Deep neural network with high‐order neuron for the prediction of foamed concrete strength

The article presents a deep neural network model for the prediction of the compressive strength of foamed concrete. A new, high‐order neuron was developed for the deep neural network model to improve the performance of the model. Moreover, the cross‐entropy cost function and rectified linear unit activation function were employed to enhance the performance of the model. The present model was then applied to predict the compressive strength of foamed concrete through a given data set, and the obtained results were compared with other machine learning methods including conventional artificial neural network (C‐ANN) and second‐order artificial neural network (SO‐ANN). To further validate the proposed model, a new data set from the laboratory and a given data set of high‐performance concrete were used to obtain a higher degree of confidence in the prediction. It is shown that the proposed model obtained a better prediction, compared to other methods. In contrast to C‐ANN and SO‐ANN, the proposed model can genuinely improve its performance when training a deep neural network model with multiple hidden layers. A sensitivity analysis was conducted to investigate the effects of the input variables on the compressive strength. The results indicated that the compressive strength of foamed concrete is greatly affected by density, followed by the water‐to‐cement and sand‐to‐cement ratios. By providing a reliable prediction tool, the proposed model can aid researchers and engineers in mixture design optimization of foamed concrete.     

Neural network prediction of concrete degradation by sulphuric acid attack

Microbiologically induced corrosion is a leading cause of the deterioration of wastewater collection, transmission and treatment infrastructure around the world. This paper examines the feasibility of using artificial neural networks (ANNs) to predict the compressive strength of concrete and its degradation under exposure to sulphuric acid of various concentrations. A database incorporating 78 concrete mixtures performed by the authors was developed to train and test the ANN models. Data were arranged in a patterned format in such a manner that each pattern contains input variables (concrete mixture parameters) and the corresponding output vector (weight loss of concrete by H₂SO₄ attack and compressive strength at different ages). Results show that the ANN model I successfully predicted the weight loss of concrete specimens subjected to sulphuric acid attack, not only for mixtures used in the training process, but also for new mixtures unfamiliar to the ANN model designed within the practical range of the input parameters used in the training process. Root-mean-squared error (RMSE) and average absolute error (AAE) for ANN predictions of weight loss due to sulphuric acid attack were 0.013 and 8.45%, respectively. The ANN model II accurately predicted the compressive strength of the various concrete mixtures at different ages with RMSE and AAE of 2.35 MPa and 4.49%, respectively. A parametric study shows that both models I and II can successfully capture the sensitivity of output variables to changes in input parameters.     

影响塑性混凝土强度的因素分析

分析了水泥掺量、水胶比和膨润土与黏土掺量对塑性混凝土无侧限抗压强度的影响,试验结果表明,塑性混凝土无侧限抗压强度随水泥掺量的增加而增大;随着水胶比的增加,强度有明显减小;随着膨润土与黏土的掺入比例增加,塑性混凝土的抗压强度有一定降低。     

塑性混凝土抗压强度试验研究

为了探讨塑性混凝土抗压强度的影响因素,给出了测试塑性混凝土抗压强度的合理方法,针对原材料和养护条件对塑性混凝土抗压强度进行了各种对比试验。通过对影响塑性混凝土抗压强度因素的详细分析,得出了各因素对塑性混凝土抗压强度的影响规律。结果表明,减小水胶比及黏土和膨润土的用量,增加水泥用量,掺加粉煤灰和外加剂,均可提高塑性混凝土的抗压强度。     

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.     

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.     

基于LS-SVM的粉煤灰混凝土强度预测模型

提出一种基于最小二乘支持向量机(LS-SVM)的粉煤灰混凝土强度智能预测模型,并给出了相应的步骤和算法。通过该模型分析了水胶比、水泥用量、粉煤灰替代率及砂率等因素对粉煤灰混凝土强度的影响。在此基础上,对不同配比所浇注的混凝土强度进行预测,有助于准确认识混凝土强度随配比参数的变化规律。与多元线性回归、神经网络及标准SVM模型比较,该模型的优点为:(1)采用了结构风险最小化准则,在最小化样本误差的同时减小模型泛化误差的上界,提高了模型小样本泛化能力;(2)将迭代学习算法转换为求解线性方程组,使得整个模型仅有一个全局最优点,解决局部最小问题;(3)用等式约束代替标准SVM算法中的不等式约束,将求解二次规划问题转化为直接求解线性矩阵方程,有效提高建模速度。用该模型对混凝土的强度预测实例表明,其建模速度比标准SVM高近1个数量级,预测误差仅为SVM方法的20%、BP神经网络方法的10%左右。     

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.     

