New numerical procedure for the prediction of temperature development in early age concrete structures

A new numerical model for the prediction of temperature development in young concrete structures is briefly presented. With the pre-program, adiabatic hydration curves, which are used to determine the internal heat generation, are calculated. An artificial neural networks approach is used for this purpose. Adiabatic hydration curves, which were included in the learning set, were determined by our own experiments, using the adiabatic calorimeter which uses air as the coupling media. The main program is implemented in the finite element code. This program allows concrete structure designers and contractors to quantify and evaluate the effects of some concrete initial parameters on the adiabatic hydration curves and corresponding temperature development at an arbitrary point in the concrete element. Some examples are also presented and discussed.     

大体积混凝土温升预测的非线性模型

大体积混凝土的水化热若不能及时散发,会产生很大的温度应力,导致出现温度裂缝。为了避免温度裂缝的产生,人们必须预测和控制大体积混凝土的温度形成。针对大体积混凝土温度场的非稳态特性,提出了一种基于灰色人工神经网络的温升预测模型,介绍了灰色神经网络预测方法在工程中的应用,采用Matlab进行计算。预测结果表明,该模型收敛速度快,预测精度较高,实现了对大体积混凝土温升的准确预测。说明了灰色人工神经网络方法的可行性和实用性。     

Iterative Equation Solver for Bridge Analysis Using Neural Networks

A technique for enhancing finite-element analysis equation solvers for particular problem domains, i.e., particular classes of structures such as highway bridges, is presented. The technique involves merging artificial neural networks, used as a domain knowledge-encoding mechanism, together with a preconditioned conjugate gradient iterative equation-solving algorithm. In the algorithm, neural networks are used to seed the initial solution vector and to precondition the matrix system using customizable and trainable neural networks. A case study is presented in which the technique is applied to the particular domain of flat-slab highway bridge analysis. In the case study, neural networks are trained to encode the load-displacement relationships for concrete flat-slab highway bridges. Analytical load-displacement data are generated using finite-element analyses and subsequently used to train neural networks. Acting collectively, the neural networks predict approximate displacement patterns for flat-slab bridges under arbitrary loading conditions.     

混凝土绝热温升的实验测试与分析

从理论上讨论了影响混凝土绝热温升的主要因素，并通过实验分析了初始入模温度、掺加粉煤灰和减水剂等因素对混凝土绝热温升和温升速率的影响．结果表明，提高混凝土初始入模温度将加速胶凝材料的水化，并缩短水化反应持续时间，这对低强度等级混凝土所用胶凝材料的水化程度影响不大，所以对其绝热温升值影响不显著，但明显降低高强度等级混凝土所用胶凝材料的水化程度，使其绝热温升值下降．掺加粉煤灰或减水剂来改变混凝土的工作性也会影响混凝土的绝热温升值和温升速率．     

漠河多年冻土区砼灌注桩承载力形成时间数值分析

根据带内热源伴有相变瞬态温度场的控制方程,在空间域内用采用混合单元的有限元网格划分,在时间域内用有限差分格式划分的混合解法编制有限元计算程序。针对黑龙江省漠河多年冻土区场地,根据实验得到的不同水泥水化热释放热量,进行钻孔灌注桩周土体温度场数值计算,分析影响砼灌注桩冻结强度形成的因素和变化规律及原因。研究表明砼浇注温度、不同外加剂组合水化热放热量对桩壁土体温度有显著的影响。在漠河多年冻土区,建议混凝土外加剂选用粉煤灰+硅粉+早强剂+减水剂组成,灌注桩混凝土向冻土钻孔中浇注温度应控制在5℃,这样可以大大缩短混凝土灌注桩承载力形成时间。     

Modeling of hydration kinetics in cement based materials considering the effects of curing temperature and applied pressure

