FRP-混凝土-钢管组合柱滞回性能数值分析

在确定各材料应力-应变关系的基础上,采用纤维模型法对FRP-混凝土-钢管组合柱在往复荷载作用下的滞回性能进行了数值模拟,建立了FRP-混凝土-钢管组合柱滞回模型。算例分析结果表明,有限元计算和试验结果总体上吻合良好。     

FRP约束圆钢筋混凝土柱滞回性能的理论分析

在分别确定FRP、钢筋和核心混凝土在单调应力以及往复应力作用下的应力-应变关系模型的基础上,采用纤维模型法对FRP约束圆钢筋混凝土柱的M-和P-Δ滞回曲线进行了计算,计算结果和试验结果吻合较好。在数值计算的基础上,分析了含FRP率、钢筋屈服强度、纵向配筋率、混凝土强度、轴压比和长细比等参数对FRP约束圆钢筋混凝土压弯构件M-和P-Δ滞回关系骨架曲线的影响规律,最后建议了简化的滞回模型。     

T、L形钢管混凝土柱的本构模型及非线性分析研究

以现有T、L形钢管混凝土柱试验研究为基础,本文进行了如下工作：（1）针对T、L形钢管混凝土柱的受压约束承载机理,建立了内填混凝土的等效单轴滞回本构模型;（2）建立了悬臂柱在反复荷载作用下的滞回全过程分析模型,编制了T、L形钢管混凝土柱的滞回全过程分析程序;（3）对所完成的试件进行了滞回全过程数值分析,分析与试验结果在荷载-位移和荷载-应变两种层次上进行了对比,验证了本文模型和分析程序的正确性,表明该程序能从构件、材料两种层次上表现出钢管混凝土柱的非线性发展过程。之后,对T、L形钢管混凝土柱中有代表性混凝土和钢单元应力-应变发展全过程进行了数值模拟。结果表明本文模型能用于T、L形钢管混凝土结构的非线性分析。     

钢管混凝土T,L形柱在反复荷载作用下的抗震性能分析研究

在现有T,L形钢管混凝土柱试验研究基础上,针对T,L形钢管混凝土柱的受压约束承载机理,提出了内填混凝土的等效单轴本构模型,建立了悬臂柱在反复荷载作用下的滞回全过程分析模型,编制了T,L形钢管混凝土柱的滞回全过程分析程序,将全过程滞回数值分析结果与试验结果进行了对比,验证了模型和分析程序的正确性。对T,L形钢管混凝土柱中有代表性混凝土和钢单元应力-应变发展全过程进行了数值模拟,结果表明模型能用于T形、L形钢管混凝土结构的非线性分析。     

铁尾矿砂混凝土柱滞回性能数值模拟分析

为准确了解铁尾矿砂混凝土柱的滞回性能,基于ABAQUS软件中的混凝土塑性损伤模型,采用箍筋约束铁尾矿砂混凝土受压应力—应变关系,对铁尾矿砂混凝土柱在低周反复荷载作用下的滞回性能进行了数值模拟;并将计算结果与8根不同混凝土强度等级、配箍特征值及设计轴压比的柱试件进行对比分析。分析计算结果与试验结果吻合较好,表明所采用的箍筋约束铁尾矿砂混凝土受压应力—应变材料模型能够较全面准确地表达铁尾矿砂混凝土柱在反复荷载作用下的复杂受力行为。     

反复荷载作用下矩形中空钢管混凝土柱的滞回性能研究

首先建立钢管与核心混凝土界面的粘结-滑移滞回模型、考虑包辛格效应的钢材单轴滞回模型和考虑矩形钢管约束效应后内填混凝土的单轴应力-应变滞回模型。在截面纤维模型的基础上,建立考虑界面粘结-滑移的矩形钢管混凝土柱分析模型,用Fortran语言编制相应的全过程分析程序。考虑不同的空心率因素,对4个矩形中空钢管混凝土试件进行数值模拟计算,并对荷载-位移滞回关系进行初步的理论分析。     

型钢混凝土柱基于OpenSEES平台的滞回性能分析

为研究型钢混凝土柱在低周反复荷载作用下的抗震性能,采用开源的有限元分析软件OpenSEES,建立基于纤维模型的型钢混凝土柱模型,通过定义不同单轴材料本构关系的纤维来模拟其各自不同的应力-应变关系,结合非线性梁柱单元,形成完整的型钢混凝土柱模型,并采用此模型对低周反复荷载作用下4个不同轴压比的型钢混凝土柱进行滞回性能的全过程数值模拟.研究结果表明;型钢混凝土柱具有较好的抗震性能,但在高轴压比下,耗能能力和延性下降剧烈.     