Development of deep neural network model to predict the compressive strength of FRCM confined columns

The present study describes a reliability analysis of the strength model for predicting concrete columns confinement influence with Fabric-Reinforced Cementitious Matrix (FRCM). through both physical models and Deep Neural Network model (artificial neural network (ANN) with double and triple hidden layers). The database of 330 samples collected for the training model contains many important parameters, i.e., section type (circle or square), corner radius r_c, unconfined concrete strength f_co, thickness n_t, the elastic modulus of fiber E_f , the elastic modulus of mortar E_m. The results revealed that the proposed ANN models well predicted the compressive strength of FRCM with high prediction accuracy. The ANN model with double hidden layers (APDL-1) was shown to be the best to predict the compressive strength of FRCM confined columns compared with the ACI design code and five physical models. Furthermore, the results also reveal that the unconfined compressive strength of concrete, type of fiber mesh for FRCM, type of section, and the corner radius ratio, are the most significant input variables in the efficiency of FRCM confinement prediction. The performance of the proposed ANN models (including double and triple hidden layers) had high precision with R higher than 0.93 and RMSE smaller than 0.13, as compared with other models from the literature available.     

钢-混凝土粘结预测的人工神经网络模型

提出人工神经网络模型来模拟传统的带肋钢筋和混凝土之间的粘结性能,目的是预测钢筋从混凝土混合物中拔出的极限荷载(第一神经网络模型)或抗压强度(第二神经网络)以及根据RILEM试验设计的不同钢筋直径的拔出极限荷载。采用112个带肋钢筋(直径为10mm、12mm)以及三种不同混凝土配合比的拔出试验结果数据库,对神经网络模型进行训练。根据反向传播算法,进行多层感知器训练。第一个模型(ANN-6)有6个输入:钢筋直径、水灰比、砂石比、级配、水泥种类和混凝土龄期。第二个模型(ANN-2)有2个输入:钢筋直径、混凝土抗压强度,两个模型的输出均为极限拔出荷载。研究结果显示:所采用的模型预测精度高、误差低、具有鲁棒性。从鲁棒性方面,第一个模型(ANN-6)比第二个模型(ANN-2)更精确。将混凝土的成分作为输入参数,而不是混凝土的强度,对于带肋钢筋-混凝土界面的局部现象更具代表性。     

Artificial neural network model for steel–concrete bond prediction

In this paper, an Artificial Neural Network (ANN) is proposed for modelling the bond between conventional ribbed steel bars and concrete. The purpose is to predict the ultimate pull-out load from the concrete mix constituents (first ANN model) or the compressive strength (second ANN model) and from the steel bar diameter according to the RILEM test configuration [RILEM. Essai portant sur l’adhérence des armatures du béton: essai par traction. Materials and Structures 1970; 3 (3) 175–78]. The ANN models were implemented using an experimental database of 112 pull-out test results performed with ribbed bars 10 mm or 12 mm in diameter and three concrete mixes with different constituent proportions. A Multi-Layer-Perceptron was trained according to a back-propagation algorithm. The first model has six inputs (ANN-6): the diameter of the ribbed bar, the water to cement ratio, the gravel to sand ratio, the crushed to rolled gravel ratio, the type of cement and the concrete maturity. The second model has two inputs (ANN-2): the diameter of the bar and the concrete compressive strength. The ultimate pull-out load was the output data for both models.The results show that the implemented models have good prediction and generalisation capacity with low errors. The ANN-6 model is more accurate, regarding the generalisation capacity, than the ANN-2 model. Concrete mix constituents as input parameter, instead of the compressive strength, are more representative of the local phenomenon at the steel-ribs-to-concrete interface.     

基于神经网络的碳纤维布与混凝土极限粘结强度分析

碳纤维布与混凝土的极限粘结强度问题属于高度非线性问题,难以建立精确的数学表达式进行分析。对基于拉出试验的极限粘结强度数据进行分析,建立了人工神经网络,对极限粘结强度进行仿真预测。神经网络的建立考虑了碳纤维布的厚度、宽度、粘结长度、弹性模量、抗拉强度和混凝土试块抗压强度、抗拉强度、宽度这8个参数,运用了118组试验数据对网络进行训练,对15组数据进行了预测分析。将神经网络计算结果同4种经验公式计算结果进行比较,其精度明显高于其他4种模型。结果表明,运用人工神经网络对碳纤维布与混凝土的极限粘结强度进行预测是可行的。     

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.     