Portland cement is the most widely used cement in the world. In the industrial by-products suitable for use as mineral admixtures in Portland concrete are ashes produced from the combustion of coal and granulated slag in metal industries. However, comparing such ashes with Portland cement, determining the hydration of this concrete is much more complex because of the reaction between calcium hydroxide and fly ash or slag. In this paper, the production of calcium hydroxide in cement hydration and its consumption in the reaction of mineral admixtures are considered in order to develop a numerical model for simulating the hydration of concrete, which contains fly ash or slag. The proposed numerical model includes the effects of water to binder ratios, slag or fly ash replacement ratios, curing temperature, and applied pressure. The heat evolution rate of fly ash- or slag-blended concrete is determined by the contribution of both cement hydration and the reaction of mineral admixtures. Furthermore, an adiabatic temperature rise in hardened blended concrete is evaluated based on the degree of hydration of the cement and mineral admixtures. The proposed model is verified through experimental data obtained from the concrete with different water-to-cement ratios and mineral admixture substitution ratios at elevated temperature and high pressure.     

混凝土绝热温升的影响因素

研究了混凝土的初始入模温度、流动度和掺加粉煤灰等因素对混凝土绝热温升值和温升速率的影响,同时还研究了所用胶凝材料的水化动力学。研究结果表明,提高混凝土初始入模温度将加速胶凝材料的水化,并缩短水化反应持续时间。这对低强度等级混凝土所用胶凝材料的水化程度影响不大,因而对其绝热温升值影响不显著;但却会明显降低高强度等级混凝土所用胶凝材料的水化程度,使其绝热温升值下降。掺加粉煤灰或改变混凝土的工作性也会影响混凝土的绝热温升值和温升速率。     

混凝土绝热温升公式的理论推导与试验研究

由固体热传导理论可知,混凝土绝热温升仅与水泥水化放热规律有关。根据笔者研究得出的水泥恒温水化放热规律及水泥水化放热行为的温度效应,可以预测任意温度条件下任一时刻水化放热总量。进而,从理论上推导出混凝土绝热温升表达式;然后,采用10 mm木胶板内衬100 mm聚苯乙烯泡沫板和3 mm胶合板模拟绝热状态,进行试验验证。最后,得出混凝土绝热温升表达式可以采用双曲函数和复合指数函数表达,而双曲函数在形式上要比复合指数函数简单,所以多数人倾向于采用双曲函数表达式。     

Beurteilung der Betonfestigkeit unter Nutzung der künstlichen Neuronalen Netze aufgrund zerstörungsfreier Untersuchungen

Determination of the Compressive Strength of Concrete using artificial Neural Network and non‐destructive Tests This paper deals with the neural identification of the compression strength of concrete on the basis of nondestructively determined parameters. A methodology for the neural identification of the compression strength of concrete has been developed. The obtained results show that Levenberga‐Marquardta artificial neural networks are highly suitable for assessing the compression strength of concrete. The results of practical verification of neural identification two reinforced concrete building structures are presented.     

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.     

大体积混凝土水化热温度场的数值计算

以考虑温度影响的混凝土水化放热为基础,通过试验获得混凝土的绝热温升数据,在此基础上建立三维有限元模型,对大体积混凝土足尺模型浇筑后60d内的温度场进行了计算,并与实测数据进行比较。结果表明,混凝土内部最大温升约为40℃,5~7d达到峰值;任意两点间的温度差不超过25℃;在竖直方向上,上下表面温度梯度最大,且出现在混凝土自身放热完毕并开始向环境中散热的初期。     

Modeling of bromate formation by ozonation of surface waters in drinking water treatment

The main objective of this paper is to try to develop statistically and chemically rational models for bromate formation by ozonation of clarified surface waters. The results presented here show that bromate formation by ozonation of natural waters in drinking water treatment is directly proportional to the "Ct" value ("Ctau" in this study). Moreover, this proportionality strongly depends on many parameters: increasing of pH, temperature and bromide level leading to an increase of bromate formation; ammonia and dissolved organic carbon concentrations causing a reverse effect. Taking into account limitation of theoretical modeling, we proposed to predict bromate formation by stochastic simulations (multi-linear regression and artificial neural networks methods) from 40 experiments (BrO(3)(-) vs. "Ctau") carried out with three sand filtered waters sampled on three different waterworks. With seven selected variables we used a simple architecture of neural networks, optimized by "neural connection" of SPSS Inc./Recognition Inc. The bromate modeling by artificial neural networks gives better result than multi-linear regression. The artificial neural networks model allowed us classifying variables by decreasing order of influence (for the studied cases in our variables scale): "Ctau", [N-NH(4)(+)], [Br(-)], pH, temperature, DOC, alkalinity.     

