动态单调荷载下约束再生混凝土单轴受压应力-应变全曲线方程

在MTS 815力学试验上,完成了81个箍筋约束再生混凝土短柱动态力学试验,分析约束再生混凝土在不同变率下的破坏特征。基于获得的应力-应变试验曲线,研究应变率效应、箍筋约束效应和再生粗骨料取代率对约束再生混凝土力学和变形性能的影响。根据应变率效应对约束再生混凝土力学和变形性能的影响规律,初步提出约束再生混凝土性能指标动态放大系数（DIF）模型。分析箍筋形式、箍筋间距、箍筋屈服强度和配箍率等因素对约束再生混凝土短柱力学行为的影响,初步提出约束再生混凝土性能指标约束放大系数（CIF）模型。分析表明,箍筋约束效应对再生混凝土的力学和变形性能有重要的影响,在很大程度上改善了再生混凝土的延性。研究再生粗骨料取代率对约束再生混凝土力学和变形性能的影响规律,提出取代率影响因子（RIF）模型。基于约束再生混凝土短柱动态试验数据分析,提出考虑应变率效应、箍筋约束效应和再生粗骨料取代率影响的约束再生混凝土应力-应变分析模型,定义受压应力-应变全曲线特征点参数,确定约束再生混凝土受压应力-应变全曲线方程。研究表明,应力-应变计算曲线和试验曲线吻合较好,验证了分析模型的合理性,为再生混凝土结构动力非线性分析和抗震优化设计提供研究基础。     

对“准脆性材料单轴拉伸破坏全过程物理模型研究”的讨论

笔者很高兴读到白卫峰先生等的"准脆性材料单轴拉伸破坏全过程物理模型研究"[1](以下简称原文)一文。文章提出了一个由具有回折失稳(也称跃返失稳)特征的应力–应变曲线和一个应变软化特征的应力–应变曲线组成的"双本构物理模型(DCPM)",并以此来模拟混凝土类准脆性材料的破坏现象。文章表明作者进行了大量的研究工作。     

结构轻骨料混凝土应力—应变曲线研究

对国内外四种轻骨料混凝土应力-应变曲线方程进行了分析、比较,结合相关试验结果,提出了分段式方程.上升段表达式和现行<轻骨料混凝土结构技术规程>(JGJ12-2006)一致,下降段采用有理分式,方程中唯一的常数B可由关键点坐标确定,并给出了关键点坐标和峰值应力之间的关系表达式.该方程形式简单,能够完整、准确地反映曲线下降段的应力一应变关系,可为轻骨料混凝土结构非线性分析提供参考依据.     

土的初始和再压缩曲线分析模型

 土的初始和再压缩过程中,孔隙比与压力关系(即e-p曲线)是天然地基沉降计算与分析的关键之一。首先在土的初始压缩变形机制及其e-p曲线特征研究基础上,引入土应力–应变关系的双曲线模型,并从完全侧限条件下土的应变与孔隙比关系入手,建立出土的初始压缩e-p曲线分析模型及其参数确定分析方法;然后,根据土的初始与再压缩e-p曲线模型的区别是其初始孔隙比与初始压缩模量不同及其产生的原因,提出再压缩土的初始孔隙比与初始压缩模量等参数确定方法,进而建立出考虑应力历史影响的再压缩土e-p曲线分析模型。本文建立的初始与再压缩e-p曲线分析模型均只包含初始孔隙比、初始压缩模量和压缩系数3个常规试验参数,模型简单,参数少,且物理意义明确。最后,通过实测与理论分析曲线的对比分析,表明了该模型的合理性与可行性。     

FRP约束钢筋混凝土柱承载力非线性有限元分析

借助有限元分析软件ABAQUS,通过建模合理选取材料本构关系、确定单元类型、界面处理以及网格划分的设置,建立FRP约束钢筋混凝土柱在轴心压力作用下的非线性有限元计算模型,得出各个阶段试件的承载能力。与试验结果进行比较,结果表明,有限元计算的极限承载力和荷载-挠度曲线与试验结果较好吻合,验证了有限元分析模型的适用性。     

