Application of user preference mining algorithms based on data mining and social behavior in brand building

Small and medium-sized enterprises currently suffer from a lack of branding.Therefore,to further promote their active branding,this study proposes a user preference mining algorithm based on data mining and social behavior.Employing this algorithm to study the degree of users’brand preference can provide data support for enterprises’brand building.The experimental results showed that the proposed algorithm outperforms previous algorithms in terms of performance,convergence,and accuracy.The area under the curve reached 0.953,indicating highly authentic output results with extremely high realism.In actual simulation experiments,its prediction results for the user’s brand preference index are accurate,with an error of only 0.11,and the algorithm has extremely high ratings among industry insiders.In conclusion,the user-preference mining algorithm based on data mining and social behaviors suggested in this study plays a better role in promoting an enterprise’s brand building.It can help the enterprise know the level of consumer preference for its brand;accordingly,it can determine the shortcomings in,provide effective and accurate data support for,and thereby promote its brand building.

Digital Restoration of Historical Buildings by Integrating 3D PC Reconstruction and GAN Algorithm

Historical architecture is an important carrier of cultural and historical heritage in a country and region,and its protection and restoration work plays a crucial role in the inheritance of cultural heritage.However,the damage and destruction of buildings urgently need to be repaired due to the ancient age of historical buildings and the influence of natural environment and human factors.Therefore,an artificial intelligence repair technology based on three-dimensional(3D)point cloud(PC)reconstruction and generative adversarial networks(GANs)was proposed to improve the precision and efficiency of repair work.First,in-depth research on the principles and algorithms of 3D PC data processing and GANs should be conducted.Second,a digital restoration frameworkwas constructed by combining these two artificial intelligence technologies to achieve precise and efficient restoration of historical buildings through continuous adversarial learning processes.The experimental results showed that the errors in the restoration of palace buildings,defense walls,pagodas,altars,temples,and mausoleums were 0.17,0.12,0.13,0.11,and 0.09,respectively.The technique can significantly reduce the error while maintaining the high-precision repair effect.This technology with artificial intelligence as the core has excellent accuracy and stability in the digital restoration.It provides a new technical means for the digital restoration of historical buildings and has important practical significance for the protection of cultural heritage.

High-rise building fire pre-warning model based on the support vector regression

Aiming at reducing the deficiency of the traditional fire pre-warning algorithms and the intelligent fire pre-warning algorithms such as artificial neural network,and then to improve the accuracy of fire prewarning for high-rise buildings,a composite fire pre-warning controller is designed according to the characteristic（ nonlinear,less historical data,many influence factors）,also a high-rise building fire pre-warning model is set up based on the support vector regression（ SV R）. Then the wood fire standard history data is applied to make empirical analysis. The research results can provide a reliable decision support framework for high-rise building fire pre-warning.

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.

Machine learning prediction models for ground motion parameters and seismic damage assessment of buildings at a regional scale

This study examines the feasibility of using a machine learning approach for rapid damage assessment of rein-forced concrete(RC)buildings after the earthquake.Since the real-world damaged datasets are lacking,have limited access,or are imbalanced,a simulation dataset is prepared by conducting a nonlinear time history analy-sis.Different machine learning(ML)models are trained considering the structural parameters and ground motion characteristics to predict the RC building damage into five categories:null,slight,moderate,heavy,and collapse.The random forest classifier(RFC)has achieved a higher prediction accuracy on testing and real-world damaged datasets.The structural parameters can be extracted using different means such as Google Earth,Open Street Map,unmanned aerial vehicles,etc.However,recording the ground motion at a closer distance requires the installation of a dense array of sensors which requires a higher cost.For places with no earthquake recording station/device,it is difficult to have ground motion characteristics.For that different ML-based regressor models are developed utilizing past-earthquake information to predict ground motion parameters such as peak ground acceleration and peak ground velocity.The random forest regressor(RFR)achieved better results than other regression models on testing and validation datasets.Furthermore,compared with the results of similar research works,a better result is obtained using RFC and RFR on validation datasets.In the end,these models are uti-lized to predict the damage categories of RC buildings at Saitama University and Okubo Danchi,Saitama,Japan after an earthquake.This damage information is crucial for government agencies or decision-makers to respond systematically in post-disaster situations.

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.

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.

基于自适应蚁群算法的建筑工程消防疏散路径规划方法研究

随着城市化进程的加速,大型建筑如雨后春笋般涌现,对建筑的消防安全提出了更高的要求。在火灾等紧急情况下,如何迅速、安全地疏散人员,成为建筑设计中不可忽视的重要环节。近年来,随着智能算法的发展,利用算法进行建筑消防疏散路径规划已成为研究热点。研究提出一种基于自适应蚁群算法的建筑工程消防疏散路径规划方法。该方法通过综合考虑建筑内部的空间布局、火灾状况、人员疏散的影响因素,实现了高效、安全的疏散路径规划。通过与传统蚁群算法进行对比实验,验证了该方法在收敛速度和路径优化方面的优越性。

Feasibility Study of Parameter Identification Method Based on Symbolic Time Series Analysis and Adaptive Immune Clonal Selection Algorithm

The feasibility of a parameter identification method based on symbolic time series analysis (STSA) and the adaptive immune clonal selection algorithm (AICSA) is studied. Data symbolization by using STSA alleviates the effects of harmful noise in raw acceleration data. The effect of the parameters in STSA is theoretically evaluated and numerically verified. AICSA is employed to minimize the error between the state sequence histogram (SSH) that is transformed from raw acceleration data by STSA. The proposed methodology is evaluated by comparing it with AICSA using raw acceleration data. AICSA combining STSA is proved to be a powerful tool for identifying unknown parameters of structural systems even when the data is contaminated with relatively large amounts of noise.