三种神经网络在混凝土强度预测中的应用

通过建立改进的BP和RBF两种神经网络模型，对混凝土的强度进行预测，将预测值与常规BP神经网络模型预测结果进行了比较，研究表明，改进的BP和RBF的神经网络模型能够充分考虑影响混凝土强度的各种因素，在强度预测中具有广泛的应用前景。     

A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak

In blasting operation, the aim is to achieve proper fragmentation and to avoid undesirable events such as backbreak. Therefore, predicting rock fragmentation and backbreak is very important to arrive at a technically and economically successful outcome. Since many parameters affect the blasting results in a complicated mechanism, employment of robust methods such as artificial neural network may be very useful. In this regard, this paper attends to simultaneous prediction of rock fragmentation and backbreak in the blasting operation of Tehran Cement Company limestone mines in Iran. Back propagation neural network (BPNN) and radial basis function neural network (RBFNN) are adopted for the simulation. Also, regression analysis is performed between independent and dependent variables. For the BPNN modeling, a network with architecture 6-10-2 is found to be optimum whereas for the RBFNN, architecture 6-36-2 with spread factor of 0.79 provides maximum prediction aptitude. Performance comparison of the developed models is fulfilled using value account for (VAF), root mean square error (RMSE), determination coefficient (R²) and maximum relative error (MRE). As such, it is observed that the BPNN model is the most preferable model providing maximum accuracy and minimum error. Also, sensitivity analysis shows that inputs burden and stemming are the most effective parameters on the outputs fragmentation and backbreak, respectively. On the other hand, for both of the outputs, specific charge is the least effective parameter.     

A hybrid FDD strategy for local system of AHU based on artificial neural network and wavelet analysis

This paper presents a self-adaptive sensor fault detection and diagnosis (FDD) strategy for local system of air handing unit (AHU). This hybrid strategy consists of two stages. In the first stage, a fault detection model for the AHU control loop including two back-propagation neural network (BPNN) models is developed. BPNN models are trained by the normal operating data of system. Based on sensitive analysis for the first BPNN model, the second BPNN model is constructed in the same control loop. In the second stage, a fault diagnosis model is developed which combines wavelet analysis method with Elman neural network. The wavelet analysis is employed to process the measurement data by extracting the approximation coefficients of sensor measurement data. The Elman neural network is used to identify sensor faults. A new approach for increasing adaptability of sensor fault diagnosis is presented. This approach gains clustering information of the approximations coefficients by fuzzy c-means (FCM) algorithm. Based on cluster information of the approximation coefficients, the unknown sensor fault can be identified in the control loop. Simulation results in this paper show that this strategy can successfully detect and diagnose fixed biases and drifting fault of sensors for the local system of AHU.     

Prestressing wire breakage monitoring using sound event detection

Detecting prestressed wire breakage in concrete bridges is essential for ensuring safety and longevity and preventing catastrophic failures. This study proposes a novel approach for wire breakage detection using Mel-frequency cepstral coefficients (MFCCs) and back-propagation neural network (BPNN). Experimental data from two bridges in Italy were acquired to train and test the models. To overcome the limited availability of real-world training data, data augmentation techniques were employed to increase the data set size, enhancing the capability of the models and preventing over-fitting problems. The proposed method uses MFCCs to extract features from acoustic emission signals produced by wire breakage, which are then classified by the BPNN. The results show that the proposed method can detect and classify sound events effectively, demonstrating the promising potential of BPNN for real-time monitoring and diagnosis of bridges. The significance of this work lies in its contribution to improving bridge safety and preventing catastrophic failures. The combination of MFCCs and BPNN offers a new approach to wire breakage detection, while the use of real-world data and data augmentation techniques are significant contributions to overcoming the limited availability of training data. The proposed method has the potential to be a generalized and robust model for real-time monitoring of bridges, ultimately leading to safer and longer-lasting infrastructure.     

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.     

Neural networks for predicting properties of concretes with admixtures

Backpropagation neural networks were used to predict the strength and slump of ready mixed concrete and high strength concrete, in which chemical admixtures and/or mineral additives were used. Although various data transforms were tried, it was found that models based on raw data gave the best results. When non-dimensional ratios were used, arranging the ratios such that their changes resulted in corresponding changes in the output (e.g. increases in ratios to cause increases in output values) improved network performance. The neural network models also performed better than the multiple regression ones, especially in reducing the scatter of predictions. Problems associated with models trained on non-dimensional ratios were uncovered when sensitivity analyses were carried out. A rational approach was used for carrying out sensitivity analyses on these mix design problems by constraining the sum of input values. These analyses, using the raw data based model, showed that the modelling had picked up not only the fundamental domain rules governing concrete strength, but also some well-known second order effects.     

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.     

