Neural network and neuro-fuzzy assessments for scour depth around bridge piers

The mechanism of flow around a pier structure is so complicated that it is difficult to establish a general empirical model to provide accurate estimation for scour. Interestingly, each of the proposed empirical formula yields good results for a particular data set. Hence, in this study, alternative approaches, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS), are proposed to estimate the equilibrium and time-dependent scour depth with numerous reliable data base. Two ANN models, multi-layer perception using back-propagation algorithm (MLP/BP) and radial basis using orthogonal least-squares algorithm (RBF/OLS), were used. The equilibrium scour depth was modeled as a function of five variables; flow depth, mean velocity, critical flow velocity, mean grain diameter and pier diameter. The time variation of scour depth was also modeled in terms of equilibrium scour depth, equilibrium scour time, scour time, mean flow velocity and critical flow velocity. The training and testing data are selected from the experimental data of several valuable references. Numerical tests indicate that MLP/BP model provide a better prediction of scour depth than RBF/OLS and ANFIS models as well as the previous empirical approaches. Finally, sensitivity analysis shows that pier diameter has a greater influence on equilibrium scour depth than the other independent parameters.     

Prediction of local scour around bridge piers using the ANFIS method

Local scour around bridge piers is a complicated physical process and involves highly three-dimensional flows. Thus, the scour depth, which is directly related to the safety of a bridge, cannot be given in the form of the exact relationship of dependent variables via an analytical method. This paper proposes the use of the adaptive neuro-fuzzy inference system (ANFIS) method for predicting the scour depth around a bridge pier. Five variables including mean velocity, flow depth, size of sediment particles, critical velocity for particles’ initiation of motion, and pier width were used for the scour depth. For comparison, predictions by the artificial neural network (ANN) model were also provided. Both the ANN model and ANFIS method were trained and validated. The findings indicate that the modeling with dimensional variables yields better predictions than when normalized variables are used. The ANN model was applied to a field-scale dataset. Prediction results indicated that the errors are much larger compared to the case of a laboratory-scale dataset. The MAPE by the ANN model trained with part of the field data was not seriously different from that by the model trained with the laboratory data. However, the application of the ANFIS method improved the predictions significantly, reducing the MAPE to the half of that by the ANN model. Five selected empirical formulas were also applied to the same dataset, and Sheppard and Melville’s formula was found to provide the best prediction. However, the MAPEs for the scour depths predicted by empirical formulas are much larger than MAPEs by either the ANN or the ANFIS method. The ANFIS method predicts much better if the range of the training dataset is sufficiently wide to cover the range of the application dataset.

     

Group method of data handling to predict scour depth around bridge piers

In this study, group method of data handling network with quadratic polynomial was used to predict scour depth around bridge piers. Effective parameters on scour phenomena include sediment size, geometry of bridge pier, and upstream flow conditions. Different shapes of piers have been utilized to develop the GMDH network. Back propagation algorithm was performed to train the GHMD network which updated weighting coefficients of quadratic polynomial in each iteration of the training stage. The GMDH performed with the lowest errors of training and testing stages for cylindrical pier. Also, Richardson and Davis, Johnson’s equations produced relatively good performances for different types of piers. Finally, the results indicated that GMDH could be provided more accurate prediction than those obtained using traditional equations.     

Bayesian neural networks for prediction of equilibrium and time-dependent scour depth around bridge piers

The physical process of scour around bridge piers is complicated. Despite various models presented to predict the equilibrium scour depth and its time variation from the characteristics of the current and sediment, scope exists to improve the existing models or to provide alternatives to them. In this paper, a neural network technique within a Bayesian framework, is presented for the prediction of equilibrium scour depth around a bridge pier and the time variation of scour depth. The equilibrium scour depth was modeled as a function of five variables; flow depth and mean velocity, critical flow velocity, median grain diameter and pier diameter. The time variation of scour depth was also modeled in terms of equilibrium scour depth, equilibrium scour time, scour time, mean flow velocity and critical flow velocity. The Bayesian network predicted equilibrium and time-dependent scour depth much better when it was trained with the original (dimensional) scour data, rather than using a non-dimensional form of the data. The selection of water, sediment and time variables used in the models was based on conventional scour depth data analysis. The new models estimate equilibrium and time-dependent scour depth more accurately than the existing expressions. A committee model, developed by averaging the predictions of a number of individual neural network models, increased the reliability and accuracy of the predictions. A sensitivity analysis showed that pier diameter has a greater influence on equilibrium scour depth than the other independent parameters.     

