Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers

In this research, a new wavelet artificial neural network (WANN) model was proposed for daily suspended sediment load (SSL) prediction in rivers. In the developed model, wavelet analysis was linked to an artificial neural network (ANN). For this purpose, daily observed time series of river discharge (Q) and SSL in Yadkin River at Yadkin College, NC station in the USA were decomposed to some sub-time series at different levels by wavelet analysis. Then, these sub-time series were imposed to the ANN technique for SSL time series modeling. To evaluate the model accuracy, the proposed model was compared with ANN, multi linear regression (MLR), and conventional sediment rating curve (SRC) models. The comparison of prediction accuracy of the models illustrated that the WANN was the most accurate model in SSL prediction. Results presented that the WANN model could satisfactorily simulate hysteresis phenomenon, acceptably estimate cumulative SSL, and reasonably predict high SSL values.     

Prediction of elastic modulus of normal and high strength concrete by artificial neural networks

In the present paper, application of artificial neural networks (ANNs) to predict elastic modulus of both normal and high strength concrete is investigated. The paper aims to show a possible applicability of ANN to predict the elastic modulus of both high and normal strength concrete. An ANN model is built, trained and tested using the available test data gathered from the literature. The ANN model is found to predict elastic modulus of concrete well within the ranges of the input parameters considered. The average value of the experimental elastic modulus to the predicted elastic modulus ratio is found to be 1.00. The elastic modulus results predicted by ANN are also compared to those obtained using empirical results of the buildings codes and various models. These comparisons show that ANNs have strong potential as a feasible tool for predicting elastic modulus of both normal and high strength within the range of input parameters considered.     

The effects of unpaved roads on suspended sediment concentration at varying spatial scales – a case study from Southern Brazil

Unpaved roads may induce adverse effects on downstream water resources by increasing suspended sediment concentration (SSC). This study documents the localized impacts on stream SSC of six unpaved road–stream crossings in the rural Guabiroba River Catchment, in southern Brazil. Results demonstrated that SSC values downstream of road–stream crossings was between 3.5 and 10 times higher than upstream SSC at all third‐ and fourth‐order stream locations. However, downstream values were statistically undistinguishable from those collected upstream of road–stream crossings at fifth‐order sampling sites. These findings suggest that localized road effects on stream SSCs are scale‐dependent in that these are important for low‐order headwater streams yet undetectable for their higher order counterparts. Findings point to the importance of low‐order stream crossings in increasing SSC and the need to further explore the role of unpaved roads as agents of water quality degradation in agriculturally active rural settings.     

Estimation of suspended sediment concentrations using Terra MODIS: An example from the Lower Yangtze River, China

Traditional measurements of suspended sediment concentrations (SSC) through in-situ sampling in rivers are expensive and time-consuming to perform. Thus, these methods cannot provide continuous SSC records. Although remote sensing has been used for SSC estimation, little research has been undertaken on inland rivers, especially for highly turbid rivers like the Yangtze. Previous studies have proposed Landsat TM/ETM+ Band 4 as a spectral SSC indicator for the Yangtze, but its limitation on temporal resolution is insufficient for the study of dynamic changes of sediment. This paper presents a method of estimating SSC of the Yangtze at Jiujiang using time-series satellite data of high temporal resolution Terra MODIS. It was found that differences in water reflectance between Band 2 and Band 5 could provide relatively accurate SSC estimation even when in-situ atmospheric conditions were unknown. After cross-validation, mean absolute relative error (ARE) and relative root mean square error (RRMSE) were relatively low (i.e., 25.5% and 36.5%, respectively). This empirical relationship was successfully applied to the estimation of SSC at Datong in the Lower Yangtze River, although the SSC values were generally underestimated. This study suggests that Terra MODIS could be used to estimate SSC in large turbid rivers, although some influencing factors require further study to improve the accuracy of SSC estimation.     

