A novel control strategy for reproducing the floor motions of high-rise buildings by earthquake-simulating shake tables

To enable the experimental assessment of the seismic performance of full-scale nonstructural elements with multiple engineering parameters(EDPs),a three-layer testbed named Nonstructural Element Simulator on Shake Table(NEST)has been developed.The testbed consists of three consecutive floors of steel structure.The bottom two floors provide a space to accommodate a full-scale room.To fully explore the flexibility of NEST,we propose a novel control strategy to generate the required shake table input time histories for the testbed to track the target floor motions of the buildings of interest with high accuracy.The control strategy contains two parts:an inverse dynamic compensation via simulation of feedback control systems(IDCS)algorithm and an offline iteration procedure based on a refined nonlinear numerical model of the testbed.The key aspects of the control strategy were introduced in this paper.Experimental tests were conducted to simulate the seismic responses of a full-scale office room on the 21^(st)floor of a 42-story high-rise building.The test results show that the proposed control strategy can reproduce the target floor motions of the building of interest with less than 20%errors within the specified frequency range.

A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems

Integrated building design is inherently a multi-objective optimization problem where two or more conflicting objectives must be minimized and/or maximized concurrently.Many multi-objective optimization algorithms have been developed;however few of them are tested in solving building design problems.This paper compares performance of seven commonly-used multi-objective evolutionary optimization algorithms in solving the design problem of a nearly zero energy building(n ZEB) where more than 1.610 solutions would be possible.The compared algorithms include a controlled non-dominated sorting genetic algorithm witha passive archive(p NSGA-II),a multi-objective particle swarm optimization(MOPSO),a two-phase optimization using the genetic algorithm(PR_GA),an elitist non-dominated sorting evolution strategy(ENSES),a multi-objective evolutionary algorithm based on the concept of epsilon dominance(ev MOGA),a multi-objective differential evolution algorithm(sp MODE-II),and a multi-objective dragonfly algorithm(MODA).Several criteria was used to compare performance of these algorithms.In most cases,the quality of the obtained solutions was improved when the number of generations was increased.The optimization results of running each algorithm20 times with gradually increasing number of evaluations indicated that the PR_GA algorithm had a high repeatability to explore a large area of the solution-space and achieved close-to-optimal solutions with a good diversity,followed by the p NSGA-II,ev MOGA and sp MODE-II.Uncompetitive results were achieved by the ENSES,MOPSO and MODA in most running cases.The study also found that 1400-1800 were minimum required number of evaluations to stabilize optimization results of the building energy model.

Intelligent Building Load Scheduling Based on Multi-Objective Multi-Verse Algorithm

In the multi-objective of intelligent building load scheduling, aiming at the problem of how to select Pareto frontier scheme for multi-objective optimization algorithm, the current optimal scheme mechanism combined with multi-objective multi-verse algorithm is used to optimize the intelligent building load scheduling. The update mechanism is changed in updating the position of the universe, and the process of correction coding is omitted in the iterative process of the algorithm, which reduces the com-putational complexity. The feasibility and effectiveness of the proposed method are verified by the optimal scheduling experiments of residential loads.

Review and Simulation of a Novel Formation Building Algorithm While Enabling Obstacle Avoidance

Technically, a group of more than two wheeled mobile robots working collectively towards a common goal are known as a multi-robot system. An increasing number of industries have implemented multi-robot systems to eliminate the risk of human injuries while working on hazardous tasks, and to improve productivity. Globally, engineers are continuously researching better, simple, and faster cooperative Control algorithms to provide a Control strategy where each agent in the robot formation can communicate effectively and achieve a consensus in their position, orientation and speed. This paper explores a novel Formation Building Algorithm and its global stability around a configuration vector. A simulation in MATLAB? was carried out to examine the performance of the Algorithm for two geometric formations and a fixed number of robots. In addition, an obstacle avoidance technique was presented assuming that all robots are equipped with range sensors. In particular, a uniform rounded obstacle is used to analyze the performance of the technique with the use of detailed geometric calculations.