Estimating moment capacity of ferrocement members using self-evolving network

In this paper, an empirical model based on self-evolving neural network is proposed for predicting the flexural behavior of ferrocement elements. The model is meant to serve as a simple but reliable tool for estimating the moment capacity of ferrocement members. The proposed model is trained and validated using experimental data obtained from the literature. The data consists of information regarding flexural tests on ferrocement specimens which include moment capacity and cross-sectional dimensions of specimens, concrete cube compressive strength, tensile strength and volume fraction of wire mesh. Comparisons of predictions of the proposed models with experimental data indicated that the models are capable of accurately estimating the moment capacity of ferrocement members. The proposed models also make better predictions compared to methods such as the plastic analysis method and the mechanism approach. Further comparisons with other data mining techniques including the back-propagation network, the adaptive spline, and the Kriging regression models indicated that the proposed models are superior in terms prediction accuracy despite being much simpler models. The performance of the proposed models was also found to be comparable to the GEP-based surrogate model.     

Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses

The determination of deformation modulus of rock masses is one of the most difficult tasks in the field of rock mechanics. Due to the high cost and measurement difficulties of in situ tests in modulus determination, the predictive models using regression based statistical methods, back propagation neural networks (BPNN) and fuzzy systems are recently employed for the indirect estimation of the modulus. Among these methods, the BPNN has been reported to be very useful in modeling the rock material behavior, such as deformation modulus, by many researchers. Despite its extensive applications, design and structural optimization of BPNN are still done via a time-consuming reiterative trial-and-error approach. This research focuses on the efficiency of the genetic algorithm (GA) in design and optimizing the BPNN structure and its application to predict the deformation modulus of rock masses. GA is utilized to find the optimal number of neurons in hidden layer, learning rates and momentum coefficients of hidden and output layers of network. Then the result is compared with that of trial-and-error procedure. For the purpose, a database including 120 data sets was employed from four dam sites and power house locations in Iran. Taking advantages of performance criteria such as MSE, MAE, r, proved that the GA-ANN model gives superior predictions over the trial-and-error model.     

基于粒子群算法和广义回归神经网络的岩爆预测

 岩爆是岩石深部开挖中一种常见的工程地质灾害。为评价岩爆发生的可能性,提出一种基于粒子群算法和广义回归神经网络模型(PSO-GRNN模型)的岩爆预测方法。该方法利用已有岩爆数据,通过神经网络技术建立回归模型,采用粒子群算法对模型参数进行优化,减少人为因素对神经网络设计的影响。据此方法,在能量理论的基础上,选取洞壁围岩最大切向应力、岩石单轴抗压强度、抗拉强度和弹性能量指数作为主要影响因素,利用国内外26组已有工程数据建立岩爆预测的PSO-GRNN模型。通过对苍岭隧道和冬瓜山铜矿岩爆预测的工程实例分析验证该方法的可行性和适用性。所提方法可为类似工程的岩爆预测提供参考。     

多重群体遗传规划在高性能混凝土强度预测中的应用

针对影响高性能混凝土强度的主要因素作为输入因子,28 d抗压强度作为输出变量,应用遗传规划理论(GP)建立了高性能混凝土强度预测的非线性显式数学解析式模型。为了更好地保持进化过程中的遗传多样性,提高求解此问题的效率,提出了多重群体遗传规划理论。通过实测数据进行验证,并分别与线性回归模型和神经网络模型相比较,结果表明,多重群体遗传规划(MGGP)模型具有更高的拟合精度和更好的预测效果,在高性能混凝土强度预测方面有很强的实用价值。     

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。     

基于神经网络的混凝土强度预测

在传统预测混凝土强度的基础上,提出一种基于人工智能的新的预测方法,建立了两种神经网络模型:BP神经网络和RBF神经网络,实现了从新拌混凝土成分及其特性到硬化后混凝土强度之间的复杂的非线性映射。通过对试验数据的学习,网络结构可以早期预测混凝土28d抗压强度。另外,还利用BP神经网络模拟分析了混凝土成分质和量的变化对抗压强度的影响,其结果符合已知的经典混凝土强度变化规律,表明神经网络模型具有较高的精度和较强的泛化能力。     

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.     

