大空间空调系统控制耦合问题分析及对策

研究了大空间大面积空调系统设备的集群协调控制问题，分析了楼宇自动化设备及计算机网络的应用体系结构，从信息理论的角度提出了利用大空间空调系统的测量信息冗余提高动态控制性能的措施，并介绍了采用人工神经网络控制器进行信息融合进而实现协调控制的对策或方法。     

Nonparametric Identification of a Building Structure from Experimental Data Using Wavelet Neural Network

Abstract:   This study presents a wavelet neural network-based approach to dynamically identifying and modeling a building structure. By combining wavelet decomposition and artificial neural networks (ANN), wavelet neural networks (WNN) are used for solving chaotic signal processing. The basic operations and training method of wavelet neural networks are briefly introduced, since these networks can approximate universal functions. The feasibility of structural behavior modeling and the possibility of structural health monitoring using wavelet neural networks are investigated. The practical application of a wavelet neural network to the structural dynamic modeling of a building frame in shaking tests is considered in an example. Structural acceleration responses under various levels of the strength of the Kobe earthquake were used to train and then test the WNNs. The results reveal that the WNNs not only identify the structural dynamic model, but also can be applied to monitor the health condition of a building structure under strong external excitation.     

智能建筑计算机网络的实验教学

针对智能建筑工程建筑智能化系统设计选型的需要 ,总结出了相应的计算机网络知识结构 ,包括数据通信原理、计算机网络原理、信息网络、控制网络、网络集成、网络计算模式、网络操作系统、网络安全及网络管理等 9个领域。设计了 7个计算机网络相关实验 ,并给出了实验教学大纲摘要     

基于MATLAB神经网络的高层建筑结构选型研究

随着土木工程技术的发展,结构选型在高层建筑结构设计中的重要性越来越明显。但是由于高层建筑结构选型是一个非常复杂的问题,本文提出应用MATLAB神经网络方法对高层建筑进行结构选型。并用MATLAB语言编制了人工神经网络高层建筑结构选型专家系统使选型过程简单明了。结果表明此方法可行,可以帮助设计人员选择恰当的结构型式。     

A neural network based system for predicting earthmoving production

An artificial neural network based system (NN earth) is developed for construction practitioners as a simple tool for predicting earthmoving operations, which are modelled by back propagation neural networks with four expected parameters and seven affecting factors. These networks are then trained using the data patterns obtained from simulation because there are insufficient data available from industrial sources. The trained network is then incorporated as the computation engine of NN earth. To engender confidence in the results of neural computation, a validation function is implemented in NN earth to allow the user to apply the engine to historic cases prior to applying it to a new project. An equipment database is also implemented in NN earth to provide default information, such as internal cost rate, fuel cost, and operator's cost. User interfaces are developed to facilitate inputting project information and manipulating the system. The major functions and use of NN earth are illustrated in a sample application. In practice, NN earth can assist the user either in selecting a crew to minimize the unit cost of a project or in predicting the performance of a given crew.     

Neural Network Approaches to Aid Simple Truss Design Problems

Abstract: Diverse problems in engineering may be solved accurately with computers. In structural engineering, many solution techniques exist. Over the past few years, neural networks have evolved as a new computing paradigm, and many engineering applications have been studied. This paper describes configuring and training of a neural network for a truss design application and explores the possible roles for neural networks in structural design problems. The specific problem considered is a simple truss design where, given a geometry and a loading, economical cross-sectional areas of all the members are to be selected. For this problem, a two-layer neural network is trained using the back-propagation algorithm with patterns representing optimal designs for diverse loading conditions. The performance of the trained neural network is evaluated with a sample problem.     

Introduction

A good formwork system enables speedy completion of the concrete structure, following which other subsequent trades can be started. However, the current intuitive judgment approach in the selection of formwork systems cannot assure an optimal and consistent result. Artificial neural networks may improve the selection process. Formwork represents a significant part of the cost of concrete structure construction. Most subsequent trades including internal finishing and external cladding depend on the completion of the building structure. A suitable formwork system is thus crucial for maintaining the smooth flow of the various trades and a proper working sequence of various work activities. Based on data collected from a previous study, it is clear that the key factors affecting the selection of a relevant formwork system include building height and structural system, concrete finish, site conditions, availability of equipment and building shape. Neural network models are developed for the selection of vertical formwork systems using the architecture of the probabilistic neural network (PNN) model. A case study verifies the validity of this approach.     

基于LM方法的神经网络用于线性时不变系统的广义状态转移矩阵的识别

用神经网络方法对系统状态转移矩阵进行识别的研究,首先建立结构动力学运动方程的状态转移矩阵的识别与神经网络的权值矩阵识别间的相互等价关系,其次利用神经网络功能强大的并行运算能力和丰富的学习功能以单层线性神经网络来识别线性时不变系统的广义状态转移矩阵以获取结构的状态参数.最后通过识别获得的神经网络仿真系统结构在任意激励下的响应,可用以抗震性能评估或振动控制.     

