Numerical modeling of masonry-infilled RC frames subjected to seismic loads

The behavior of masonry-infilled reinforced concrete frames under cyclic lateral loading is complicated because a number of different failure mechanisms can be induced by the frame-infill interaction, including brittle shear failures of the concrete columns and damage of the infill walls. In this study, nonlinear finite element models have been used to simulate the behavior of these structures. Diffused cracking and crushing in concrete and masonry are described by a smeared-crack continuum model, while dominant cracks as well as masonry mortar joints are modeled with a cohesive crack interface model. The interface model adopts an elasto-plastic formulation to describe the mixed-mode fracture of concrete and masonry. The model accounts for cyclic crack opening and closing, reversible shear dilatation, and joint compaction due to damage. The constitutive models have been validated with experimental data and successfully applied to the dynamic analysis of a three-story, two-bay, masonry-infilled, non-ductile, reinforced concrete frame tested on a shake table. The results have demonstrated the capabilities of the finite element method in capturing the nonlinear cyclic load–displacement response and failure mechanisms of the structure, and indicated the important contribution of infill walls to the seismic resistance of a non-ductile reinforced concrete frame.     

From the ground up: building a high-resolution seismic model

The author discusses two applications of computer animation: forensic animation in court cases; and high resolution seismic models. The forensic application concerns a case study of human skull identification using computer graphics and modelling of the crime scene. The seismic application concerns a team of seismologists that used a cluster of four IBM RS/6000s to create a detailed 3D model of the faults and structures under California's shaky San Francisco Bay Area     

Numerical problems in modelling of collision in sliding systems subjected to seismic excitations

The study addresses collision in sliding systems subjected to seismic excitations. The collision is modelled according to the impact laws of the mechanics of particles using coefficient of restitution to account for energy losses (Newton's hypothesis). An analytical solution in a small time interval after the collision is constructed viewing the sliding velocity. When constructing numerical solutions, it is assumed that the friction force does not change its sign within one step of integration. If at end of the time step, velocity with an opposite sign is calculated, the obtained solution is incorrect, because the result contradicts the accepted constant sign of the friction force during the time step. To avoid these problems expressions are derived for the magnitude of the time step in which a mathematically correct solution will have place. Recommendations are formulated for numerical simulation of collision in sliding systems subjected to seismic excitations. The obtained correct numerical solutions using the Coulomb model are compared with numerical results from the velocity model of friction forces. It is shown that the velocity model provides the possibility to avoid automatically the above numerical problems by use of its correctness condition.     

The task of building a trusted computing environment on the Intel hardware platform

The problem of building a trusted computing environment on the basis of modern computing devices has been considered. The main features of software execution at different levels of privilege have been indicated. The architecture of the Intel ME subsystem has been considered. The potential threats of Intel ME technology have been highlighted, and possible ways to eliminate them have been given.     

Application of a novel IWO to the design of encoding sequences for DNA computing

Encoding and processing information in DNA-, RNA- and other biomolecule-based devices is an important requirement for DNA based computing with potentially important applications. To make DNA computing more reliable, much work has focused on designing the good DNA sequences. However, this is a bothersome task as encoding problem is an NP problem. In this paper, a new methodology based on the IWO algorithm is developed to optimize encoding sequences. Firstly, the mathematics models of constrained objective optimization design for encoding problems based on the thermodynamic criteria are set up. Then, a modified IWO method is developed by defining the colonizing behavior of weeds to overcome the obstacles of the original IWO algorithm, which cannot be applied to discrete problems directly. The experimental results show that the proposed method is effective and convenient for the user to design and select effective DNA sequences in silicon for controllable DNA computing.     

基于小波消噪的非等间隔加权G(1,1)模型在建筑物沉降预测中的应用

建筑物沉降监测数据序列受观测条件影响大多呈现出数据量少、光滑性差、含噪声和非等时距等特点。在小波除噪和等间隔灰色GM(1,1)模型的基础上建立了非等间隔G(1,1)预测模型。在建模数据中,早期观测数据对模型预测精度贡献小(甚至有拖累现象),近期观测数据对模型预测精度贡献较大,故在建模时引入了权重矩阵以此提高模型的预测精度。实证分析表明所建非等间隔加权灰色GM(1,1)模型具有较高的预测精度,可用以工程实践。     

Optimal synthesis method for transmission tower truss structures subjected to static and seismic loads

In this study, an efficient optimal synthesis method for determining the optimum solutions for the structural shape, cross-sectional dimensions, and material type of all member elements of large-scale transmission tower truss structures subjected to static and seismic loads is presented. The method is developed by using the dual method, the response spectrum method, suboptimization techniques, and a two-stage optimization process. The example of a cost-minimization problem for a 218-bar transmission tower truss that considers not only the material costs but also the cost of land as objective functions is presented to demonstrate the rigorousness, efficiency, and reliability of the proposed method.     