混凝土冻融损伤力学性能变化研究综述

混凝土在经历一定次数冻融循环后,其各方面指标均会有一定的改变。其中应力应变曲线发生明显的变化。本文会在已有的试验及其数据结果之上描述混凝土冻融循环后的应力-应变曲线性能的改变,同时还给出了部分研究者经过冻融循环后混凝土应力-应变关系模型及拟合情况。     

爆炸作用下钢筋混凝土柱非线性动力响应及破坏模式分析方法

为建立爆炸作用下钢筋混凝土柱非线性动力响应及破坏模式的高效分析方法,从材料应力-应变关系出发,分别应用截面分层法、修正压场理论方法（MCFT）和Krauthammer模型建立了单调加载条件下抗力曲线（包括截面弯矩-曲率关系、平均剪应力-平均剪应变关系和直剪剪力-直剪滑移关系）,并以单调加载条件下抗力曲线为骨架,提出了加卸载条件下RC柱截面抗力曲线;在此基础上,根据Timoshenko梁理论和有限差分方法,建立了爆炸作用下RC柱非线性动力响应的显式分析方法。研究结果表明：本文方法能够定量分析爆炸作用下RC柱的动力响应和破坏模式,计算结果与相关试验数据有较好的一致性。     

聚丙烯纤维水泥砂浆动态力学特性与本构模型研究

本文通过改进的分离式Hopkinson压杆试验装置,对聚丙烯纤维水泥砂浆进行动态力学性能以及动态损伤本构关系的研究。基于试验结果,得到应力～应变曲线,并对聚丙烯纤维水泥砂浆在不同应变率下的弹性模量、峰值应力和韧度变化规律进行了探讨。根据实验应力应变曲线的基本特征,选择引入朱-王-唐本构模型,并考虑纤维的阻裂作用,对试验结果进行拟合,提出了一种新的聚丙烯纤维水泥砂浆动态损伤本构模型。该模型在考虑聚丙烯纤维增强、应变率硬化、损伤软化等因素下,描述聚丙烯纤维水泥砂浆的动态受力特性。     

高强混凝土构件正截面承载力计算方法

根据近年来国内外高强混凝土的研究成果,总结了高强混凝土强度指标的换算关系,提出正截面承载力计算用的高强混凝土受压应力-应变曲线.根据该关系曲线分析了高强混凝土构件的正截面承载力,建议了应力-应变曲线参数的取值.在此基础上,进一步给出了等效矩形应力图形系数.     

岩土体复合幂–指数非线性模型

以往岩土体的应力–应变曲线只能根据其形态类型采用不同的数学模式来描述,且针对相应体变曲线的描述研究较少。为实现描述形态各异的应力–应变曲线及体变曲线数学模式的统一,通过分析幂函数与指数函数,提出了一个新的非线性模型——复合幂–指数模型(简称CPE模型),并给出了模型参数的确定方法。以结构性黄土为例,通过对不同含水量、不同围压常规三轴试验得到的应力–应变曲线及体变曲线进行CPE模式拟合,并分析各参数的变化规律,得到了非饱和原状黄土考虑含水量的应变软化型、考虑围压的应变硬化型应力–应变曲线CPE模型,及考虑围压的剪缩型体变曲线CPE模型,并将其计算曲线与实测曲线进行了对比,研究表明:CPE模型可描述各种型式的应力–应变曲线及不同形态的体变曲线,具有广泛的适用性和准确性,为形态各异的应力–应变曲线及体变曲线提供了一个统一的数学模型。     

A new hard rock TBM performance prediction model for project planning

Among the models used for performance prediction of hard rock tunnel boring machines two stand out and are often used in the industry. They include the semi theoretical model by Colorado School of Mines and the empirical model by Norwegian University of Science and Technology in Trondheim (NTNU). While each have their strong points and area of applications, more accurate prediction has been sought by modifying one of the existing models or introduction of a new model. To achieve this, a database of actual machine performance from different hard rock TBM tunneling projects has been compiled and analyzed to develop a new TBM performance prediction model. To analyze the available data and offer new equations using statistical methods, relationships between different geological and TBM operational parameters were investigated. Results of analyzes show that there are strong relationships between geological parameters (like UCS, joint spacing and RQD) and TBM performance parameters specially Field Penetration Index (FPI). In this study, a boreability classification system and a new empirical chart, for preliminary estimation of rock mass boreability and TBM performance is suggested.     

