房屋倒塌事故分析及预防建议

近几年发生的几起房屋整体倒塌事故，再次引起全社会的强烈关注。房屋倒塌不仅造成人民生命财产的巨大损失，而且社会影响很坏。从近两年倒塌房屋的特点分析，不难发现这类事故的性质有越来越严重的趋势，主要表现在：从局部倒塌发展成整体倒塌；施工中倒塌演变成使用中倒塌；从农房、乡镇建筑倒塌扩展到城市房屋倒塌2倒塌房屋的面积不断扩大，伤亡人员大幅度增加。为了防止房屋倒塌事故的发生，建设部采取了一系列措施，并已取得一定成效。但是，要真正杜绝房屋倒塌事故的再发生，还需作大量细致扎实的工作。本文以总结80年代以来我国十起…     

多层砌筑结构的基底剪力系数考虑变形集中的修正

一、前言在地震区,多层砌筑房屋采用了配筋构造,使得该类房屋具有一定的塑性变形能力。许多震害表明,无筋砌体房屋往往由于变形集中在个别层而引起整幢房屋倒塌。本文试图寻求这种变形分布不均匀的规律,提出改善方法,以避免具有有限变形能力的配筋砌体房屋发生类似破坏。     

坍塌建筑中被困人员的自防自救研究

地震属于自然灾害，人类无法抗拒。在突发性且具有巨大破坏力的灾难面前，人们只能采取震前防范、震中急救的措施，尽可能地生存下去。据有关的研究数据显示，地震所带来的人员伤亡率与房屋倒塌率有着一定的关系，房屋倒塌率越高的地方人员死亡率也就越高；     

怎样预测火场房屋坍塌

房屋倒塌给受灾单位和个人带来难以估计的损失,要准确预测房屋是否倒塌,在这之前,安全有序地组织人员将被困人员以及贵重物品安全救出,是减少损失和人员伤亡的关键。如何预测火场房屋坍塌的时间呢？     

既有老旧砌体加固研究现状

中国是一个地震多的国家,从以往的震害经验中了解到造成重大伤亡的房屋大多是老旧砌体房屋,所以加强农村老旧房屋的改造工作迫在眉睫,老旧砌体在地震作用下常见的倒塌形式有整体倒塌和局部倒塌,墙体常见的裂缝形式有交叉斜裂缝、水平裂缝、竖向裂缝。采用构柱圈梁可有效提高砌体抗震能力,但此法的推广具有一定的局限性。     

钢-混凝土混合排架结构厂房火灾后结构安全鉴定

近年来,建筑物火灾事故频发,给人民群众生命财产安全带来重大损失。就钢-混凝土混合排架结构房屋而言,火灾发生时其本身具有一定的耐火性,通常不会发生倒塌。但是,高温作用后的钢材和混凝土强度、弹性模量等值都发生大幅度的降低,相应的结构构件变形增大、内力发生重分布,结构构件的承载能力削弱严重。作为房屋安全检测鉴定人员,应当通过查勘火灾现场、结构构件检测与承载能力验算相结合的方式科学、合理地鉴定房屋损伤,并相应提出房屋加固修复方案,确保加固修复后的房屋结构安全、经济、适用。     

基于模糊神经网络的供热负荷预测

为了克服传统BP神经网络预测精度差,易陷入局部极值的缺陷,提出了模糊神经网络系统。利用模糊粗糙集通过历史负荷数据信息的模糊化替代负荷变化的离散化,快速寻找出样本数据间的连续属性的信息,将其与传统BP神经网络结合组成模糊神经网络对热负荷进行预测。实验结果表明:该模糊神经网络预测结果的相对误差很小不超过2%,在短期负荷预测方面具有的优越性。     

基于模糊神经网络变风量空调系统风量预测研究

总风量控制法自提出后,由于其末端动作频繁、通信量大、控制复杂而一直处于研究完善阶段。在空调室的神经网络预测控制系统得出满意的动、静态性能的基础上,将模糊控制与BP神经网络结合,建立了模糊神经网络,对变风量空调系统的风量进行预测,使它们有效地发挥各自的优势并弥补各自的不足,提高了预测的精度。预测结果表明这种改进的控制方式在空调系统的负荷预测方面是有效的、可行的。     