Application of economic MPC to the energy and demand minimization of a commercial building

This paper presents an application case study of an economic model predictive control (EMPC) method for optimizing the building demand and energy cost under the time-of-use price policy. The control strategy is comprised of an economic objective function that accounts for the combination of energy and demand costs with a time-of-use rate structure, a dynamic thermal process and power model of the building thermal mass dynamics, and a set of constraints to ensure the building is operated properly. The optimization is a min–max optimization problem and is converted to a linear program. The EMPC method is implemented in a commercial office building located in Milwaukee, Wisconsin, USA. An internet-based control architecture is developed to carry out tests with the EMPC controller at a remote location. The test results show that the EMPC strategy is capable of shifting the peak demand to off-peak hours and reducing energy costs compared to a baseline case for the building.     

Application of soft computing to predict blast-induced ground vibration

In this study, an attempt has been made to evaluate and predict the blast-induced ground vibration by incorporating explosive charge per delay and distance from the blast face to the monitoring point using artificial neural network (ANN) technique. A three-layer feed-forward back-propagation neural network with 2-5-1 architecture was trained and tested using 130 experimental and monitored blast records from the surface coal mines of Singareni Collieries Company Limited, Kothagudem, Andhra Pradesh, India. Twenty new blast data sets were used for the validation and comparison of the peak particle velocity (PPV) by ANN and conventional vibration predictors. Results were compared based on coefficient of determination and mean absolute error between monitored and predicted values of PPV.     

Application of neural network computing to thermal non-destructive evaluation

A methodological study on the use of neural networks for defect characterisation by means of a thermal method is presented. Neural networks are used here as defect classifiers, based on the infrared emission of the target object after heating. In this kind of application, there is a high degree of uncertainty in defect class boundaries due to several factors, such as the noise in the measurement, the uneven heating of the target object and the anisotropies in its thermal conductivity. For this reason, the classical 1 of N coding scheme during training did not provide satisfactory results. Much better results have instead been obtained using a smoother activation function for the output units during training. The non-destructive evaluation of material using neural networks proved extremely satisfactory, especially when compared to the classical procedures of thermographic analysis.     

Application of parallel computing to stochastic parameter estimation in environmental models

Parameter estimation or model calibration is a common problem in many areas of process modeling, both in on-line applications such as real-time flood forecasting, and in off-line applications such as the modeling of reaction kinetics and phase equilibrium. The goal is to determine values of model parameters that provide the best fit to measured data, generally based on some type of least-squares or maximum likelihood criterion. Usually, this requires the solution of a non-linear and frequently non-convex optimization problem. In this paper we describe a user-friendly, computationally efficient parallel implementation of the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm for stochastic estimation of parameters in environmental models. Our parallel implementation takes better advantage of the computational power of a distributed computer system. Three case studies of increasing complexity demonstrate that parallel parameter estimation results in a considerable time savings when compared with traditional sequential optimization runs. The proposed method therefore provides an ideal means to solve complex optimization problems.     

边缘计算和云计算在智慧城市建设中的应用

文章主要对边缘计算、云计算的定义和特点及智慧城市的概念进行简单阐释,结合城市建设情况,分析边缘计算和云计算在智慧城市建设中应用的必要性,并提出二者的具体应用,希望充分挖掘边缘、云计算技术的优势,在二者相互补充的过程中实现智慧城市的建设,为实际智慧城市建设工作提供参考依据。     

Numerical and experimental evaluation of seismic capacity of high-rise steel buildings subjected to long duration earthquakes

Occurrences of large earthquakes having a magnitude larger than eight along subduction zones have been reported worldwide. Due to large number of load reversals the effect of cumulative damage on structural components due to deterioration becomes critical for steel buildings of old construction but may also become critical for buildings designed based on current seismic provisions. A state-of-the-art analytical model that simulates component deterioration and fracture due to low cycle fatigue has been developed and implemented in the OpenSees computational framework. The model serves for seismic evaluation of steel moment frame structures subjected to long duration records. The effectiveness of the numerical model in quantification of the seismic capacity of high rise steel structures is demonstrated through validation with a full scale shaking table test of a high-rise steel building subjected to a long duration record at the world’s largest shaking table facility (E-Defense). Limitations of the proposed numerical model are also discussed.     

