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.     

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.     

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 blast-induced ground vibration using artificial neural network

An attempt has been made to evaluate and predict the blast-induced ground vibration and frequency by incorporating rock properties, blast design and explosive parameters using the artificial neural network (ANN) technique. A three-layer, feed-forward back-propagation neural network having 15 hidden neurons, 10 input parameters and two output parameters were trained using 154 experimental and monitored blast records from one of the major producing surface coal mines in India. Twenty new blast data sets were used for the validation and comparison of the peak particle velocity (PPV) and frequency by ANN and other predictors. To develop more confidence in the proposed method, same data sets have also been used for the prediction of PPV by commonly used vibration predictors as well as by multivariate regression analysis (MVRA). Results were compared based on correlation and mean absolute error (MAE) between monitored and predicted values of PPV and frequency.     

Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems

Bulletin of Engineering Geology and the Environment - A realistic analysis of rock deformation in response to any change in stresses is heavily dependent on the reliable determination of the rock...     

基于模糊逻辑与神经网络的高层结构半主动控制

根据剪切型结构动力特性提出AVSD开关控制律。把开关控制律作为专家知识,利用模糊逻辑转化为模糊控制规则。为了进行时滞和结构动力特性时变的控制补偿,采用神经网络在线自适应跟踪辨识方法进行在线辨识和响应预测。最后以某框架结构为例进行仿真分析,结果表明这一方法控制效果及鲁棒性好、实际应用方便可靠。     

Prediction of bed load sediments using different artificial neural network models

Modeling and prediction of bed loads is an important but difficult issue in river engineering. The introduced empirical equations due to restricted applicability even in similar conditions provide different accuracies with each other and measured data. In this paper, three different artificial neural networks (ANNs) including multilayer percepterons, radial based function (RBF), and generalized feed forward neural network using five dominant parameters of bed load transport formulas for the Main Fork Red River in Idaho-USA were developed. The optimum models were found through 102 data sets of flow discharge, flow velocity, water surface slopes, flow depth, and mean grain size. The deficiency of empirical equations for this river by conducted comparison between measured and predicted values was approved where the ANN models presented more consistence and closer estimation to observed data. The coefficient of determination between measured and predicted values for empirical equations varied from 0.10 to 0.21 against the 0.93 to 0.98 in ANN models. The accuracy performance of all models was evaluated and interpreted using different statistical error criteria, analytical graphs and confusion matrixes. Although the ANN models predicted compatible outputs but the RBF with 79% correct classification rate corresponding to 0.191 network error was outperform than others.     

Service life prediction of fly ash concrete using an artificial neural network

Carbonation is one of the most aggressive phenomena affecting reinforced concrete structures and causing their degradation over time. Once reinforcement is altered by carbonation, the structure will no longer fulfill service requirements. For this purpose, the present work estimates the lifetime of fly ash concrete by developing a carbonation depth prediction model that uses an artificial neural network technique. A collection of 300 data points was made from experimental results available in the published literature. Backpropagation training of a three-layer perceptron was selected for the calculation of weights and biases of the network to reach the desired performance. Six parameters affecting carbonation were used as input neurons: binder content, fly ash substitution rate, water/binder ratio, CO₂ concentration, relative humidity, and concrete age. Moreover, experimental validation carried out for the developed model shows that the artificial neural network has strong potential as a feasible tool to accurately predict the carbonation depth of fly ash concrete. Finally, a mathematical formula is proposed that can be used to successfully estimate the service life of fly ash concrete.     

Prediction of roadheader performance by artificial neural network

Performance prediction of the roadheaders is one of the main subjects in determining the economics of the underground excavation projects. During the last decades, researchers have focused on developing performance prediction models for roadheaders. In the first stage of this study, the performance of a roadheader used in Kucuksu sewage tunnel (Istanbul) was recorded in detail and the instantaneous cutting rate (ICR) of the machine was determined. The uniaxial compressive strength (UCS) and rock quality designation (RQD) are used as input parameters in previously developed empirical models in order to point out the efficiency of these models, and the relationships between measured and predicted ICR for different encountered formations. In the second stage of the study, Artificial Neural Network (ANN) technique is used for predicting of the ICR of the roadheader. A data set including UCS, RQD, and measured ICR are established. It is traced that a neural network with two inputs (RQD and UCS) and one hidden layer can be sufficient for the estimation of ICR. In addition, it is determined that increase in number of neurons in hidden layer has positive optimizing on the performance of the ANN and a hidden layer larger than 10 neurons does not have a significant effect on optimizing the performance of the neural network. Furthermore, probability of memorizing is being recognized in this situation. Based on this study, it is concluded that the prediction capacity of ANN is better than the empirical models developed previously.     