Solving Job-Shop Scheduling Problems by Genetic Algorithms Based on Building Block Hypothesis

In this paper, we propose a new genetic algorithm for job-shop scheduling problems （JSP）. The proposed method uses the operation-based representation, based on schema theorem and building block hypothesis, a new crossover is proposed ： By selecting short, low order highly fit schemas to genetic operator, the crossover can exchange meaningful ordering information of parents effectively and can search the global optimization. Simulation results on MT benchmark problem coded by C ＋ ＋ show that our genetic operators are very powerful and suitable to job-shop scheduling problems and our method outperforms the previous GA-based approaches.

Optimum Energy Management of the Central Air-Conditioning System in Intelligent Buildings

An optimum energy saving scheduling strategy of the central air conditioning system in an intelligent building (IB) was proposed. Based on the system analysis a set of models of the central air conditioning system was established. The periodically autoregressive models (PARM) based on genetic algorithms (GA) were used to predict the next day’s cold load. The improved genetic algorithms (IGA) with stochastic real number coding were used to finish the optimum energy saving scheduling of the system. The simulation results for the building of the Liangmahe Plaza show that the proposed strategy can save energy up to about 24 5%.

DYNAMIC ANALYSIS OF DAMPER CONNECTED ADJACENT BUILDINGS UNDER EARTHQUAKE

A formulation of the multi degree of freedom equations of motion for damper connected adjacent multi story buildings under earthquake excitation is presented.The ground acceleration due to earthquake is regarded as a stochastic process,and a pseudo excitation algorithm in frequency domain is implemented in a computer program to handle the non orthogonal damping properties of the system.The effectiveness of joint dampers is then investigated in terms of the reduction of displacement,acceleration and shear force responses of adjacent buildings.

Active TLCGD control of plane asymmetric buildings under earthquake excitation

The sealed,tuned liquid column gas damper （TLCGD） with gas-spring effect extends the frequency range of application and efficiently increases the modal structural damping.Active tuned liquid column gas damper （ATLCGD） is developed for the vibration control of plane asymmetric buildings subjected to earthquake excitation,improving the performance of the passive control scheme.The active behaviour is obtained by adjusting the pressure at the end of the liquid column using a pressurised reservoir.The classical linear quadratic regulator （LQR） design is presented as a straightforward approach to optimal control.Numerical simulations indicate a significant vibration reduction of plane asymmetric buildings by active control within the strong motion of the dynamic response.

Design and development of safety evaluation system of buildings on a seismic field based on the network platform

This paper describes a set of on-site earthquake safety evaluation systems for buildings, which were developed based on a network platform. The system embedded into the quantitative research results which were completed in accordance with the provisions from Post-earthquake Field Works, Part 2： Safety Assessment of Buildings, GB18208.2 -2001, and was further developed into an easy-to-use software platform. The system is aimed at allowing engineering professionals, civil engineeing technicists or earthquake-affected victims on site to assess damaged buildings through a network after earthquakes. The authors studied the function structure, process design of the safety evaluation module, and hierarchical analysis algorithm module of the system in depth, and developed the general architecture design, development technology and database design of the system. Technologies such as hierarchical architecture design and Java EE were used in the system development, and MySQL5 was adopted in the database development. The result is a complete evaluation process of information collection, safety evaluation, and output of damage and safety degrees, as well as query and statistical analysis of identified buildings. The system can play a positive role in sharing expert post-earthquake experience and promoting safety evaluation of buildings on a seismic field.