Data Mining and Knowledge Discovery in Sediment Transport

The means for data collection have never been as advanced as they are today. Moreover, the numerical models we use today have never been so advanced. Feeding and calibrating models against collected measurements, however, represents only a one-way flow: from measurements to the model. The observations of the system can be analyzed further in the search for the information they encode. Such automated search for models accurately describing data constitutes a new direction that can be identified as that of data mining. It can be expected that in the years to come we shall concentrate our efforts more and more on the analysis of the data we acquire from natural or artificial sources and that we shall mine for knowledge from the data so acquired.
Data mining and knowledge discovery aim at providing tools to facilitate the conversion of data into a number of forms, such as equations, that provide a better understanding of the process generating or producing these data. These new models combined with the already available understanding of the physical processes—the theory—result in an improved understanding and novel formulations of physical laws and improved predictive capability.
This article describes the data mining process in general, as well as an application of a data mining technique in the domain of sediment transport. Data related to the concentration of suspended sediment near a bed are analyzed by the means of genetic programming. Machine-induced relationships are compared against formulations proposed by human experts and are discussed in terms of accuracy and physical interpretability.     

改进的岩石节理弹性非线性法向变形本构模型研究

回顾了已有的几类岩石节理法向变形本构模型,指出以往模型在中应力水平条件下模拟结果与试验结果发生偏离的现象。针对这一问题,引入"半值节理最大闭合量应力"σ1/2概念,着重论述了传统的BB模型与经典指数模型本身所固有的数学缺陷。随后,以Malama和Kulatilake(2003)提出的统一指数模型为例,进一步阐明模型改进的必要性,同时分析了该模型因采用对σn/σ1/2项添加幂函数n的修正方式而丢失节理法向初始刚度Kni的物理意义这一不足之处。基于上述研究,根据前人试验数据分析建立单调加载条件下岩石节理法向应力–位移关系曲线的控制微分方程,定义拟节理最大允许闭合量Dmax=ξdmax的概念,提出一种新的三参数本构模型--改进的岩石节理弹性非线性法向变形本构关系,并在数学上严格证明了BB模型与经典指数模型是新模型的两个特例。新模型克服了前述模型数学上的缺陷,弥补了以往模型模拟结果与试验结果发生偏离的不足,且没有因新参数ξ的添加丢失Kni的物理意义。最后采用新模型对他人试验结果进行预测,建议了修正参数ξ的确定方法。模型预测结果与试验结果吻合良好,进而验证了本文模型的可行性。     

三维离散元计算参数选取方法研究

简要分析了岩体力学计算中比较重要的几何参数和物理参数的选取问题。通过对几种离散元方法接触关系的比较，讨论了在不同的计算模型中结构面刚度所代表的物理意义。指出在刚性块体模型中，结构面的刚度是包含了岩块和结构面物理特性的等效刚度，而在可变形块体模型中，只有当块体间的接触弹簧刚度取足够大时，才能客观地反映岩体的特性。太大的接触刚度影响计算的可靠性，为此给出了一种简单的处理方法。通过量纲分析的方法给出了岩体结构振动的阻尼表达式，指出可以通过岩体的振动实验确定阻尼比，并进而给出特征时间。量纲分析得到的特征时间为计算时间步长提供了参考值。最后，介绍了离散元法的计算控制及滑动与失稳的判据。     

Usage of fuzzy modeling for cultivation of forests irigated by wastewater treatment plants

Cultivation of forests and plant species by utilizing processed wastewater and sludge from the wastewater treatment plants is an effective method to reduce the release of harmful substances and provide favorable conditions for the development of agricultural sector under the shortage of water resources. For this purpose, the experiments have been carrying out to investigate the ability of species to absorb chemical elements under increased concentrations in wastewater, as well as other characteristics and features of species’ behavior in biological, chemical and physical aspects. The behavior is a result of complex cause‐effect dynamical interactions in ecological networks with unknown mathematical models of underlying functional relationships. There are two important features inherent in the investigation of cultivating species by processed wastewater: empirical modeling of species’ behavior and qualitative reasoning with imprecise information about this behavior.