The application of neural networks to rock engineering systems (RES)

This paper proposes a new approach for applying neural networks in Rock Engineering Systems (RES) based on the learning abilities of neural networks. By considering the analysis of the coding methods for the interaction matrix in RES and the learning processes of neural networks such as the Back Propagation (BP) method, neural networks can provide a useful mapping from system inputs to system outputs for rock engineering, so that the influence of inputs on outputs can be obtained. Then the results of the neural network analysis can be presented in a similar way to the global interaction matrix used in RES to present the fully-coupled system results. The neural network procedures are explained first, with illustrative demonstrations for simultaneous equations. Then, the link with the RES type of analysis is explained, together with some demonstration examples for rock engineering data sets. The specific analysis procedure is presented and then wider rock engineering examples are given relating to the characteristics of rock masses and engineering parameters. The main presentation tools used in this neural network approach are the Relative Strength Effect (RSE) and the Global Relative Strength Effect (GRSE) matrix. There is discussion of the value of this approach and an indication of the likely areas of future development.     

人工神经网络在建筑工程项目管理中的应用

简要介绍人工神经网络的基本原理,综述了人工神经网络在建筑工程项目管理的造价预测、项目管理绩效评价、招投标等方面的应用及ANN在该领域应用中存在的不足之处,并对人工神经网络在建筑工程项目管理中的应用提出了建议。     

Unraveling local relationship patterns in project networks: A network motif approach

Characterizing relationship patterns among organizations in project networks has become a hot issue in the emerging field of project network management. Many researchers focus on social network centralities (e.g. degree, betweenness, and closeness) and global network measures (e.g., network density, degree distribution, and clustering coefficient) to investigate the relationship patterns in project networks. However, little is known about the local relationship patterns, i.e., relational structures among a small number of project organizations. In this paper, we will construct the contractors’ collaboration networks by mapping collaborative relationships between hundreds of contractors within the electronic database of National Quality Award Projects (NQAP) of China. The research purpose is to make use of the normative network motif approach in characterizing the local relationship patterns of project networks and in demonstrating how such relationship patterns evolve, thus contributing to the field of project network research. We find two distinct types of local collaboration patterns, i.e., motif and antimotif in the NQAP collaboration network. The motifs are the evolutionary favored patterns induced by macro network clustering tendency and individual structural embeddedness. Moreover, the evolution of the NQAP collaboration network shows a phase transition characterized by the distribution of the local collaboration patterns. The motif approach is expected to help project organizations devise appropriate strategies to select partners as well as to help governors establish effective strategies of project network governance.     

Neural Networks in Structural Engineering

In the past few years literature on computational civil engineering has concentrated primarily on artificial intelligence (Al) applications involving expert system technology. This article discusses a different Al approach involving neural networks. Unlike their expert system counterparts, neural networks can be trained based on observed information. These systems exhibit a learning and memory capability similar to that of the human brain, a fact due to their simplified modeling of the brain's biological function. This article presents an introduction to neural network technology as it applies to structural engineering applications. Differing network types are discussed. A back-propagation learning algorithm is presented. The article concludes with a demonstration of the potential of the neural network approach. The demonstration involves three structural engineering problems. The first problem involves pattern recognition; the second, a simple concrete beam design; and the third, a rectangular plate analysis. The pattern recognition problem demonstrates a solution which would otherwise be difficult to code in a conventional program. The concrete beam problem indicates that typical design decisions can be made by neural networks. The last problem demonstrates that numerically complex solutions can be estimated almost instantaneously with a neural network.     

An applied artificial intelligence approach towards assessing building performance simulation tools

With the development of modern computer technology, a large amount of building energy simulation tools is available in the market. When choosing which simulation tool to use in a project, the user must consider the tool's accuracy and reliability, considering the building information they have at hand, which will serve as input for the tool. This paper presents an approach towards assessing building performance simulation results to actual measurements, using artificial neural networks (ANN) for predicting building energy performance. Training and testing of the ANN were carried out with energy consumption data acquired for 1 week in the case building called the Solar House. The predicted results show a good fitness with the mathematical model with a mean absolute error of 0.9%. Moreover, four building simulation tools were selected in this study in order to compare their results with the ANN predicted energy consumption: Energy_10, Green Building Studio web tool, eQuest and EnergyPlus. The results showed that the more detailed simulation tools have the best simulation performance in terms of heating and cooling electricity consumption within 3% of mean absolute error.     

