.结果表明该方法用于掺矿物掺合料混凝土强度预测方面是可行的.     

高强混凝土芯样抗压强度试验研究

针对采用不同粗骨料配制的50～90 MPa混凝土,分别钻取尺寸为φ100mm和φ70 mm两种芯样,测定其抗压强度,并与同条件养护的混凝土立方体抗压强度进行比较.试验结果表明,骨料品种对不同尺寸芯样的抗压强度影响不大,φ100mm芯样抗压强度（f10cor）与同条件养护的100mm立方体试件抗压强度（f10cu）相当,φ70 mm芯样抗压强度（f7cor）平均高出100mm立方体试件抗压强度约13%.因此,采用小芯样评定高强混凝土抗压强度时,需要进行修正.     

二级配干硬混凝土抗冻耐久性的试验研究

从粉煤灰的掺量与品质、含气量、配比、水灰比、龄期、气泡特性等方面对二级配干硬混凝土抗冻耐久性的研究表明：选用品质好的粉煤灰和合适的掺量，掺入外加剂并使混凝土达到足够的含气量，可以大大地提高其抗冻耐久性；另外，采用合适的水泥用量对提高抗冻性也有明显的效果。     

再生混凝土抗压强度研究

本文对再生混凝土的抗压强度进行了试验分析.重点分析了再生粗骨料取代率及水灰比对再生混凝土抗压强度的影响,再生混凝土的抗压强度与表观密度的关系,并建议给出再生混凝土的配合比设计方法.     

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.     

再生混凝土的抗压强度初步研究

设计并完成了相关试验,系统研究了再生混凝土的抗压强度特征。主要包括再生粗骨料含量对再生混凝土抗压强度的影响,再生混凝土抗压强度随龄期的发展规律,再生混凝土的龄期系数以及普通混凝土28 d抗压强度方程对再生混凝土的适用性。试验结果表明,随着再生粗骨料增加,混凝土的抗压强度降低;再生混凝土的抗压强度发展规律与普通混凝土基本一致,但是再生混凝土各龄期系数均低于普通混凝土,表明其强度增长较慢;普通混凝土28 d抗压强度方程不适用于再生混凝土。本文的研究结果对再生混凝土在实际中的推广应用具有重要的价值。