Prediction of roadheaders' performance using artificial neural network approaches (MLP and KOSFM)

Application of mechanical excavators is one of the most commonly used excavation methods because it can bring the project more productivity, accuracy and safety. Among the mechanical excavators, roadheaders are mechanical miners which have been extensively used in tunneling, mining and civil industries. Performance prediction is an important issue for successful roadheader application and generally deals with machine selection, production rate and bit consumption. The main aim of this research is to investigate the cutting performance(instantaneous cutting rates(ICRs)) of medium-duty roadheaders by using artificial neural network(ANN) approach. There are different categories for ANNs, but based on training algorithm there are two main kinds: supervised and unsupervised. The multi-layer perceptron(MLP) and Kohonen self-organizing feature map(KSOFM) are the most widely used neural networks for supervised and unsupervised ones, respectively. For gaining this goal, a database was primarily provided from roadheaders' performance and geomechanical characteristics of rock formations in tunnels and drift galleries in Tabas coal mine, the largest and the only fullymechanized coal mine in Iran. Then the database was analyzed in order to yield the most important factor for ICR by using relatively important factor in which Garson equation was utilized. The MLP network was trained by 3 input parameters including rock mass properties, rock quality designation(RQD), intact rock properties such as uniaxial compressive strength(UCS) and Brazilian tensile strength(BTS), and one output parameter(ICR). In order to have more validation on MLP outputs, KSOFM visualization was applied. The mean square error(MSE) and regression coefficient(R) of MLP were found to be 5.49 and 0.97, respectively. Moreover, KSOFM network has a map size of 8 5 and final quantization and topographic errors were 0.383 and 0.032, respectively. The results show that MLP neural networks have a strong capability to predict and evaluate the performance of medium-duty roadheaders in coal measure rocks. Furthermore, it is concluded that KSOFM neural network is an efficient way for understanding system behavior and knowledge extraction. Finally, it is indicated that UCS has more influence on ICR by applying the best trained MLP network weights in Garson equation which is also confirmed by KSOFM.     

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.     

基于人工神经网络的混凝土抗冻性预测

简要介绍了MATLAB人工神经网络,阐述了人工神经网络预测模型的基本思想和模型特点。随着神经网络技术的不断完善和发展,人工神经网络广泛地用于土木工程的各个领域,可针对混凝土冻融与各种因素之间的相互关系,并且随着数据的不断积累,可在新样本基础上进行自学,形成较完整的评估预测系统。针对混凝土冻融后的相对动弹性模量的变化,应用BP网络建立了评估混凝土冻融后的性能的人工神经元网络模型,较好地解决了多影响因子的非线性映射问题,但真正用于工程实践还有许多问题需要进一步的研究解决。     

橡胶混凝土工作性及力学性能研究

本文主要研究将改性与未改性的橡胶颗粒以不同掺量分别等体积代替砂子成型混凝土。测试其对混凝土工作性及物理力学性能的影响。     

Prediction of effective thermal conductivity of moist porous materials using artificial neural network approach

An artificial neural networks (ANNs) approach is presented for the prediction of effective thermal conductivity of porous systems filled with different liquids. ANN models are based on feedforward backpropagation network with training functions: Levenberg–Marquardt (LM), conjugate gradient with Fletcher–Reeves updates (CGF), one-step secant (OSS), conjugates gradient with Powell–Beale restarts (CGB), Broyden, Fletcher, Goldfrab and Shanno (BFGS) quasi-Newton (BFG), conjugates gradient with Polak–Ribiere updates (CGP). Training algorithm for neurons and hidden layers for different feedforward backpropagation networks at the uniform threshold function TANSIG-PURELIN are used and run for 1000 epochs. The complex structure encountered in moist porous materials, along with the differences in thermal conductivity of the constituents makes it difficult to predict the effective thermal conductivity accurately. For this reason, artificial neural networks (ANNs) have been utilized in this field. The resultant predictions of effective thermal conductivity (ETC) of moist porous materials by the different models of ANN agree well with the available experimental data.     

Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks

Petrographic features of a rock are intrinsic properties, which control the mechanical behaviour of the rock mass at the fundamental level. This paper deals with the application of neural networks for the prediction of uniaxial compressive strength, tensile strength and axial point load strength simultaneously from the mineral composition and textural properties. Statistical analysis has also been conducted for prediction of the same strength properties and compared with the predicted values by neural networks to investigate the authenticity of this approach. The network was trained to predict the uniaxial compressive strength, tensile strength and axial point load strength from the mineralogical composition, grain size, aspect ratio, form factor, area weighting and orientation of foliation planes (planes of weakness). A data set having 112 test results of the four schistose rocks were used to train the network with the back-propagation learning algorithm. Another data set of 28 test results of the four schistose rocks were used to validate the generalization and prediction capabilities of the network.     

BP人工神经网络在混凝土耐久性评价上的应用

混凝土耐久性评价与预测一直是学术界与工程界的研究热点,常规的预测模型主要基于某几项指标,形式因个人的理解不同而各异.一种仿生模型--人工神经网络则能很好地解决这个难题,试验尝试用BP人工神经网络对多种配后比的混凝土进行耐久性评价与预测,结果表明此模型的可靠度很高,可以用以优化混凝土的配合比设计.     

基于人工神经网络的混凝土抗渗性能预测

在进行了正交试验的基础上,采用人工神经网络方法,建立混凝土的氯离子扩散系数与混凝土配比六个参数之间的非线性映射关系,研究各个参数对混凝土抗渗性能的影响,该研究成果可以减少混凝土试配次数,节约大量的人力、物力和时间,为高性能混凝土的研究发展奠定了基础。     

基于人工神经网络的回弹-拔出综合法检测混凝土强度的研究

在试验研究的基础上,建立了混凝土强度回弹-拔出综合法检测的人工神经网络模型.探索并应用了神经网络的改进算法,其中包括附加冲量法、自适应学习算法及S型函数输出限幅算法等,以保证建立的神经网络的快速有效.与传统的回归算法相比,采用人工神经网络模型推测出的混凝土强度具有更高的精度.     

橡胶粉改性水泥混凝土引气性能试验分析的研究

通过利用细度分别为20、40、60目橡胶粉颗粒以0、1.0%、2.0%、3.5%、5.0%、6.0%替代同质量砂配制成的橡胶粉水泥混凝土分别进行表观密度及新拌橡胶粉水泥混凝土含气量试验分析,试验结果表明当橡胶粉替代量小于3.5%时,橡胶粉的细度对橡胶粉水泥混凝土表观密度和含气量变化影响较小;当橡胶粉替代量大于或等于3....     

基于CPTu数据的黏性土不排水抗剪强度#br# 神经网络预测

利用人工神经网络模型,建立基于孔压静力触探（CPTu）现场测试数据的黏性土不排水抗剪强度的预测方法。为建立和验证人工神经网络模型,在3个场地开展CPTu和十字板剪切现场测试,共取得33个测孔的CPTu试验数据和相对应的不排水抗剪强度实测值。通过对比分析不同输入向量、不同网络隐层数、不同神经元数及不同改进算法对人工神经网络模型性能的影响,确定人工神经网络模型的具体形式。通过对训练组数据开展机器学习,所建立的人工神经网络模型能够有效地基于CPTu获得的端阻力和孔隙水压力现场测试数据对黏土不排水抗剪强度进行预测,预测结果与十字板剪切试验实测结果非常接近。与传统用于估算不排水强度的经验关系相比,采用人工神经网络模型预测结果与实测结果相关性显著提高、误差明显降低。