A Comparative Analysis of Two Artificial Neural Networks Using Pavement Performance Prediction

The management of pavements requires the ongoing allocation of substantial manpower and capital resources by the responsible agencies. These agencies ultimately report to the executive and legislative branches of government, which require justification and proof of the efficacy of these expenditures. This and the need for improved engineering technical feedback have encouraged the development of pavement management systems (PMS). One goal of a PMS is to provide decision makers at all levels with optimal resource-allocation strategies. This requires evaluation of alternatives over an analysis period based on predicted values of pavement performance. This necessitates more reliable pavement performance prediction models. Traditional modeling uses multiple regression techniques to predict pavement performance from traffic, time, and pavement distress or various combinations of these factors. Within the last 10 years, new modeling techniques, including artificial neural networks (ANNs), have been applied to transportation problems. The ANNs examined usually have been of a single type called a dot product ANN. This paper examines a different type called the quadratic function ANN and compares the results to the dot product ANN. The quadratic function ANN is a generalized adaptive, feedforward neural network that combines supervised and self-organizing learning. Models were developed to predict roughness using both types of ANN on the same data samples and the results compared. The data samples were drawn from the Kansas Department of Transportation's PMS database. The results indicate a significant improvement in the use of the self-organizing quadratic function ANNs and lead to recommendations for specific areas of additional research.     

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.     

An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine,Iran简

Blast-induced ground vibration is one of the inevitable outcomes of blasting in mining projects and may cause substantial damage to rock mass as well as nearby structures and human beings. In this paper, an attempt has been made to present an application of artificial neural network (ANN) to predict the blast-induced ground vibration of the Gol-E-Gohar (GEG) iron mine, Iran. A four-layer feed-forward back propagation multi-layer perceptron (MLP) was used and trained with Levenberg–Marquardt algorithm. To construct ANN models, the maximum charge per delay, distance from blasting face to monitoring point, stemming and hole depth were taken as inputs, whereas peak particle velocity (PPV) was considered as an output parameter. A database consisting of 69 data sets recorded at strategic and vulnerable locations of GEG iron mine was used to train and test the generalization capability of ANN models. Coefficient of determination (R²) and mean square error (MSE) were chosen as the indicators of the performance of the networks. A network with architecture 4-11-5-1 and R² of 0.957 and MSE of 0.000722 was found to be optimum. To demonstrate the supremacy of ANN approach, the same 69 data sets were used for the prediction of PPV with four common empirical models as well as multiple linear regression (MLR) analysis. The results revealed that the proposed ANN approach performs better than empirical and MLR models.     

Neural networks for modelling ultimate pure bending of steel circular tubes

The behaviour of steel circular tubes under pure bending is complex and highly nonlinear. The literature has a number of solutions to predict the response of steel circular tubes under pure bending; however, most of these solutions are complicated and difficult to use in routine design practice. In this paper, the feasibility of using artificial neural networks (ANNs) for developing more accurate and simple-to-use models for predicting the ultimate pure bending of steel circular tubes is investigated. The data used to calibrate and validate the ANN models are obtained from the literature and comprise a series of 49 pure bending tests conducted on fabricated steel circular tubes and 55 tests carried out on cold-formed tubes. Multilayer feed-forward neural networks that are trained with the back-propagation algorithm are constructed using four design parameters (i.e. tube thickness, tube diameter, yield strength of steel and modulus of elasticity of steel) as network inputs and the ultimate pure bending as the only output. A sensitivity analysis is conducted on the ANN models to investigate the generalization ability (robustness) of the developed models, and predictions from the ANN models are compared with those obtained from most available codes and standards. To facilitate the use of the developed ANN models, they are translated into design equations suitable for spreadsheet programming or hand calculations. The results indicate that ANNs are capable of predicting the ultimate bending capacity of steel circular tubes with a high degree of accuracy, and outperform most available codes and standards.     

Development of a neural-based forecasting tool to classify recreational water quality using fecal indicator organisms