ANN optimized by PSO and Firefly algorithms for predicting scour depths around bridge piers

Engineering with Computers - The estimation of scour depths is extremely important in designing the foundation of piers which ensure the integrity of bridges and other hydraulic structures....     

Generalized Regression Neural Networks and Feed Forward Neural Networks for prediction of scour depth around bridge piers

In this study, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN) approaches are used to predict the scour depth around circular bridge piers. Hundred and sixty five data collected from various experimental studies, are used to predict equilibrium scour depth. The model consisting of the combination of dimensional data involving the input variables is constructed. The performance of the models in training and testing sets are compared with observations. Then, the model is also tested by Multiple Linear Regression (MLR) and empirical formula. The results of all approaches are compared in order to get more reliable comparison. The results indicated that GRNN can be applied successfully for prediction of scour depth around circular bridge piers.     

CFD modeling of the in-cylinder flow in direct-injection Diesel engines

Three-dimensional flow calculations of the intake and compression stroke of a four-valve direct-injection Diesel engine have been carried out with different combustion chambers. A limited number of validation calculations of the compression stroke were first performed in order to explore the limits of CFD representation of the in-cylinder flow. The calculated flow field in three different combustion chambers was compared with laser Doppler velocimetry measurements; the comparison shows that the three-dimensional model is reasonably accurate for crank-angles around top dead center (TDC). In general, it performs better for low swirl combustion chambers while turbulence velocities are under-predicted when squish effects are important.In the main study, the flow characteristics inside the engine cylinder equipped with different piston configurations were compared. For this, complete calculations of the intake and compression strokes were performed under realistic operating conditions and the ensemble-averaged velocity and turbulence flow fields obtained in each combustion chamber analyzed in detail. The results confirmed that the piston geometry had little influence on the in-cylinder flow during the intake stroke and the first part of the compression stroke. However, the bowl shape plays a significant role near TDC and in the early stage of the expansion stroke by controlling both the ensemble-averaged mean and the turbulence velocity fields.     

Numerical modeling of three-dimensional flow and pollutant dispersion around structures

This paper describes a numerical modeling approach that can be used to provide estimates of emissions at industrial sites. In particular the models presented are capable of simulating the wind flow and dispersion of airborne pollutants around surface-mounted structures such as buildings or building complexes. The calculational procedure in this approach consists of two sequential steps, namely: (i) prediction of the mean flow via a turbulent flow model; and (ii) employment of the calculated flow field to drive a particle-in-cell transport and diffusion model. A benchmark simulation is performed in which numerical results from the flow model are compared with other numerical models and with experimental data for flow over a backward-facing step. Results from three-dimensional simulations of flow and dispersion over a two-building complex are also presented.     

水平单调载荷作用下的桩基桥墩滞回特性

为研究桩基桥墩-地基系统的非线性性能,根据模型与原型的物理相似关系制作1∶5比例的桩基桥墩模型. 采用力-位移混合控制加载的拟静力试验方法,通过在墩顶施加水平单调增加载荷,得到墩顶水平载荷下桩基桥墩的载荷-位移滞回曲线、骨架曲线和滞回特性. 用非线性弹簧单元模拟土体、用梁单元模拟桩和桥墩,建立模型桥墩的计算模型. 计算模型的骨架曲线与试验模型的骨架曲线吻合较好,表明采用非线性弹簧单元和梁单元分别模拟土体和桩是可行的,可以为考虑土-结构相互作用时的桥梁抗震分析提供参考依据.     

