基于模糊神经网络的建筑结构半主动控制研究

建立了采用MR阻尼器的基于模糊神经网络的半主动控制算法。该方法采用模糊神经网络离线训练辨识MR阻尼器在不同电压下的动力滞回特性，在此基础上提出一种基于最佳逼近LQG主动控制的智能半主动控制算法。模糊神经网络做在线控制时，从已辨识好的模糊规则库中自动选择最接近LQG控制力的控制电压，施加到MR阻尼器上形成控制力，有效地减小结构在地震作用下的响应。数值算例仿真验证了所提方法的有效性。     

相邻结构地震反应的半主动控制分析

现代城市中相邻结构大量的出现,这样就有可能导致地震中建筑碰撞破坏的加剧.针对安装了AVSD控制器与安装了MR阻尼器的相邻结构半主动控制体系,运用Matlab编程仿真分析了El-Centro波、Taft 波和Northridge波三种地震激励下控制效果.结果表明:对相邻结构中较高结构的控制AVSD系统的控制效果较好,但是...     

结构振动半主动控制的实用性研究

介绍了结构振动半主动控制的发展动态及其应用现状。探讨了结构振动半主动控制工程应用所面临的几个主要问题,即:结构振动半主动控制工程应用的综合效益问题、控制装置的产业化问题、控制装置的优化设计问题、半主动控制系统商业软件的开发问题,以及智能材料的应用问题,并针对上述问题指出了半主动控制工程应用中值得进一步研究的课题及其发展方向。     

模糊逻辑下的MR阻尼器减振控制研究

利用MATLAB Simulink模块对一MR阻尼模糊控制器下的三层钢结构建筑对地震荷载的响应进行了数值仿真。仿真结果表明,钢结构建筑物中采用MR阻尼模糊控制器能对结构的地震响应进行即时、有效和显著的强非线性半主动控制。因而其具有一定的工程应用价值。     

基于模糊逻辑的熟料与水泥强度预测研究

本文分析了熟料与水泥强度预测的模糊性,提出了一些模糊预测模型,并对一些熟料与水泥样品进行了计算.为比较,还以回归方法计算了其中的一些样品.结果用标准偏差及散点图表示.     

半主动控制的国内外研究现状

通过对结构振动控制的概述,介绍了结构振动半主动控制的发展动态及其应用现状,探讨了结构振动半主动控制工程应用所面临的几个主要问题,并指出了半主动控制工程应用中值得进一步研究的课题及其发展方向。     

基于模糊逻辑的熟料与水泥强度观测研究

本文分析了熟料与水泥强度预测的模糊性，提出了一些模糊预测模型，并对一些熟料与水泥样品进行了计算，为比较，还以回归方法计算了其中的一些样品，结果用标准偏差及散点图表示。     

基于模糊逻辑算法的感应式信号灯仿真设计

为提高主干路与支路交叉口的通行效率,并减少由于固定信号造成的绿灯时间浪费和经济损失。针对主支路路口的车流量比,将模糊逻辑算法加以改进应用,由路口交通流量算出相关流量比等数据以建立算式,建立基于模糊逻辑算法的感应式信号灯的控制器设计,随后通过MATLAB软件和VISSIM软件分别得到算法实践及模拟仿真,并同时对仿真进行了改进前后的数据比对。     

RBF神经网络在结构振动半主动控制中的应用

利用RBF神经网络对建筑结构地震反应进行预测,设计了加入MR阻尼器的RBF神经网络半主动控制系统,利用Matlab进行仿真分析,结果表明该控制方案能够有效减少建筑结构的地震反应,并且很好的发挥了MR阻尼器的可控性。     

Prediction of rubberized concrete properties using artificial neural network and fuzzy logic

In this study, waste automobile tyres in two different sizes were used in production of rubberized fresh concretes. Their unit weight and flow table values were determined experimentally. The values determined were also found when artificial neural networks (ANN) and fuzzy logic (FL) models were employed. According to the given rubberized concrete data, it was demonstrated that properties of fresh concrete could be determined without attempting any experiments by using ANN and FL models. During the tests similar results were observed for experimental results with those of ANN and FL models. Besides, the facts that lighter concrete might be produced using tyre as a light material and waste tyres may be recycled this way were put forth.     

空间结构风振控制系统的神经网络时滞补偿

结合神经网络方法和传统补偿方法,研究空间结构风振控制系统的时滞补偿问题.运用单个神经网络取代由两个神经网络组成的控制系统,有效减小系统时滞和产生时滞的环节.针对空间结构风振控制系统,综合运用神经网络方法与状态预测补偿法,构建基于神经网络的多步预测时滞补偿方法.研究显示,该时滞补偿方法克服了传统方法对多自由度系统不适用、数值计算困难等缺陷,可成功运用于空间结构风振控制系统,经时滞补偿后控制效果优于未经时滞补偿的系统.     

基于人工神经网络的变风量空调控制系统

研究了变风量空调系统神经网络预测优化控制方法,优化指标考虑了舒适性和耗能量,舒适性指标取PMV指标,耗能量包括风机和冷水泵能耗。系统的控制量为送风风速和冷水流量,被控参数为空调区域的温湿度,采用预测滚动优化控制算法训练多层前向神经网络,然后将其作为优化反馈控制器来求解变风量暖通空调系统的优化解,并在运行中实时预测空调区域的负荷。仿真结果表明,采用此方法,在模型环境、负荷参数变化的情况下,既可以达到节能的要求,又可以使空调区域的温湿度保持在舒适范围内。     

Multiple linear regression,artificial neural network,and fuzzy logic prediction of 28 days compressive strength of concrete

Evaluating the in situ concrete compressive strength by means of cores cut from hardened concrete is acknowledged as the most ordinary method, however, it is very difficult to predict the compressive strength of concrete since it is affected by many factors such as different mix designs, methods of mixing, curing conditions, compaction, etc. In this paper, considering the experimental results, three different models of multiple linear regression model (MLR), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) are established, trained, and tested within the Matlab programming environment for predicting the 28 days compressive strength of concrete with 173 different mix designs. Finally, these three models are compared with each other and resulted in the fact that ANN and ANFIS models enables us to reliably evaluate the compressive strength of concrete with different mix designs, however, multiple linear regression model is not feasible enough in this area because of nonlinear relationship between the concrete mix parameters. Finally, the sensitivity analysis (SA) for two different sets of parameters on the concrete compressive strength prediction are carried out.     