Application of parallel computing methods to the processing of sea-surface radar imagery

The capabilities of advanced parallel computing methods in processing and identification of seasurface radar imagery are investigated. Their application for the real-time analysis of large-area images is justified. Based on the methods developed by the authors of this paper, estimates are given for the mass, dimensions, and power consumption of a special-purpose onboard processor needed for processing sea-surface radar imagery.     

Nonlinear seismic response evaluation of tunnel form building structures

The occurrences of (M_w=7.4) Kocaeli and (M_w=7.1) Duzce earthquakes in Turkey in 1999 once again demonstrate the nondamaged and high performance conditions of RC shear wall dominant structures commonly built by using the tunnel form technique. This study presents their seismic performance evaluation based on the nonlinear pushover analyses of two case studies. The contribution of transverse walls and slab-wall interaction during the 3D action, the effects of 3D and 2D modeling on the capacity-demand relation, as well as diaphragm flexibility, torsion and damping effects were investigated. An effort was spent to develop a shell element having closing-opening and rotating crack capabilities. This study shows that the applied methodology has a considerable significance for predicting the actual capacity, failure mechanism, and evaluation of the seismic response of tunnel form buildings.     

云计算技术在STB中的应用研究

云计算技术已在三网融合中广泛应用,文中通过分析云计算技术和智能数字机顶盒的发展趋势,阐明了三网融合可持续发展必须要建立在云计算技术的基础上的观点,同时,对云计算下构建三网系统平台进行了分析研究,并对未来云电视机顶盒的应用技术进行了探讨。     

Application of DNA computing in graph theory

Although DNA computing was rapidly discarded when researchers realized some of the drawbacks related to it, but by computer simulation of molecular reaction it may be implemented in silico by computer architectures offering massive parallelism. In this review, we describe sticker algorithm for several famous graph problem. Presented algorithms have polynomial time complexity.     

智能建筑室内环境分布式可计算WSN任务调度研究

针对智能建筑室内环境下并行计算的动态任务调度问题,构建了基于分布式CPS思想的无线传感器网络(WSN)模型,并分别设计了基于可计算复杂性的任务分配策略和基于动态调度算法的任务调度策略。通过先将任务分配成若干个子任务,采用多带图灵机输入任务,由合适的计算节点进行计算,形成有向无环图,再按调度优先级排列任务,形成任务调度序列表,依序处理任务,从而达到了将任务分配、调度和执行相结合的目的。实验结果表明该策略可有效减少智能建筑室内环境分布式可计算WSN分布运行时任务之间的通讯时间和等待时间,同时提高了任务调度的成功率,最终优化系统的运行效率。     

可信计算在VPN中的应用

对虚拟专用网VPN进行了研究。VPN使用户远程办公成为可能,但是VPN不能认证主机的配置,入侵者通过有VPN访问权限的主机获得非法的访问权限,使得终端不安全,同时相应的使网络接入也不安全。可以利用可信计算技术解决这些问题,其中可信平台模块通过绑定密钥认证VPN完整性,而可信网络连接认证网络连接安全性,以确保终端、网络接入和通信的安全可信。     

Application of a radial basis function artificial neural network to seismic data inversion

We investigate here the performance and the application of a radial basis function artificial neural network (RBF-ANN) type, in the inversion of seismic data. The proposed structure has the advantage of being easily trained by means of a back-propagation algorithm without getting stuck in local minima. The effects of network architectures, i.e. the number of neurons in the hidden layer, the rate of convergence and prediction accuracy of ANN models are examined. The optimum network parameters and performance were decided as a function of testing error convergence with respect to the network training error. An adequate cross-validation test is run to ensure the performance of the network on new data sets. The application of such a network to synthetic and real data shows that the inverted acoustic impedance section was efficient.