Proceeding from analysis of chains of interactions viewed as functional mappings between factors and investigated objects, the paper proposes a solution to the development of fuzzy modeling tools to support the investigation process. The proposed architecture includes the following components: a model that describes an ecological network under investigation; artificial neural network modules that model complex direct cause‐effect interactions; a fuzzy inference system that provides the investigator with qualitative reasoning about these interactions within a framework of the model; and control system that, under the requests from the investigator, controls the operation of the above components.     

A Condition‐Based Deterioration Model for the Stochastic Dependency of Corrosion Rate and Crack Propagation in Corroded Concrete Structures

Physics‐based models are intensively studied in mechanical and civil engineering but their constant increase in complexity makes them harder to use in a maintenance context, especially when degradation model can/should be updated from new inspection data. On the other hand, Markovian cumulative damage approaches such as Gamma processes seem promising; however, they suffer from lack of acceptability by the civil engineering community due to poor physics considerations. In this article, we want to promote an approach for modeling the degradation of structures and infrastructures for maintenance purposes which can be seen as an intermediate approach between physical models and probabilistic models. A new statistical, data‐driven state‐dependent model is proposed. The construction of the degradation model will be discussed within an application to the cracking of concrete due to chloride‐induced corrosion. Numerical experiments will later be conducted to identify preliminary properties of the model in terms of statistical inferences. An estimation algorithm is proposed to estimate the parameters of the model in cases where databases suffer from irregularities.     

Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption

There are several ways to attempt to model a building and its heat gains from external sources as well as internal ones in order to evaluate a proper operation, audit retrofit actions, and forecast energy consumption. Different techniques, varying from simple regression to models that are based on physical principles, can be used for simulation. A frequent hypothesis for all these models is that the input variables should be based on realistic data when they are available, otherwise the evaluation of energy consumption might be highly under or over estimated.In this paper, a comparison is made between a simple model based on artificial neural network (ANN) and a model that is based on physical principles (EnergyPlus) as an auditing and predicting tool in order to forecast building energy consumption. The Administration Building of the University of São Paulo is used as a case study. The building energy consumption profiles are collected as well as the campus meteorological data.Results show that both models are suitable for energy consumption forecast. Additionally, a parametric analysis is carried out for the considered building on EnergyPlus in order to evaluate the influence of several parameters such as the building profile occupation and weather data on such forecasting.     

Differences in spatial understanding between physical and virtual models

In the digital age, physical models are still used as major tools in architectural and urban design processes. The reason why designers still use physical models remains unclear. In addition, physical and 3D virtual models have yet to be differentiated. The answers to these questions are too complex to account for in all aspects. Thus, this study only focuses on the differences in spatial understanding between physical and virtual models. In particular, it emphasizes on the perception of scale. For our experiment, respondents were shown a physical model and a virtual model consecutively. A questionnaire was then used to ask the respondents to evaluate these models objectively and to establish which model was more accurate in conveying object size. Compared with the virtual model, the physical model tended to enable quicker and more accurate comparisons of building heights.     

水源热泵回灌困难颗粒阻塞试验研究

 以水源热泵回灌过程中物理堵塞现象作为研究对象,利用玻璃珠和砾石颗粒作为多孔介质,以碱性氧化铝作为悬浮颗粒,利用自行研制的砂层阻塞试验系统装置模拟多孔介质中悬浮颗粒的迁移和沉积过程,试验中介质运动速度控制在20～200 cm/min。研究悬浮颗粒在玻璃珠和砾石颗粒中沉积时水压力与流速关系,对悬浮颗粒在2种不同介质中的沉积机制进行分析;同时研究玻璃珠介质在相同的流速和不同的悬浮颗粒浓度下相对渗透率与时间的变化关系;最后给出渗透系数衰减模型,模型对预见渗透系数的降低是有效的。     

Forecasting building energy consumption with hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system

There are several ways to forecast building energy consumption, varying from simple regression to models based on physical principles. In this paper, a new method, namely, the hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system (GA-HANFIS) model is developed. In this model, hierarchical structure decreases the rule base dimension. Both clustering and rule base parameters are optimized by GAs and neural networks (NNs). The model is applied to predict a hotel’s daily air conditioning consumption for a period over 3 months. The results obtained by the proposed model are presented and compared with regular method of NNs, which indicates that GA-HANFIS model possesses better performance than NNs in terms of their forecasting accuracy.