ODTLES: A multi-scale model for 3D turbulent flow based on one-dimensional turbulence modeling

A novel multi-scale approach for extending the one-dimensional turbulence (ODT) model of [A.R. Kerstein. One-dimensional turbulence: model formulation and application to homogeneous turbulence, shear flows, and buoyant stratified flows, J. Fluid Mech. 392 (1999) 277] to treat turbulent flow in three-dimensional (3D) domains is described. In this model, here called ODTLES, 3D aspects of the flow are captured by embedding three, mutually orthogonal, one-dimensional ODT domain arrays within a coarser 3D mesh. The ODTLES model is obtained by developing a consistent approach for dynamically coupling the different ODT line sets to each other and to the large scale processes that are resolved on the 3D mesh. The model is implemented computationally and its performance is tested by performing simulations of decaying isotropic turbulence at two different Reynolds numbers and comparing to the experimental data of [H. Kang, S. Chester, C. Meneveau. Decaying turbulence in an active-grid-generated flow and comparisons with large-eddy simulations, J. Fluid Mech. 480 (2003) 129; G. Comte-Bellot, S. Corrsin, Simple Eulerian correlation of full-and narrow band velocity signals in grid-generated ’isotropic’ turbulence, J. Fluid Mech. 48 (1971) 273].     

Seismically isolated bridge piers on shallow soil stratum with soil–structure interaction

The objective of this study is to assess the effects of soil–structure interaction on the response of seismically isolated bridge piers founded on a shallow soil stratum overlying a rigid bedrock and to develop a method that considers soil–structure interaction and can be easily applied to the preliminary design of bridges. The relative importance of several parameters of the bridge-isolators-soil system is examined. Cases in which soil–structure interaction needs to be incorporated in seismically isolated bridge design are identified and ways to take advantage of soil–structure interaction in order to enhance the safety level and reduce design costs are recommended.     

基于CFD的丙烯高温氯化反应器放大研究

基于对丙烯高温氯化反应条件的研究,提出了该反应器放大的原则,确定了两种将反应器生产能力扩大至原来的1.7倍的放大方案:①反应器直径和高度都放大至原来的1.195倍,喷嘴截面积增加至1.7倍;②反应器直径放大至原来的1.306倍,高度不变,喷嘴截面积增加至1.7倍.利用计算流体力学(CFD)软件CFX5对这两种放大方案进行了模拟计算,对放大前后反应器出口氯气摩尔分率、出口温度、釜内最高温度、釜内平均温度、釜内温度和流场分布图等进行了对比分析,结果表明,高径比例不变的放大方案最好.     

Multi scale modeling of 2450 MHz electric field effects on microtubule mechanical properties

Microtubule (MT) rigidity and response to 2450 MHz electric fields were investigated, via multi scale modeling approach. For this purpose, six systems were designed and simulated to consider all types of feasible interactions between α and β monomers in MT, by using all atom molecular dynamics method. Subsequently, coarse grain modeling was used to design different lengths of MT. Investigation of effects of external 2450 MHz electric field on MT showed MT less rigidity in the presence of such field, which may perturb its functions. Moreover, an additional computational setup was designed to study effects of 2450 MHz field on MT response to AFM tip. It was found, more tip velocity led to MT faster transformation and less time was required to change MT elastic response to plastic one, applying constant radius. Moreover it was observed smaller tip caused to increase required time to change MT elastic response to plastic one, considering constant velocity. Furthermore, exposing MT to 2450 MHz field led to no significant changes in MT response to AFM tip, but quick change in MT elastic response to plastic one.     

Support vector regression based modeling of pier scour using field data

This paper investigates the potential of support vector machines based regression approach to model the local scour around bridge piers using field data. A dataset of consisting of 232 pier scour measurements taken from BSDMS were used for this analysis. Results obtained by using radial basis function and polynomial kernel based Support vector regression were compared with four empirical relation as well as with a backpropagation neural network and generalized regression neural network. A total of 154 data were used for training different algorithms whereas remaining 78 data were used to test the created model. A coefficient of determination value of 0.897 (root mean square error=0.356) was achieved by radial basis kernel based support vector regression in comparison to 0.880 and 0.835 (root mean square error=0.388 and 0.438) by backpropagation neural and generalized regression neural network. Comparisons of results with four predictive equations suggest an improved performance by support vector regression. Results with dimensionless data using all three algorithms suggest a better performance by dimensional data with this dataset. Sensitivity analysis suggests the importance of depth of flow and pier width in predicting the scour depth when using support vector regression based modeling approach.     