重复压应力作用下矿渣混凝土变形性能

采用WHY全自动应力试验机测量了最大压应力为轴心抗压强度8096重复5次时混凝土的变形。结果表明磨细矿渣对混凝土的变形性能有显著影响。磨细矿渣掺量从10％增加到30％时，混凝土28d抗压强度和静弹性模量随矿渣掺量增加而增加，最大变形随矿渣掺量增加而减小，矿渣掺量为10％和20％时，混凝土的残余变形略大于基准混凝土；矿渣掺量为40％时，28d抗压强度和静弹性模量低于基准混凝土，最大变形和残余变形均明显大于基准混凝土的变形。     

可以考虑压应力球量历史影响的混凝土强度准则

根据材料三向等压荷载试验的结果 ,建立了考虑应力球量荷载历史影响的混凝土五参数强度准则。所进行的三向等压荷载试验选取了单轴抗压强度分别为 2 1 3MPa和 7 54MPa的A、B两组试块 ,获得了试块抗压强度损失和抗拉强度损失与所经历的以最大平均应力表示的荷载历史的经验关系 ,并发现两组试块所表示的经验关系中的参数基本相同。同时 ,还系统地研究和分析了各种加载路径对强度损失的影响。     

碳化与冻融交替作用下的混凝土抗压强度

通过室内单一碳化、单一冻融,以及碳化与冻融交替作用下的混凝土耐久性循环试验,对比分析了混凝土相对抗压强度、相对动弹性模量和碳化深度等指标的变化规律.结果表明:在碳化与冻融交替作用下,混凝土相对抗压强度要比单一冻融作用时大,但增加程度有限;混凝土相对动弹性模量要比单一冻融作用时小,碳化深度则比单一碳化作用时大.碳化与冻融交替作用下的混凝土抗冻耐久性较之单一冻融作用下有所下降,抗碳化能力较之单一碳化作用下有所减弱.最后建立了碳化与冻融交替作用下以碳化时间和冻融循环次数为变量的混凝土抗压强度拟合模型.     

Application of artificial neural networks for predicting the cuttability of rocks by drag tools

Many models have previously been developed for predicting specific cutting energy (SE), being the measure of rock cuttability, from intact rock properties employing conventional multiple linear or nonlinear regression techniques. Artificial neural networks (ANN) also have a great potential in building such models. This paper is concerned with the application of ANN for the prediction of cuttability of rocks from their intact properties. For that purpose, data obtained from three different projects were subjected to statistical analyses using MATLAB. Principal components analysis together with the scatterplots of SE against intact rock properties were employed to select the predictors for SE models. Results of the principal components analysis have shown that the most of the variance in the data set can be explained by three principal components. Principal component with the highest variance is weighted mainly on the uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), static modulus of elasticity (Elasticity), and cone indenter hardness (CI), which were regarded as the independent variables driving the data set. Three predictive models for SE were developed employing above independent variables by multiple nonlinear regression with forward stepwise method and ANN, respectively. Neural networks were developed for two different numbers of hidden neurons in the hidden layer. Goodness of the fit measures revealed that ANN models fitted the data as accurately as multiple nonlinear regression model, indicating the usefulness of artificial neural networks in predicting rock cuttability.     

低弹模高抗渗性防渗墙混凝土配合比设计研究

文章结合乐眉水库坝体防渗墙混凝土试验研究,通过正交试验,分析了水胶比、膨润土掺量、引气剂用量3因素对防渗墙混凝土抗压强度、劈裂抗拉强度、弹性模量和渗透系数的影响。研究结果表明:水胶比和引气剂掺量是影响防渗墙混凝土抗压强度、劈裂抗拉强度和弹性模量的关键因素,水胶比和膨润土掺量是影响防渗墙混凝土渗透系数的关键因素。从满足抗压强度(≥5 MPa)和弹性模量(≤2 000 MPa)和良好的抗渗性能等方面来看,防渗墙混凝土的最佳配比为:膨润土掺量50%,水胶比0.9,引气剂用量0.02%。     

再生塑料改性混凝土力学性能研究及数值模拟

将再生ABS/PC塑料颗粒掺入混凝土中制成塑料改性混凝土,对该改性混凝土进行立方体抗压强度、轴心抗压强度、劈裂抗拉强度和抗折强度试验,研究了不同掺量再生ABS/PC塑料颗粒对混凝土力学性能的影响.基于二维圆形随机骨料模型,运用有限元方法进行单轴压缩细观数值模拟,得到了不同掺量下再生塑料改性混凝土的应力-应变曲线;将单轴...     

Effect of recycling hospital ash on the compressive properties of concrete: statistical assessment and predicting model

The compressive strength of mixtures made with bottom and fly hospital ash are compared statistically with those of microsilica and conventional concretes in order to evaluate the effectiveness of reusing hospital incinerator ash. Results showed that the concrete cubes recipe and temperature influence the compressive strength values. Generally, the use of microsilica or fly ash, as cement replacement, of 5% by mass increases the compressive strength of concrete at temperatures up to 800°C. Bottom ash did not achieve any increase in strength when used as cement replacement at all percentages.Finally, artificial neural network (ANN) analysis was conducted to study the practicability of using the ANN theoretical model as a prediction tool. The results showed that a one hidden layer back propagation algorithm could produce reasonable outputs that coincide with the experimental results after proper training.