Smeared crack approach: back to the original track

This paper briefly reviews the formulations used over the last 40 years for the solution of problems involving tensile cracking, with both the discrete and the smeared crack approaches. The paper focuses on the smeared approach, identifying as its main drawbacks the observed mesh‐size and mesh‐bias spurious dependence when the method is applied ‘straightly’. A simple isotropic local damage constitutive model is considered, and the (exponential) softening modulus is regularized according to the material fracture energy and the element size. The continuum and discrete mechanical problems corresponding to both the weak discontinuity (smeared cracks) and the strong discontinuity (discrete cracks) approaches are analysed and the question of propagation of the strain localization band (crack) is identified as the main difficulty to be overcome in the numerical procedure. A tracking technique is used to ensure stability of the solution, attaining the necessary convergence properties of the corresponding discrete finite element formulation. Numerical examples show that the formulation derived is stable and remarkably robust. As a consequence, the results obtained do not suffer from spurious mesh‐size or mesh‐bias dependence, comparing very favourably with those obtained with other fracture and continuum mechanics approaches. Copyright © 2006 John Wiley & Sons, Ltd.     

Inverse Analysis of Deep Excavation Using Differential Evolution Algorithm

This paper presents the applications of the differential evolution (DE) algorithm in back analysis of soil parameters for deep excavation problems. A computer code, named Python‐based DE, is developed and incorporated into the commercial finite element software ABAQUS, with a parallel computing technique to run an FE analysis for all trail vectors of one generation in DE in multiple cores of a cluster, which dramatically reduces the computational time. A synthetic case and a well‐instrumented real case, that is, the Taipei National Enterprise Center (TNEC) project, are used to demonstrate the capability of the proposed back‐analysis procedure. Results show that multiple soil parameters are well identified by back analysis using a DE optimization algorithm for highly nonlinear problems. For the synthetic excavation case, the back‐analyzed parameters are basically identical to the input parameters that are used to generate synthetic response of wall deflection. For the TNEC case with a total of nine parameters to be back analyzed, the relative errors of wall deflection for the last three stages are 2.2, 1.1, and 1.0%, respectively. Robustness of the back‐estimated parameters is further illustrated by a forward prediction. The wall deflection in the subsequent stages can be satisfactorily predicted using the back‐analyzed soil parameters at early stages. Copyright © 2014 John Wiley & Sons, Ltd.     

Mining knowledge from result comparison between spatial clustering themes

This paper introduces some definitions and defines a set of calculating indexes to facilitate the research, and then presents an algorithm to complete the spatial clustering result comparison between different clustering themes. The research shows that some valuable spatial correlation patterns can be further found from the clustering result comparison with multi-themes, based on traditional spatial clustering as the first step. Those patterns can tell us what relations those themes have, and thus will help us have a deeper understanding of the studied spatial entities. An example is also given to demonstrate the principle and process of the method.     

A fuzzy dynamic flood routing model for natural channels

A fuzzy dynamic flood routing model (FDFRM) for natural channels is presented, wherein the flood wave can be approximated to a monoclinal wave. This study is based on modification of an earlier published work by the same authors, where the nature of the wave was of gravity type. Momentum equation of the dynamic wave model is replaced by a fuzzy rule based model, while retaining the continuity equation in its complete form. Hence, the FDFRM gets rid of the assumptions associated with the momentum equation. Also, it overcomes the necessity of calculating friction slope (S_f) in flood routing and hence the associated uncertainties are eliminated. The fuzzy rule based model is developed on an equation for wave velocity, which is obtained in terms of discontinuities in the gradient of flow parameters. The channel reach is divided into a number of approximately uniform sub‐reaches. Training set required for development of the fuzzy rule based model for each sub‐reach is obtained from discharge‐area relationship at its mean section. For highly heterogeneous sub‐reaches, optimized fuzzy rule based models are obtained by means of a neuro‐fuzzy algorithm. For demonstration, the FDFRM is applied to flood routing problems in a fictitious channel with single uniform reach, in a fictitious channel with two uniform sub‐reaches and also in a natural channel with a number of approximately uniform sub‐reaches. It is observed that in cases of the fictitious channels, the FDFRM outputs match well with those of an implicit numerical model (INM), which solves the dynamic wave equations using an implicit numerical scheme. For the natural channel, the FDFRM outputs are comparable to those of the HEC‐RAS model. Copyright © 2009 John Wiley & Sons, Ltd.     

Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches

ABSTRACT

In this research, five hybrid novel machine learning approaches, artificial neural network (ANN)-embedded grey wolf optimizer (ANN-GWO), multi-verse optimizer (ANN-MVO), particle swarm optimizer (ANN-PSO), whale optimization algorithm (ANN-WOA) and ant lion optimizer (ANN-ALO), were applied for modelling monthly reference evapotranspiration (ET_o) at Ranichauri (India) and Dar El Beida (Algeria) stations. The estimates yielded by hybrid machine learning models were compared against three models, Valiantzas-1, 2 and 3 based on root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC) and Willmott index (WI). The results of comparison show that the ANN-GWO-1 model with five input variables (T_min, T_max, RH, U_s, R_s) provides better estimates at both study stations (RMSE = 0.0592/0.0808, NSE = 0.9972/0.9956, PCC = 0.9986/0.9978, and WI = 0.9993/0.9989). Also, the adopted modelling strategy can build a truthful expert intelligent system for estimating the monthly ET_o at study stations.     

A New Strategy for Solving a Class of Constrained Nonlinear Optimization Problems Related to Weather and Climate Predictability

There are three common types of predictability problemsin weather and climate, which each involve different constrained nonlinearoptimization problems: the lower bound of maximum predictable time, theupper bound of maximum prediction error, and the lower bound of maximumallowable initial error and parameter error. Highly efficient algorithmshave been developed to solve the second optimization problem. And thisoptimization problem can be used in realistic models for weather and climateto study the upper bound of the maximum prediction error. Although afiltering strategy has been adopted to solve the other two problems, directsolutions are very time-consuming even for a very simple model, whichtherefore limits the applicability of these two predictability problems inrealistic models. In this paper, a new strategy is designed to solve theseproblems, involving the use of the existing highly efficient algorithms forthe second predictability problem in particular. Furthermore, a series ofcomparisons between the older filtering strategy and the new method areperformed. It is demonstrated that the new strategy not only outputs thesame results as the old one, but is also more computationally efficient.This would suggest that it is possible to study the predictability problemsassociated with these two nonlinear optimization problems in realisticforecast models of weather or climate.     

Improving groundwater-flow modeling using optimal zoning methods

Hydraulic conductivity sometimes exhibits complicated spatial variation over a site. A thorough understanding of the spatial distributions of hydraulic conductivity helps to make deterministic models of groundwater more accurate. This study presents a novel procedure that combines simulated annealing algorithms (SA) and the shortest distance method (SD) with the modular three-dimensional groundwater flow model (MODFLOW). The procedure is applied to a hypothetical site with groundwater-monitoring wells to minimize the difference between simulated and observed hydraulic head for optimal zoning of the spatial distribution of hydraulic conductivity. The results of this optimal zoning method indicate that this new procedure not only improves the efficiency of optimization, but also increases the probability of finding the global optimum, minimizing the errors of the hydraulic head simulated by MODFLOW in two scenarios, one with known and the other with unknown hydraulic conductivity. The results also illustrated that the procedure can effectively determine and delineate hydrogeological zones.     

基于遗传算法的分类体系在地震预报中的应用探索

采用模似自然界生物进化过程的遗传算法的分类体系,并以首都圈地区为例,利用地震活动性资料的8种时空参数在地震预报中进行了应用探索．通过对1967—1992年间信息的学习演化,获得了20条拥有不同适应度的具有层次结构的地震预报规则集．对首都圈地区中等地震活动的预报规则进行了初步的分析讨论,发现了人们以往难于考虑周全的可能具有重要预报意义的异常组配.应用探索表明：在地震预报研究中应用具有自适应和潜在学习能力的基于遗传算法的分类体系,将可能推动人们对地震前兆及其物理机制的研究和探讨．     

地震层析成像LSQR算法的并行化

讨论了地震层析成像的LSQR算法(最小二乘QR分解). 在建立偏导数矩阵方程组时,对区内地震在方程中保留震源项,引入正交投影算子进行参数分离,对区外远震采用传统的平滑处理方式,用LSQR法求解联立的方程组. 由于区内地震的正交分解处理和区外远震的平滑处理,使得偏导数矩阵中的非零元素成倍增加,对于大型反演问题,这些非零元素常常达到几十GB到几百GB的数量级,巨量的内存占用成为LSQR算法的瓶颈. 针对这一问题,本文研究了偏导数矩阵中非零元素的分布规律,设计出合理的存储结构,采用分布式存储进行矩阵计算,提出了LSQR算法的并行化方案,并在联想深腾6800超级计算机上实现. 导出了LSQR算法的并行效率估算公式. 对两个地区的实际地震层析成像数据进行了效率测试.     

区块链技术在教育领域的应用现状与展望

区块链本质上的去中心化和安全特性,使得其很适合于解决目前教育领域面临的困难.本文首先介绍了区块链的基本技术原理,包括区块的结构和区块链的构成、区块链技术平台的体系结构、区块链的分类、共识算法、智能合约.接着分析了目前教育领域发展的终身教育和跨地区教育的新形势,以及传统数字化教育系统面临的主要问题.然后从教育相关信息的多方共享和验证、学习过程跟踪、激励和学习路径塑造、学习评估、教育管理与决策辅助等几个方面对区块链技术在教育领域的应用现状进行了介绍和分析.最后总结了目前区块链技术应用到教育领域的主要问题,并展望了未来的发展方向.