Modeling and optimization of a multi-carrier renewable energy system for zero-energy consumption buildings

For the carbon-neutral,a multi-carrier renewable energy system(MRES),driven by the wind,solar and geothermal,was considered as an effective solution to mitigate CO2emissions and reduce energy usage in the building sector.A proper sizing method was essential for achieving the desired 100%renewable energy system of resources.This paper presented a bi-objective optimization formulation for sizing the MRES using a constrained genetic algorithm(GA)coupled with the loss of power supply probability(LPSP)method to achieve the minimal cost of the system and the reliability of the system to the load real time requirement.An optimization App has been developed in MATLAB environment to offer a user-friendly interface and output the optimized design parameters when given the load demand.A case study of a swimming pool building was used to demonstrate the process of the proposed design method.Compared to the conventional distributed energy system,the MRES is feasible with a lower annual total cost(ATC).Additionally,the ATC decreases as the power supply reliability of the renewable system decreases.There is a decrease of 24%of the annual total cost when the power supply probability is equal to 8%compared to the baseline case with 0%power supply probability.

Optimum Scheduling for the Chilled Water System of an Intelligent Building

The energy saving issue of chilled water system in an intelligent building is analyzed from the systematic point of view, and an optimum scheduling scheme which can save energy of the system facilities and satisfy the constraints of the real time cold loads and system running is also proposed. It can make the minimum cost of the system by optimizing the number of running chillers, running parameters and the distribution of real time loads of running chillers. The improved genetic algorithm is used in the optimum scheduling scheme. The computation results show that the building energy consumption can be decreased by about 10%.

Appropriate schemata and building blocks

Appropriate schemata as a novel concept to characterize building blocks are introduced, and then, the traits of appropriate schemata are presented. The effects of building blocks by search operators are analyzed. Hence, the experiments on RR-8X8 are employed to verify that appropriate schemata construct the building blocks. The validity of appropriate schemata and building blocks from the views of theory and practice is presented.

Structural form selection of the high-rise buildingwith the improved BP neural network

As civil engineering technology development,the structural form selection is more and more critical in design of high-rise buildings.However,structural form selection involves expertise knowledge and changes with the environment which makes the task arduous.An approach utilizing improved back propagation(BP)neural network optimized by the Levenberg-Marquardt(L-M)algorithm is proposed to extract the main controlling factors of structural form selection.Then,an intelligent expert system with artificial neural network is constructed to design high-rise buildings structure effectively.The experiment tests the model in 15 well-known architecture samples and get the prediction accuracy of 93.33%.The results show that the method is feasible and can help designers select the appropriate structural form.

IMAGE RENDERING FOR BUILDING CAD

This paper presents a realistic image synthesis system suitable to buildings. It includes anti-liasing, shadow testing, transparency processing and texture mapping. The Z-buffer algorithm is utilized for shading. A simplified light buffer is used for shadow preprocessing and ray-cast method is used for shadow test so that the computation is accelerated and high quality If images is gotten.

基于BIM技术与模拟退火算法的村镇轻钢框架结构智能设计方法

传统村镇住宅结构设计需要进行大量的人工试算与重复建模,而受制于建设成本,村镇住宅无法像城镇住宅一样通过设计师进行专业的结构设计与验算,其安全性与经济性均难以满足要求。为此,提出一种村镇轻钢框架结构智能设计方法,包括智能建模与智能优化两个环节。基于图层自动识别算法、光学字符识别技术、自适应分块算法提出村镇轻钢框架结构BIM智能建模方法,包括图层识别、轴文本数据提取、墙体轮廓提取等,智能建模结果基本满足实际工程要求。基于提出的两阶段模拟退火算法给出村镇轻钢框架结构的智能优化方法,优化速度较快,优化效果良好。通过实际工程案例对提出的智能设计方法进行验证,结果表明,提出的村镇轻钢框架结构智能设计方法具有可行性,与传统的人工设计方法相比,设计周期可缩短70%以上,材料用量、结构设计指标接近人工设计结果。

