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。     

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.     

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.     

Prediction of elastic modulus of normal and high strength concrete by support vector machine

Elastic modulus is an important property of concrete and is used to calculate deformation of structures. Support vector machine (SVM) is firmly based on learning theory and uses regression technique by introducing accuracy insensitive loss function. This paper investigates the use of SVM to predict elastic modulus of normal and high strength concrete. The elastic modulus predicted by SVM was compared with the experimental data and those from other prediction models. SVM demonstrated good performance and proven to be better than other models.     

基于两相复合材料的再生混凝土弹性模量预测模型

与采用再生混凝土抗压强度计算弹性模量的公式相比,基于两相复合材料的再生混凝土弹性模量预测模型能更好地反映再生粗骨料对混凝土弹性模量的影响,预测结果更为精确。但现有再生混凝土两相复合材料弹性模量预测模型是由再生粗骨料与砂浆的弹性模量按体积加权平均得到的,该种计算方法预测结果偏于保守。目前针对天然骨料混凝土已提出多种两相复合材料弹性模量预测模型,但各模型预测结果有较大差异。基于上述模型,考虑再生粗骨料中残余砂浆的影响,推导得到了6种再生混凝土弹性模量预测模型,并搜集了100组再生混凝土弹性模量试验数据,用于评价各模型的可靠性。在此基础上,统计分析了模型不确定性及其与关键参数之间的相关性,为后续再生混凝土构件与结构的可靠度分析提供统计数据。研究表明,在常见参数范围内,各模型预测结果之间最大可相差23.3%;所提出的BNC(RAC)模型预测精度最高,其预测结果与试验结果比值的均值为1.014,变异系数为8.2%,该模型的不确定性服从正态分布。     

盐侵蚀环境下浮石混凝土的冻融损伤模型

主要进行了LC20、LC30、LC40浮石混凝土在清水中和浓度为16.55%的氯化钠盐渍溶液中的快速冻融循环试验,计算出了各个强度浮石混凝土在不同冻融介质中的质量损失率和相对动弹性模量。利用origin对浮石混凝土在清水冻融和盐渍溶液冻融下的质量损失率和相对动弹性模量进行了拟合曲线分析,建立了以质量损失率和相对动弹性模量为损伤变量的冻融损伤模型,并预测了其剩余寿命,结果表明:浮石混凝土强度越大,抗冻耐久性寿命越长;清水冻融循环下浮石混凝土的抗冻耐久性寿命比盐渍冻融循环下更长。并且得出,相对动弹性模量比质量损失率更适合作为损伤变量来建立浮石混凝土的冻融损伤模型。     

Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network

This paper is aimed at adapting Artificial Neural Networks (ANN) to predict the strength properties of SIFCON containing different minerals admixture. The investigations were done on 84 SIFCON mixes, and specimens were cast and tested after 28 days curing. The obtained experimental data are trained using ANN which consists of 4 input parameters like Percentage of fiber (PF), Aspect Ratio (AR), Type of admixture (TA) and Percentage of admixture (PA). The corresponding output parameters are compressive strength, tensile strength and flexural strength. The predicted values obtained using ANN show a good correlation between the experimental data. The performance of the 4-14-3 architecture was better than other architectures. It is concluded that ANN is a highly powerful tool suitable for assessing the strength characteristics of SIFCON.     

Prediction of shear strength of steel fiber RC beams using neural networks

This paper presents the development of artificial neural network models for predicting the ultimate shear strength of steel fiber reinforced concrete (SFRC) beams. Two models are constructed using the experimental data from the literature and the results are compared with each other and with the formula proposed by Swamy et al. and Khuntia et al. It is found that the neural network model, with five input parameters, predicts the shear strength of beams more closely than the network with four input parameters. Moreover, the neural network models predict the shear strength of SFRC beams more accurately than the above-mentioned formulas. Further, the accuracy of predicted results is found not biased with concrete strength, shear span to depth ratio and the beam depth. Limited parametric studies show that the network model captures the RC beam’s underlying shear behavior very well.     

海水海砂钢纤维混凝土的基本力学性能

制备了强度等级为C30、C50和C70的海水海砂钢纤维混凝土试件,通过180个标准立方体和72个棱柱体试件,完成了工作性、立方体抗压强度、轴心抗压强度、劈裂抗拉强度以及弹性模量试验,得到了基于两种规范模式下海水海砂钢纤维混凝土的弹性模量与立方体抗压强度的关系公式。结果表明,海水海砂能够配置成工作性良好的高强混凝土,钢纤维有利于提升混凝土拌合物的流动性。对于混凝土抗压强度、轴心抗压强度、劈裂抗拉强度和弹性模量四个指标,海水海砂混凝土均略低于普通混凝土,且随着混凝土强度等级的提高,差距逐渐减小,此外,随着钢纤维体积掺量的增加,上述指标值均逐渐增大。海水海砂混凝土的弹性模量与抗压强度关系模型与试验数据吻合较好,且具有一定安全储备,可供沿海、海岛土木加固工程借鉴。     