Postdisaster image‐based damage detection and repair cost estimation of reinforced concrete buildings using dual convolutional neural networks

Reinforced concrete (RC) buildings are commonly used around the world. With recent earthquakes worldwide, rapid structural damage inspection and repair cost evaluation are crucial for building owners and policy makers to make informed risk management decisions. To improve the efficiency of such inspection, advanced computer vision techniques based on convolutional neural networks have been adopted in recent research to rapidly quantify the damage state (DS) of structures. In this article, an advanced object detection neural network, named YOLOv2, is implemented, which achieves 98.2% and 84.5% average precision in training and testing, respectively. The proposed YOLOv2 is used in combination with the classification neural network, which improves the identification accuracy for critical DS of RC structures by 7.5%. The improved classification procedures allow engineers to rapidly and more accurately quantify the DSs of the structure, and also localize the critical damage features. The identified DS can then be integrated with the state‐of‐the‐art performance evaluation framework to quantify the financial losses of critical RC buildings. The results can be used by the building owners and decision makers to make informed risk management decisions immediately after the strong earthquake shaking. Hence, resources can be allocated rapidly to improve the resiliency of the community.     

Nonlinear Identification of Dynamic Systems Using Neural Networks

A neural-network-based method is proposed for the modeling and identification of a discrete-time nonlinear hysteretic system during strong earthquake motion. The learning or modeling capability of multilayer neural networks is explained from the mathematical point of view. The main idea of the proposed neural approach is explained, and it is shown that a multilayer neural network is a general type of NARMAX model and is suitable for the extreme nonlinear input-output mapping problems. Numerical simulation of a three-story building and a real structure (a bridge in Taiwan) subjected to several recorded earthquakes are used here to demonstrate the proposed method. The results illustrate that the neural network approach is a reliable and feasible method.     

Analysis of Bridge Condition Rating Data Using Neural Networks

Currently bridges are evaluated using either a visual inspection process or a detailed structural analysis. When bridge evaluation is conducted by a visual inspection, a subjective rating is assigned to a bridge component. With analytical evaluation, the rating is computed based on the load applied and the resistance of the bridge component. There have been several attempts to correlate the subjective rating to the analytical rating. The conventional statistical analyses, as well as methods based on fuzzy logic, have not been very successful in providing a clear relationship between the two rating systems. This paper describes the application of neural network systems in developing the relation between subjective ratings and bridge parameters as well as that between subjective and analytical ratings. It is shown that neural networks can be trained and used successfully in estimating a rating based on bridge parameters. The specific application problem for railroad bridges in the commuter rail system in the Chicago metropolitan area is presented. The study showed that a successful training of a network can be achieved, especially if the input data set contains parameters with a diverse combination of intercorrelation coefficients. When the relationship between the bridge subjective rating and bridge parameters was investigated, the network had a prediction rating of about 73%. The study also investigated the relation between the subjective and analytical rating. In this case, the prediction rate was about 43%. Compared with conventional statistical methods and the fuzzy‐logic approach, the neural network system had a much better performance ratio in establishing the relation between the bridge rating and bridge parameters.     

周边固支钢筋混凝土异型无梁楼盖有限元分析

周边固支筋混凝土异型无梁楼盖是一种特殊的结构形式,结合有限元分析工具对影响结构整体性能的因素进行仿真分析,得到无梁楼盖在外均布荷载作用下性能变化的一般特征,并同等代框架法计算得到的结果进行比较验证,从而为实际工程设计提供依据。     

iopeNet系统施工导入

随着计算机、网络和通讯等技术的进步和建筑智能化快速发展,智能建筑系统的技术也更加先进。本文主要从iopeNet系统的现场设备安装、以及系统布线进行了一系列的阐述,使读者能够更深入了解iopeNet系统的设计、施工方法,并例举了项目实施中的一些关键问题及注意事项。     

Predicting chaotic time series of PM10 concentration using artificial neural network

Respirable particulate matter (PM10) concentration at one residential site in Delhi, India was predicted using the neural network approach. The concepts of chaotic systems theory were utilized to build the neural network model. The embedding dimension was estimated to provide the inputs to the neural network. The model evaluation results indicated the importance of noise reduction before selecting the embedding dimension of the time series. The selection of a proper embedding dimension is considered to be essential for obtaining reliable predictions. The model’s performance shows the capability of neural networks in modelling the chaotic time series.     

On the energy consumption in residential buildings

A neural network approach is used in the present study for modelling and estimating the energy consumption time series for a residential building in Athens, using as inputs several climatic parameters.The hourly values of the energy consumption, for heating and cooling the building, are estimated for several years using feed forward backpropagation neural networks. Various neural network architectures are designed and trained for the output estimation, which is the building’s energy consumption. The results are tested with extensive sets of non-training measurements and it is found that they correspond well with the actual values.Furthermore, “multi-lag” output predictions of ambient air temperature and total solar radiation are used as inputs to the neural network models for modelling and predicting the future values of energy consumption with sufficient accuracy.