Users of recreational waters may be exposed to elevated pathogen levels through various point/non-point sources. Typical daily notifications rely on microbial analysis of indicator organisms (e.g., Escherichia coli) that require 18, or more, hours to provide an adequate response. Modeling approaches, such as multivariate linear regression (MLR) and artificial neural networks (ANN), have been utilized to provide quick predictions of microbial concentrations for classification purposes, but generally suffer from high false negative rates. This study introduces the use of learning vector quantization (LVQ) - a direct classification approach - for comparison with MLR and ANN approaches and integrates input selection for model development with respect to primary and secondary water quality standards within the Charles River Basin (Massachusetts, USA) using meteorologic, hydrologic, and microbial explanatory variables. Integrating input selection into model development showed that discharge variables were the most important explanatory variables while antecedent rainfall and time since previous events were also important. With respect to classification, all three models adequately represented the non-violated samples (>90%). The MLR approach had the highest false negative rates associated with classifying violated samples (41-62% vs 13-43% (ANN) and <16% (LVQ)) when using five or more explanatory variables. The ANN performance was more similar to LVQ when a larger number of explanatory variables were utilized, but the ANN performance degraded toward MLR performance as explanatory variables were removed. Overall, the use of LVQ as a direct classifier provided the best overall classification ability with respect to violated/non-violated samples for both standards.     

非线性局部剪力交互作用下的开裂组合梁长期变形神经网络研究

结合有限元分析和人工神经网络,提出一种新的思路,研究简支组合梁的短期和长期变形。本文建立两个神经网络模型,采用相关论文中有限元模型的结果进行样本训练。有限元模型考虑了抗剪连接件的非线性荷载-滑移关系,以及蠕变、收缩和混凝土板的裂缝。而对没开裂的混凝土只考虑了蠕变、收缩的影响。为训练及验证两个神经网络模型,建立了一个包括不同设计参数的大数据库。研究发现,两个神经网络模型均能预测组合梁的变形。因此,神经网络模型可用以评估非几何设计参数对简支组合梁的短、长期变形影响。最后,根据AISC规范和欧洲规范4方法计算简支组合梁的短、长期变形,并与有限元模型结果进行比较。结果表明,与有限元方法相比,AISC方法低估了短期变形而高估了长期变形。     

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.     

Estimation of local extreme suspended sediment concentrations in California Rivers

The total amount of suspended sediment load carried by a stream during a year is usually transported during one or several extreme events related to high river flow and intense rainfall, leading to very high suspended sediment concentrations (SSCs). In this study quantiles of SSC derived from annual maximums and the 99th percentile of SSC series are considered to be estimated locally in a site-specific approach using regional information. Analyses of relationships between physiographic characteristics and the selected indicators were undertaken using the localities of 5-km radius draining of each sampling site. Multiple regression models were built to test the regional estimation for these indicators of suspended sediment transport. To assess the accuracy of the estimates, a Jack-Knife re-sampling procedure was used to compute the relative bias and root mean square error of the models. Results show that for the 19 stations considered in California, the extreme SSCs can be estimated with 40-60% uncertainty, depending on the presence of flow regulation in the basin. This modelling approach is likely to prove functional in other Mediterranean climate watersheds since they appear useful in California, where geologic, climatic, physiographic, and land-use conditions are highly variable.     

Long-term deflection of cracked composite beams with nonlinear partial shear interaction — A study using neural networks

A new study of the short- and long-term deflections of simply-supported composite beams using finite element analysis and artificial neural networks (ANNs) is presented. In this study, two ANN models are developed and trained using the results of a finite element model developed by the authors in a companion paper. The finite element model accounted for the nonlinear load–slip relationship of shear connectors as well as the creep, shrinkage, and cracking of concrete slabs. The effects of creep and shrinkage of the concrete slab are considered only for non-cracked concrete. A large database representing a wide range of different design parameters was constructed for the purpose of training and verifying the two ANN models. It was found that the two ANN models were capable of predicting deflections of composite beams not used as part of the training process. The ANN models were then used to evaluate the effects of non-geometric design variables on the short- and long-term deflections of simply-supported composite beams. Finally, the short- and long-term deflections computed based on the approaches given in the AISC specification and Eurocode 4 were assessed using the results of the finite element model. It was found that the AISC approach underestimates short-term deflections and overestimate long-term deflections when compared with the results of the finite element method.     