论政策共识构建中的算法支配:模式与维度

人类社会进入算法时代,政策共识构建无论是在科学化发展还是在民主化进程中都受到算法影响,当这种影响发展到一定程度时则会出现算法支配的现象。根据算法的自动化程度与相对于人类的独立性,政策共识构建中的算法支配可分为四种模式:工具性支配、平台式支配、博弈性支配、自动化支配。四种模式对政策共识构建的影响体现在不同层面,但都是从四个方面展开:通过代码设计对政策诉求进行标准化解读;对个体感知做出隐蔽控制以此编辑诉求内容;形成严密的审查体系对行为进行监控并做出引导;设定计算规则塑造共识构建的程序和方式。总体而言,政策共识构建的算法支配体现为技术支配与权力支配的混合,需要基于对算法的全面认知做出应对。

外挂墙板施工阶段碳排放的智能监测与优化

建筑行业的温室气体过量排放逐渐引起了人们的关注,且其施工阶段往往面临成本超标、工期延长和碳排放失控等问题,因此,建筑施工阶段碳排放的监测与多目标优化问题成为目前研究重点。为了有效解决上述问题,从物理层、计算层和交互层三部分提出了监测优化系统的框架;以Revit为二次开发平台、利用MySQL建立数据库,搭建了碳排放智能监测与优化平台;以外挂墙板构件施工过程为例,运用融入柔性理念的遗传算法寻找出使得“成本工期碳排放”综合目标最适合项目特征的工序执行模式组合及最优解。研究为装配式建筑施工阶段的碳排放监测与优化提供了一套科学、高效的集成化管理技术。

基于逆向云算法的同行评议评价模型建设探析

同行评议本质上依然是一种量化的主观定性评议,其基石是参与评议活动的每位评议者,只有做到对评议者的有效评估、监督、规范与约束,才能保证同行评议结果的客观和公正。但同行评议过程始终存在着不确定性,如何处理不确定性是同行评议需解决的难点问题。为此,在论述同行评议中评议者评价的必要性和当前发展概况的基础上,利用同行评议的评价数据分析评议者和评议影响因素,基于数据构建科学的同行评议评价模型。运用云模型理论,针对同行评议特点,选择符合正态分布统计性质、无确定度的逆向云生成算法,将方案分解为评议者评议评价与多任务协同评议评价2个层次,提出同行评议评价模型;并利用中国某省2018—2022年某类人才项目同行评议在技术、前瞻、产出、风险、经济和社会6个方向共18个评议指标的数据,通过该模型分类分层次计算出其云模型数字特征,根据数字特征构建云图和数字画像进行分析。结果表明:基于逆向云算法的同行评议评价模型对于评价评议者各方面的评议能力和整体评议效果、评价同行评议活动开展成效以及监管同行评议中的异常数据具有实操意义,能据此科学构建评议者个性化权限,能发挥评议者最大优势,并最大限度减小人为因素造成的误差,缓解当前同行评议评价面临的困境。

基于数值模拟的建筑设计优化研究综述

在双碳目标的背景下,对建筑物进行优化设计是实现节能减排的一个新途径;计算机技术的飞速发展,更使得建筑优化设计的效率有了很大的提升。为了更加全面地掌握基于数值模拟的优化技术在实际建筑中的应用现状,对优化研究的主要阶段进行了概括,归类分析了各类优化平台的优缺点,总结对比了各类优化算法的性能。分析发现,优化阶段主要分为预处理、优化、后处理3个阶段;优化工具根据开发目的分为三大类;常用的优化算法有:进化算法、无导数搜索算法、混合算法;并且当下的优化研究还有很大的空间值得探索与改进。

基于BIM的PC构件排样优化设计

针对PC构件排样与上游装配式建筑设计模型的协同效用问题,进行了基于BIM的PC构件排样优化设计研究。分析了装配式BIM中PC构件的组成元素,完成PC构件信息的提取;结合改进蚁群算法与动态剩余矩形算法对PC构件排样进行优化,采用遗传变异策略与路径选择机制改进算法后期易陷入局部最优的不足,经过多次优化迭代得出最优排样。某PC构件企业的生产排样实例验证了基于BIM的PC构件排样优化方案可实现上游装配式建筑设计模型与PC构件排样的协同效用,提高了排样效率。