往复荷载下混凝土本构模型的选择在ABAQUS中的应用

对ABAQUS有限元软件中的混凝土模型进行简要介绍,通过选用几种具有代表性的混凝土本构,对往复荷载下的混凝土应力-应变关系进行数值模拟,将模拟结果同试验结果进行比较,探讨适用于ABAQUS有限元软件的用于模拟往复荷载的混凝土本构模型。在比较分析的基础上,选用同试验吻合情况良好的Mander混凝土本构模型及用于模拟钢管混凝土的韩林海模型对钢管混凝土叠合柱构件和钢管混凝土柱-钢梁连接节点构件进行滞回性能模拟。模拟结果表明所选混凝土本构适用于模拟含混凝土材料的构件在往复荷载下的混凝土力学性能,可供研究参考。     

几种有代表性的约束混凝土滞回模型

约束混凝土模型是结构非线性计算机模拟分析的基础之一,它包括应力—应变模型与滞回模型两个部分。通过文献层面的分析比较,从约束混凝土滞回模型中优选其中5种加以介绍,并对部分模型存在的不足进行了分析与修正。     

考虑钢筋与混凝土相对滑移的修正钢筋本构模型

为了研究钢筋滑移对钢筋混凝土柱抗震性能的影响,在材料层次上建立了考虑钢筋与混凝土相对滑移的修正钢筋本构模型。采用等效三段阶梯函数描述黏结应力沿黏结长度的非线性分布规律,结合钢筋双线性应力-应变关系,推导了锚固钢筋滑移和平均滑移应变的理论计算式。锚固钢筋的总应变定义为理想黏结的钢筋应变与平均滑移应变之和。分别通过屈服和极限应力对应的钢筋总应变修正弹性模量和硬化模量,进而得到间接考虑钢筋滑移的简化分段线性应力-应变关系。非锚固钢筋的滑移采用降低屈服应力和屈服应变的方法加以考虑。对于钢筋的滞回规则,采用Sakayi等修正的Menegotto-Pinto模型。将本构模型通过子程序嵌入ABAQUS软件中,基于纤维梁单元,对往复荷载作用下钢筋混凝土柱的非线性响应进行了数值模拟,并与试验结果对比,验证了该模型的有效性。结果表明,所建议的钢筋本构模型,数学表达形式简单且计算效率高,能够模拟地震作用下钢筋混凝土柱的承载力和变形特性,达到满足工程需要的精度。     

盘形滚刀与岩石相互作用理论研究现状及分析（一）

全断面岩石掘进机盘形滚刀破岩理论是全断面岩石掘进机研制及其施工工程工期预测等的理论基础。通过对国内外学者对盘形滚刀破岩理论研究成果的分析,总结建立这些成果所依据的模型特点和分析方法,据此将这些模型分为经验模型、一维模型、二维模型和三维模型进行论述,国内外学者对一维模型的研究较深入,成果也较多;对二维模型的研究次之,但也产生了相对成熟和实用的结论;对三维模型的研究则较少,尚未建立成熟和实用的结论。     

面向数字城市规划的城市规划模型研究

作为面向数字城市规划的关键技术,地理信息系统技术、遥感技术支撑下的城市规划模型的研究已经成为城市规划领域的热点问题之一。根据规划模型研究的对象和目的的不同,提出将城市规划模型分为综合规划模型和工程专项规划模型,按照这种分类方式这两种模型的研究进展进行了简要的介绍,并对其研究趋势与研究对策进行了分析。     

Comparison of artificial neural networks and general linear model approaches for the analysis of abrasive wear of concrete

This study aims to determine the influence of metallic aggregate content, cement content and different loads applied on the abrasive wear of concrete by using artificial neural networks (ANN) and general linear model (GLM) approaches. For this purpose, experimental studies are made and suitable models based on experimental results are developed to estimate the abrasive wear of concrete. In these models, 60 data set was used. For training set, 48 data (80%) were randomly selected and the residual data (12 data, 20%) were selected as test set. Root mean square error (RMSE) and determination coefficient (R²) statistics are used as evaluation criteria of the ANN and GLM models and the experimental results are compared with these models. The comparison results indicate that the ANN models are superior to the GLM models in modeling of the influence metallic aggregate content, cement content and different loads applied on the abrasive wear of concrete.     