A multi‐time‐delay compensation controller using a Takagi–Sugeno fuzzy neural network method for high‐rise buildings with an active mass damper/driver system

An active mass damper/driver (AMD) control system with a single mass has such problems as the excessive weight of the auxiliary mass and the insufficient capacity of its driving equipment. It is necessary to work through multiple subsystems to achieve effective control of high‐rise buildings. However, the time‐delay effect in each subsystem impedes its application in engineering practices. In the paper, an augmented system based on a zero‐order hold is proposed for discrete‐time systems with multiple time delays, and then the system is designed according to the compensation strategy using a classical linear quadratic regulator algorithm. After that, the sample data obtained from the zero‐order hold compensation controller is trained through a Takagi–Sugeno fuzzy neural network method. Finally, a new simplified compensation controller is designed to further shorten the time consuming calculation on the premise of guaranteeing its control effects and parameters. To verify its effectiveness, an AMD system in a high‐rise building is regarded as an example, and the proposed methodology is also applied to an experiment of a four‐story frame. Both results demonstrate that the method can enhance the performance of an AMD system with multiple time delays.     

基于神经网络的连续刚构桥施工线形控制参数预测研究

结合白果渡嘉陵江大桥施工线形控制的具体实践,采用BP神经网络进行连续刚构桥施工线形控制中的参数识别及预测工作。基于影响桥梁线形主要参数的截面尺寸、距离及标高建立神经网络系统,并对其进行计算训练样本、训练神经网络和网络仿真分析。运用神经网络仿真分析进行连续刚构桥施工线形的具体方法是,先计算当前施工状态的标高,再预测下一节段的标高。经过往复循环,逐一进行节段预测调整,从而指导连续刚构桥顺利施工。网络学习及仿真预测结果表明:该法对数据的处理及预测,在操作简单的基础上,分析结果具有较高的精度。该结论可推广到采用悬臂法施工的连续梁桥、拱桥、斜拉桥等桥型的施工线形控制工作及研究。     

Optimizing overbreak prediction based on geological parameters comparing multiple regression analysis and artificial neural network

Underground mining becomes more efficient due to the technological advancements of drilling and blasting methods and the developing of highly productive mining methods that facilitate easier access to ore. In the perspective of maximizing productivity in underground mining by drilling and blasting methods, overbreak control is an essential component. The causing factors of overbreak can simply divided as blasting and geological parameters and all of the factors are nonlinearly correlated. In this paper, the blasting design of the tunnel was fixed as the standard blasting pattern and the research focus on effects of geological parameters to the overbreak phenomenon. 49 sets of rock mass rating (RMR) and overbreak data were applied to linear and nonlinear multiple regression analysis (LMRA and NMRA) and artificial neural network (ANN) to predict overbreak as input and output parameters, respectively. The performance of LMRA, NMRA, and optimized ANN models was evaluated by comparing coefficient correlations (R²) and their values are 0.694, 0.704 and 0.945, respectively, which means that the relatively high level of accuracy of the optimized ANN in comparison with LMRA and NMRA. The developed optimum overbreak predicting ANN model is suitable for establishing an overbreak warning and preventing system and it will utilize as a foundation reference for a practical drift blasting reconciliation at mines for operation improvements.     

Prediction of elastic modulus of normal and high strength concrete by artificial neural networks

In the present paper, application of artificial neural networks (ANNs) to predict elastic modulus of both normal and high strength concrete is investigated. The paper aims to show a possible applicability of ANN to predict the elastic modulus of both high and normal strength concrete. An ANN model is built, trained and tested using the available test data gathered from the literature. The ANN model is found to predict elastic modulus of concrete well within the ranges of the input parameters considered. The average value of the experimental elastic modulus to the predicted elastic modulus ratio is found to be 1.00. The elastic modulus results predicted by ANN are also compared to those obtained using empirical results of the buildings codes and various models. These comparisons show that ANNs have strong potential as a feasible tool for predicting elastic modulus of both normal and high strength within the range of input parameters considered.     

Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models

The safety control of large dams is based on the measurement of some important quantities that characterize their behaviour (like absolute and relative displacements, strains and stresses in the concrete, discharges through the foundation, etc.) and on visual inspections of the structures. In the more important dams, the analysis of the measured data and their comparison with results of mathematical or physical models is determinant in the structural safety assessment.In its lifetime, a dam can be exposed to significant water level variations and seasonal environmental temperature changes. The use of statistical models, such as multiple linear regression (MLR) models, in the analysis of a structural dam’s behaviour has been well known in dam engineering since the 1950s. Nowadays, artificial neural network (NN) models can also contribute in characterizing the normal structural behaviour for the actions to which the structure is subject using the past history of the structural behaviour. In this work, one important aspect of NN models is discussed: the parallel processing of the information.This study shows a comparison between MLR and NN models for the characterization of dam behaviour under environment loads. As an example, the horizontal displacement recorded by a pendulum is studied in a large Portuguese arch dam. The results of this study show that NN models can be a powerful tool to be included in assessments of existing concrete dam behaviour.