高温后钢筋-混凝土界面黏结性能分析

基于均匀分布压力作用的厚壁圆筒模型,将钢筋混凝土拉拔试件变形钢筋周围的受高温损伤混凝土保护层按应力状态分为内外两部分,对内层开裂混凝土认为其产生弥散裂缝,并考虑其抗拉软化特性,同时引入高温后混凝土弹性模量、抗拉强度、断裂能的劣化,通过对受高温损伤钢筋-混凝土间黏结破坏时的极限状态进行理论分析,推导得出高温后钢筋-混凝土界面黏结强度的计算方法,建立了与钢筋、混凝土的尺寸、材性相关的高温后钢筋-混凝土界面黏结强度模型。基于混凝土开裂半径与端部滑移之间的线性关系,建立了高温后界面黏结应力-端部滑移关系。对模型计算结果与已有高温后钢筋与混凝土黏结性能试验所得数据进行比较,共对比了118组黏结强度、15组黏结应力-端部滑移关系。结果表明:该理论分析模型具有很高的准确性,可广泛适用于不同参数拉拔试验的高温后界面黏结强度的分析与预测。     

Mix proportions for HPC incorporating multi-cementitious composites using artificial neural networks

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.     

外掺硅粉混凝土早龄期强度及弹性模量试验研究

为了研究外掺硅粉混凝土早龄期的抗压强度及弹性模量,以不同硅粉掺量及时间龄期为变化参数,共设计并完了168个棱柱体试块的静力加载试验。观察了试件的破坏形态,获取了外掺硅粉混凝土早龄期抗压强度及变形等关键特征参数,揭示了外掺硅粉对混凝土早龄期强度和弹性模量影响的变化规律。研究结果表明:外掺硅粉可细化混凝土内部的孔结构,减少骨料与水胶体间的孔隙率,增加混凝土的密实度,从而有效提高混凝土的早期强度和弹性模量;当外掺硅粉掺量为1.4%对混凝土力学性能的提高最显著,其中3 d强度提高了35%,弹性模量提高了62.7%。     

Use of an improved ANN model to predict collapse depth of thin and extremely thin layered rock strata during tunnelling

Numerous collapses have occurred during the excavation of diversion tunnels in the thin and extremely thin layered rock strata at Wudongde Hydropower Station in China. Hence, a reliable method is required to predict the risk and the depth of collapse. However, both theory and practice indicate that one single criterion methods cannot satisfactorily predict the collapse depth accurately. In this study, using an artificial neural network (ANN), an intelligent prediction method has been investigated. Through theoretical and statistical analyses, six input parameters (i.e., cover depth, minor–major principal stress ratio, geological strength index, excavation method, support strength and attitude of rock), have been selected and used in the model. Obtained from three diversion tunnels at Wudongde Hydropower Station, forty-five learning samples and six testing samples were used in the training of the model. The structural parameters and the initial weights of the ANN have been optimized by a genetic algorithm (GA). The trained model was then used to predict the collapse depth of another six excavation sites. The predictions show good agreement with the measurements at the sites. The absolute errors between the predicted and the measured collapse depths are all less than 0.35 m, and the relative errors are all less than 15%. Application of the improved ANN method to the tunnel collapse analysis at Wudongde Hydropower Station confirms its effectiveness in predicting collapse depth during tunnelling.     

箍筋约束再生混凝土受压应力-应变本构关系模型

为获得箍筋约束再生混凝土受压应力-应变关系曲线,对17个足尺再生混凝土圆形截面柱进行轴心受压试验,再生粗骨料取代率为50%,加载应变率为(10^-6～10^-4)/s,主要变化参数为纵筋配筋率和箍筋约束水平。结果表明:箍筋约束再生混凝土的初始弹性模量主要与棱柱体抗压强度有关,棱柱体抗压强度越高,初始弹性模量越大;峰值应力在箍筋侧向约束下显著增大,提高幅度为侧向压应力的4.17～8.77倍,当箍筋提供的侧向压应力较小时,峰值应力增大程度较大,当箍筋提供的侧向压应力较大时,峰值应力增大程度较小;峰值应变随配箍水平增大呈增大趋势。基于试验数据,提出了箍筋约束再生混凝土受压本构关系模型,上升段与Mander模型一致,下降段中引入曲线形状参数,以调整曲线陡峭程度,结果表明,所提出的约束本构关系模型与试验曲线吻合良好,可较好地反映再生混凝土下降段较为陡峭的特点。     

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.     

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.     

High Temperature Mechanical Properties of High Strength Structural Steels Q550, Q690 and Q890

This paper reports the results of an experimental study on high temperature mechanical properties of high strength structural steel produced in accordance with Chinese materials standards. Steady-state tensile coupon tests were carried out on specimens made of China grade steels of Q550, Q690 and Q890. Nine elevated temperature levels up to 800°C were considered. The elastic modulus, yield strength, ultimate strength and ultimate strain were derived from the measured stress–strain curves. A model was developed to predict the high temperature properties of these steels using an approach developed by the National Institute of Standards and Technology and by calibrating the model parameters to the test data. The test results are compared to other tests on high strength steels reported in literature. The test results are also compared to predictions of high temperature properties from various building codes and other standards. The study found that steel grade has significant effect on the reduction factors. The difference between the reduction factors of elastic modulus for Q690 and Q550 was 30% at 600°C. In this study, reduction factor is defined as the ratio of the high temperature property to the corresponding room temperature property. The study also found that the material models in current codes are not applicable to the investigated high strength steels.     

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.