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.     

Analysis of durability of high performance concrete using artificial neural networks

This study aims to determine the influence of the content of water and cement, water–binder ratio, and the replacement of fly ash and silica fume on the durability of high performance concrete (HPC) by using artificial neural networks (ANNs). To achieve this, an ANNs model is developed to predict the durability of high performance concrete which is expressed in terms of chloride ions permeability in accordance with ASTM C1202-97 or AASHTO T277. The model is developed, trained and tested by using 86 data sets from experiments as well as previous researches. To verify the model, regression equations are carried out and compared with the trained neural network. The results indicate that the developed model is reliable and accurate. Based on the simulating durability model built using trained neural networks, the optimum cement content for designing HPC in terms of durability is in the range of 450–500 kg/m³. The results also revealed that the durability of concrete expressed in terms of total charge passed over a 6-h period can be significantly improved by using at least 20% fly ash to replace cement. Furthermore, it can be concluded that increasing silica fume results in reducing the chloride ions penetrability to a higher degree than fly ash. This study also illustrates how ANNs can be used to beneficially predict durability in terms of chloride ions permeability across a wide range of mix proportion parameters of HPC.     

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。     

神经网络法在混凝土强度研究中的应用

讨论了如何应用人工神经网络（ANN）的方法预测混凝土抗压强度,详细论述了采用BP算法建立混凝土抗压强度神经网络模型的过程,以及在活化剂作用下高掺量粉煤灰混凝土的强度效应,仿真结果表明,通过学习,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。     

高层剪力墙结构温度应力初探

从日照和水化热两方面考虑高层剪力墙结构温度的作用效应.通过热传导微分方程导出日照作用下非匀质材料的差分格式,用数值方法求解墙体的温度场分布;考虑一般情况下的水化热降温曲线,求得构件在水化热反应过程中温度应力的等效温差.综合考虑两者的温度变化,计算结构的最不利温度应力和组合值.对一个实际的剪力墙结构进行了温度应力全过程计算.     

Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks

Numerous attempts to use ultrasonic pulse velocity (UPV) as a measure of compressive strength of concrete has been made due to obvious advantages of non-destructive testing methods. The present study is conducted for prediction of compressive strength of concrete based on weight and UPV for two different concrete mixtures (namely M20 and M30) involving specimens of two different sizes and shapes as a result of need for rapid test method for predicting long-term compressive strength of concrete. The prediction is done using multiple regression analysis and artificial neural networks. A comparison between two methods depicts that artificial neural networks can be used to predict the compressive strength of concrete effectively. The results are plotted as experimentally evaluated compressive strength versus predicted strength through both methods of analysis.     

钢筋混凝土梁裂纹损伤分析

应用人工神经网络模型，对钢筋混凝土梁结构上面出现的裂纹损伤对于结构固有频率下降率的影响进行了诊断和预测研究，并与传统的诊断和预测结果进行了比较，结果表明，采用神经网络技术能够更好地反映结构损伤与特征参数之间的非线性特征。     

Prediction of fire resistance of concrete filled tubular steel columns using neural networks

A functional relationship between the fire resistance of a concrete filled steel column and the parameters which cause the fire resistance is represented using an artificial neural network. Experimental data obtained from previous laboratory fire tests are used for training the neural network model. The model predicted values are compared with actual test results. The results indicate that the model can predict the fire resistance with adequate accuracy required for practical design purpose. The developed neutral network can be used to predict the fire resistance of similar columns under fire by observing various factors influencing the resistance such as: (a) structural factors, (b) material factors, and (c) loading conditions. The structural engineer is required to provide the magnitude of these influencing factors as inputs to the neural network and the network will predict the fire resistance, based on the combined effect of these factors. This system can be used by structural engineers to predict the resistance of fire in similar concrete filled steel columns without conducting costly fire tests, by using the known parameters such as column dimensions, column height, and loading conditions.