Prediction of bearing capacity of circular footings on soft clay stabilized with granular soil

The shortage of available and suitable construction sites in city centres has led to the increased use of problematic areas, where the bearing capacity of the underlying deposits is very low. The reinforcement of these problematic soils with granular fill layers is one of the soil improvement techniques that are widely used. Problematic soil behaviour can be improved by totally or partially replacing the inadequate soils with layers of compacted granular fill. The study presented herein describes the use of artificial neural networks (ANNs), and the multi-linear regression model (MLR) to predict the bearing capacity of circular shallow footings supported by layers of compacted granular fill over natural clay soil. The data used in running the network models have been obtained from an extensive series of field tests, including large-scale footing diameters. The field tests were performed using seven different footing diameters, up to 0.90 m, and three different granular fill layer thicknesses. The results indicate that the use of granular fill layers over natural clay soil has a considerable effect on the bearing capacity characteristics and that the ANN model serves as a simple and reliable tool for predicting the bearing capacity of circular footings in stabilized natural clay soil.     

Application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results

Rolling dynamic compaction (RDC), which involves the towing of a noncircular module, is now widespread and accepted among many other soil compaction methods. However, to date, there is no accurate method for reliable prediction of the densification of soil and the extent of ground improvement by means of RDC. This study presents the application of artificial neural networks (ANNs) for a priori prediction of the effectiveness of RDC. The models are trained with in situ dynamic cone penetration (DCP) test data obtained from previous civil projects associated with the 4-sided impact roller. The predictions from the ANN models are in good agreement with the measured field data, as indicated by the model correlation coefficient of approximately 0.8. It is concluded that the ANN models developed in this study can be successfully employed to provide more accurate prediction of the performance of the RDC on a range of soil types.     

Modelling optimal risk allocation in PPP projects using artificial neural networks

This paper aims to establish, train, validate, and test artificial neural network (ANN) models for modelling risk allocation decision-making process in public-private partnership (PPP) projects, mainly drawing upon transaction cost economics. An industry-wide questionnaire survey was conducted to examine the risk allocation practice in PPP projects and collect the data for training the ANN models. The training and evaluation results, when compared with those of using traditional MLR modelling technique, show that the ANN models are satisfactory for modelling risk allocation decision-making process. The empirical evidence further verifies that it is appropriate to utilize transaction cost economics to interpret risk allocation decision-making process. It is recommended that, in addition to partners' risk management mechanism maturity level, decision-makers, both from public and private sectors, should also seriously consider influential factors including partner's risk management routines, partners' cooperation history, partners' risk management commitment, and risk management environmental uncertainty. All these factors influence the formation of optimal risk allocation strategies, either by their individual or interacting effects.     

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。     

Crack detection in beam-like structures

Sensibility analysis of experimentally measured frequencies as a criterion for crack detection has been extensively used in the last decades due to its simplicity. However the inverse problem of the crack parameters (location and depth) determination is not straightforward. An efficient numerical technique is necessary to obtain significant results. Two approaches are herein presented: The solution of the inverse problem with a power series technique (PST) and the use of artificial neural networks (ANNs). Cracks in a cantilever Bernoulli–Euler (BE) beam and a rotating beam are detected by means of an algorithm that solves the governing vibration problem of the beam with the PST. The ANNs technique does not need a previous model, but a training set of data is required. It is applied to the crack detection in the cantilever beam with a transverse crack. The first methodology is very simple and straightforward, though no optimization is included. It yields relative small errors in both the location and depth detection. When using one network for the detection of the two parameters, the ANNs behave adequately. However better results are found when one ANN is used for each parameter. Finally, a combination between the two techniques is suggested.     

基于ANN的岩土体热阻系数预测模型研究

为准确预测土体热阻系数,通过室内热探针测试与数据分析,简要分析了含水量、干密度、矿物成分和颗粒形态等因素对土体热传导特性的影响,利用人工神经网络(ANN)技术,建立了计算土体热阻系数的预测模型,并与传统经验关系模型进行对比,明确所提计算模型的可靠性与优越性.结果表明:土体传热性能受众多因素影响,其热阻系数难以准确估算,基于ANN的计算模型可以较好地解决这一问题;以含水量和干密度为输入参数的单个模型适用于特定类型土体,而4个输入参数(含水量、干密度、黏粒含量和石英含量)的广义模型不受此限制,增加相关输入参数可有效保证模型计算结果的精确度;单个模型和广义模型的计算结果与实测结果吻合良好,预测能力均显著优于传统经验关系模型;对于工程性质差异显著、沉积环境复杂的不同类型土体,建议优先选用广义模型来估算其热阻系数.