Risk assessment of “down-the-drain” chemicals: Search for a suitable model

Legal and regulatory authorities around the world generally require that risk assessments are undertaken for the licensing of new and existing substances that present high risk to the environment or the human health. This applies to ‘down-the-drain’ chemicals that are usually found in household products, such as detergents, that are mainly discharged into rivers via sewer systems. However, the data available for these chemicals is often limited due to cost constraints: in particular, concentration time series for works effluent are generally unavailable, even load data for specific works is often scarce.Although a wide range of models are available, there is a general lack of knowledge on their suitability to model the fate of down-the-drain chemicals at the catchment scale. Several models are presented in this review. The models selected are: the Mackay models, EUSES, Mike 11, QUAL2E, TOMCAT and GREAT-ER. Various applications of these models were investigated to investigate their strength and weaknesses. It appears that, where the availability of data is limited, multimedia fate models such as the Mackay models and EUSES may best be applied to estimate the global risk within each media. However, for site-specific risk assessment the GREAT-ER in-stream water quality model was considered to be more appropriate for modelling down-the-drain chemicals, because it accounts for both spatial and temporal variability, while its data requirements are lower than for models such as Mike 11 and QUAL2E.     

SPT based determination of undrained shear strength: Regression models and machine learning

The purpose of this study is the accurate prediction of undrained shear strength using Standard Penetration Test results and soil consistency indices, such as water content and Atterberg limits. With this study, along with the conventional methods of simple and multiple linear regression models, three machine learning algorithms, random forest, gradient boosting and stacked models, are developed for prediction of undrained shear strength. These models are employed on a relatively large data set from different projects around Turkey covering 230 observations. As an improvement over the available studies in literature, this study utilizes correct statistical analyses techniques on a relatively large database, such as using a train/test split on the data set to avoid overfitting of the developed models. Furthermore, the validity and consistency of the prediction results are ensured with the correct use of statistical measures like p-value and cross-validation which were missing in previous studies. To compare the performances of the models developed in this study with the prior ones existing in literature, all models were applied on the test data set and their performances are evaluated in terms of the resulting root mean squared error (RMSE) values and coefficient of determination (R²). Accordingly, the models developed in this study demonstrate superior prediction capabilities compared to all of the prior studies. Moreover, to facilitate the use of machine learning algorithms for prediction purposes, entire source code prepared for this study and the collected data set are provided as supplements of this study.     

关于下沉时间函数模型的探讨

简要介绍了地表下沉时间函数的几种模型,对目前应用较多的时间函数模型进行了对比分析,指出了各个模型的特点及其适用性,从而建立正确的地表下沉时间函数模型,推动环境工程研究工作的发展。     

体育场看台结构地震作用的计算

体育场看台结构为在一向基本对称,而在另一向不对称的框架结构。过去常用平面框架计算模型来计算这类结构的地震作用。本文结合即将颁布的新建筑抗震设计规范讨论了不对称结构地震作用计算的方法,分析了几种不同计算模型的区别。并用计算实例,闸明了用空间结构计算模型计算此类结构的必要性和采用考虑扭转地震效应的空间协同计算模型的可行性。     

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.     

Probabilistic models for mechanical properties of concrete,reinforcing steel and pre-stressing steel

This paper presents probabilistic models for mechanical properties of pre-cast and cast-on-site concrete as well as of reinforcing and pre-stressing steel. An extended review of models available in the literature is made and new probabilistic models are developed based on a significant amount of data collected by the authors. New probabilistic models are proposed for concrete ultimate strength (separately for precast and cast-in-place concretes), for yield and ultimate strength of reinforcing steel and for proportionality limit and ultimate strength of pre-stressing steel. The new models account for a recent improvement of production and are more appropriate for the probabilistic assessment of modern concrete structures then the models available in the literature.