Experimental investigation and finite element modelling of the effects of flow velocities on a skewed integral bridge

This paper presents finite element modelling of the effects of different flow velocities on the structural behaviour of a skewed integral bridge. Flow velocities affect the scour depths at the piles of a bridge and thus affect its structural behaviour. Laboratory tests on a scaled-down hydraulic model along with numerical modelling were performed to simulate the structural behaviour of the scoured integral bridge. A finite element package was used for the numerical modelling work, and the displacements and strains corresponding to the measured locations on the physical model were extracted. The experimental and numerical results for the case of maximum scour depths were compared.     

Numerical study of confinement effectiveness in solid and hollow reinforced concrete bridge piers: Methodology

A consistent methodology is suggested for modelling confinement in both solid and hollow reinforced concrete bridge pier sections, within the computational framework of three-dimensional nonlinear finite element analysis. The ultimate goal is to suggest the most convenient transverse reinforcement arrangements in terms of enhanced strength and ductility, as well as ease of construction and cost-effectiveness. The present study is particularly relevant with respect to confinement of hollow sections, for which previous experimental and analytical research is limited. Constitutive laws, modelling techniques, post-processing issues and preliminary applications are first introduced, and a large parametric model setup for circular and rectangular bridge piers of solid and hollow section, is subsequently presented. A detailed discussion follows on various issues concerning confinement modelling, aiming to broaden the scope and applicability of the suggested methodology. The respective numerical results and their interpretation and evaluation will be presented in a companion paper.     

Estimation of current-induced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system

The process of local scour around bridge piers is fundamentally complex due to the three-dimensional flow patterns interacting with bed materials. For geotechnical and economical reasons, multiple pile bridge piers have become more and more popular in bridge design. Although many studies have been carried out to develop relationships for the maximum scour depth at pile groups under clear-water scour condition, existing methods do not always produce reasonable results for scour predictions. It is partly due to the complexity of the phenomenon involved and partly because of limitations of the traditional analytical tool of statistical regression. This paper addresses the latter part and presents an alternative to the regression in the form of artificial neural networks, ANNs, and adaptive neuro-fuzzy inference system, ANFIS. Two ANNs model, feed forward back propagation, FFBP, and radial basis function, RBF, were utilized to predict the depth of the scour hole. Two combinations of input data were used for network training; the first input combination contains six-dimensional variables, which are flow depth, mean velocity, critical flow velocity, grain mean diameter, pile diameter, distance between the piles (gap), besides the number of piles normal to the flow and the number of piles in-line with flow, while the second combination contains seven non-dimensional parameters which is a composition of dimensional parameters. The training and testing experimental data on local scour at pile groups are selected from several precious references. Networks’ results have been compared with the results of empirical methods that are already considered in this study. Numerical tests indicate that FFBP-NN model provides a better prediction than the other models. Also a sensitivity analysis showed that the pile diameter in dimensional variables and ratio of pile spacing to pile diameter in non-dimensional parameters are the most significant parameters on scour depth.     

CFD模拟与流化床气体分布板的结构设计

针对流化床工业设备,简要阐述了加强气体分布板结构设计的重要性。在此基础上,结合对计算流体力学(computational fluid dynamics,CFD)理论及相关计算软件的介绍,重点阐明了FLUENT软件的计算特点及气固流化系统的流场模型,同时还列举了若干基于FLUENT、FIRE、PHOENICS、CFX等CFD软件的流化过程模拟及气体分布板结构优化的实例,并指出了该类研究的几点不足之处,以明确下一步的研发重点。     

达索V6平台桥梁建模分析一体化探索和应用

达索V6平台逐步引入SIMULIA软件的计算分析功能,改变传统的设计建模与计算分析相互独立完成的状态.为体验其一体化流程,以一座实际悬索桥中的桥塔结构为探索对象,在达索V6平台下对该桥塔从建模到计算分析的一体化进行初步探索.     

基于CFD仿真的切向涡轮流量传感器结构优化

为降低切向涡轮流量计的测量下限,以25 mm口径机械式热量表为例,利用计算流体动力学（ CFD）仿真软件对传感器内部三维流场进行研究,提出了提高传感器性能的10种结构改进方案,并通过CFD仿真对优化后的传感器性能进行预测,选择出最佳方案。结果表明：结构参数改进后,测量下限由0.07 m3/h降为0.035 m3/h。