Effectiveness of predicting tunneling-induced ground settlements using machine learning methods with small datasets

Prediction of tunneling-induced ground settlements is an essential task, particularly for tunneling in urban settings. Ground settlements should be limited within a tolerable threshold to avoid damages to aboveground structures. Machine learning (ML) methods are becoming popular in many fields, including tunneling and underground excavations, as a powerful learning and predicting technique. However, the available datasets collected from a tunneling project are usually small from the perspective of applying ML methods. Can ML algorithms effectively predict tunneling-induced ground settlements when the available datasets are small? In this study, seven ML methods are utilized to predict tunneling-induced ground settlement using 14 contributing factors measured before or during tunnel excavation. These methods include multiple linear regression (MLR), decision tree (DT), random forest (RF), gradient boosting (GB), support vector regression (SVR), back-propagation neural network (BPNN), and permutation importance-based BPNN (PI-BPNN) models. All methods except BPNN and PI-BPNN are shallow-structure ML methods. The effectiveness of these seven ML approaches on small datasets is evaluated using model accuracy and stability. The model accuracy is measured by the coefficient of determination (R²) of training and testing datasets, and the stability of a learning algorithm indicates robust predictive performance. Also, the quantile error (QE) criterion is introduced to assess model predictive performance considering underpredictions and overpredictions. Our study reveals that the RF algorithm outperforms all the other models with the highest model prediction accuracy (0.9) and stability (3.02 × 10^?27). Deep-structure ML models do not perform well for small datasets with relatively low model accuracy (0.59) and stability (5.76). The PI-BPNN architecture is proposed and designed for small datasets, showing better performance than typical BPNN. Six important contributing factors of ground settlements are identified, including tunnel depth, the distance between tunnel face and surface monitoring points (DTM), weighted average soil compressibility modulus (ACM), grouting pressure, penetrating rate and thrust force.     

Prediction of the compressive strength of no-slump concrete: A comparative study of regression,neural network and ANFIS models

No-slump concrete (NSC) is defined as concrete having either very low or zero slump that traditionally used for prefabrication purposes. The sensitivity of NSC to its constituents, mixture proportion, compaction, etc., enforce some difficulties in the prediction of the compressive strength. In this paper, by considering concrete constituents as input variables, several regression, neural networks (NNT) and ANFIS models are constructed, trained and tested to predict the 28-days compressive strength of no-slump concrete (28-CSNSC). Comparing the results indicate that NNT and ANFIS models are more feasible in predicting the 28-CSNSC than the proposed traditional regression models.     

BP网络预测再生混凝土性能研究

利用BP神经网络模型,对再生混凝土强度及工作性能的预测方法进行了探讨。根据再生混凝土的特殊性,找出影响其强度和坍落度、保水性的主要因素,对试验中通过主观观察得到的数据进行量化,在此基础上建立预测其强度和工作性能的BP神经网络模型,针对所建模型,输入一定量的实测数据样本,对网络进行训练。为了验证训练好的网络的推广性能,用预留的一组试验数据进行仿真训练的效率和误差及仿真计算的结果表明,采用优化的BP网络模型及合适的样本参数训练出的预测系统对再生混凝土的强度及工作性能进行预测是可行的。     

型钢高强混凝土柱抗剪承载力RBF神经网络预测方法及参数分析

在已有文献有关试验的基础上,引入径向基(RBF)网络理论,提出了型钢高强混凝土柱抗剪承载力RBF神经网络预测方法.以混凝土强度等级、剪跨比、轴压比和配箍率为输入参数,混凝土柱的抗剪承载力为输出参数,建立精确RBF神经网络模型,以多组不同试验数据分别作为训练样本和检验样本,对网络进行训练和检验,并把仿真结果与采用非线性最小二乘法拟合公式的计算结果进行了比较.在文中所提方法的基础上,对型钢高强混凝土的抗剪承载力进行的参数分析结果表明,用训练成熟的RBF网络进行仿真,避免了诸多人为因素的影响,大大提高了结果的精度,使计算更加准确、高效.参数分析还表明,型钢高强混凝土柱的抗剪承载力随着混凝土强度、轴压比和配箍率的增大而增大,但随着剪跨比的增大而减小,并且剪跨比对柱的抗剪能力的影响最大,轴压比、混凝土的强度和配箍率则趋于同等重要影响程度.     

Concrete corrosion in wastewater systems: Prediction and sensitivity analysis using advanced extreme learning machine

The implementation of novel machine learning models can contribute remarkably to simulating the degradation of concrete due to environmental factors. This study considers the sulfuric acid corrosive factor in wastewater systems to simulate concrete mass loss using five machine learning models. The models include three different types of extreme learning machines, including the standard, online sequential, and kernel extreme learning machines, in addition to the artificial neural network, classification and regression tree model, and statistical multiple linear regression model. The reported values of concrete mass loss for six different types of concrete are the target values of the machine learning models. The input variability was assessed based on two scenarios prior to the application of the predictive models. For the first assessment, the machine learning models were developed using all the available cement and concrete mixture input variables; the second assessment was conducted based on the gamma test approach, which is a sensitivity analysis technique. Subsequently, the sensitivity analysis of the most effective parameters for concrete corrosion was tested using three different approaches. The adopted methodology attained optimistic and reliable modeling results. The online sequential extreme learning machine model demonstrated superior performance over the other investigated models in predicting the concrete mass loss of different types of concrete.     

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.