神经网络及在中医智能诊断中的应用

本文提出运用模糊神经网络控制理论解决中医智能诊断的问题,将模糊神经网络与中医理论、中医临床经验等结合起来处理中医诊断智能化的问题,利用神经网络的学习算法,进一步实现模糊系统的自学习与自适应,从而构建自适应神经模糊推理系统。该系统可进行中医分型诊断,并实现BP学习算法,在处理非线性、模糊性、智能信息等方面表现出一定的优越性,具有良好的泛化能力与识别精度。     

深大基坑施工变形的智能控制技术

利用神经网络 (ANN)和模糊控制 (FC)理论 ,采用预测控制的思想 ,建立了一套集基坑施工变形预测和控制于一体的智能化施工控制系统 ,该系统由神经网络预测器和模糊控制器组成。神经网络预测器对基坑变形进行连续滚动的多步预测 ,模糊控制器根据预测结果对施工参数进行决策控制。在MATLAB 5 .2平台支持下 ,研制了相应的基坑变形控制软件系统。实际应用结果表明 ,该智能控制系统对深基坑的安全施工过程具有较好的控制效果 ,真正做到了施工过程的实时、动态、智能化控制。软件系统操作界面简单、直观 ,便于实际工程应用。     

StructNet: A Neural Network for Structural System Selection

Abstract: This paper describes StructNet, a computer application developed to select the most effective structural member materials given a building project's attributes. The system analyzes 15 parameters of a building project (e.g., available site space, budget, height) and determines the most appropriate structural system for the beam, column, and slab structural members. This paper first describes the process for selecting a structural system for a building. It was very important to understand this process before determining the best type and structure for the computer application. Then a comparison between a neural network approach and a rule-based expert-system approach for this application is presented. A discussion of the reasons for selecting a neural network approach is given. The StructNet application is described in detail, including the testing of the network. Along with the testing of the network is a discussion of how varying the learning rate and error limit affect the performance of the neural network application. The testing of the network shows that the program can reasonably select the same structural system types as the expert used to collect the training project data. Since the system will be used only as a preliminary tool to limit the number of possible structural systems for a project, the accuracy of the system is acceptable. However, additional experimentation needs to be conducted to determine the accuracy and practical use of this application. The final sections of the paper discuss the lack of adequate testing procedures for neural networks used in applications for unstructured or ill-defined decision making. The use of these types of networks and their relevance to the civil engineering computer field are also discussed.     

革命纪念建筑物的历史文化场景保护——以甘肃省哈达铺红军长征旧址保护为例

一些分布于相对偏远、落后地区的革命纪念建筑物的保护目前面临着许多问题，对其历史文化场景进行积极保护是十分重要的。该文以甘肃省哈达铺红军长征旧址保护为例，对该问题加以初步探讨，指出革命纪念建筑物的保护应以当地的生产和生活现实为基础，适当修缮文物本体，重点整治文物环境，全力营造文物特定的历史文化场景。     

革命纪念建筑物的历史文化场景保护--以甘肃省哈达铺红军长征旧址保护为例

一些分布于相对偏远、落后地区的革命纪念建筑物的保护目前面临着许多问题,对其历史文化场景进行积极保护是十分重要的.该文以甘肃省哈达铺红军长征旧址保护为例,对该问题加以初步探讨,指出革命纪念建筑物的保护应以当地的生产和生活现实为基础,适当修缮文物本体,重点整治文物环境,全力营造文物特定的历史文化场景.     

基于粗糙集—RBF神经网络的单层RC厂房震害预测

RBF神经网络具有较好的仿真预测功能,粗糙集理论可以通过属性约简、重要度排序等对样本数据进行有效筛选。将粗糙集与RBF神经网络有机结合,建立单层RC厂房的震害预测模型。结合实际震例进行仿真训练,得到的单层RC厂房震害预测值与实际值基本吻合。表明该模型可对单层工业厂房进行较为有效的震害预测,且对震害预防也具有一定的指导意义。     

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。     

钢筋混凝土框架结构教学建筑抗倒塌性能研究

三柱式(大小跨)单廊教学建筑以其良好的自然通风、采光性能,应用广泛。该类结构侧向刚度小,罕遇地震作用下抗倒塌性能差,应采取可靠工程措施改善其抗倒塌性能。本文结合某些中学教学楼的抗震设计工程实践,简要介绍改善三柱式钢筋混凝土框架结构在罕遇地震作用下抗倒塌性能的工程措施。在高烈度地震区,采用钢筋混凝土框架——钢支撑结构,或钢筋混凝土框架-抗震墙结构,可改善其抗倒塌性能。由于教学建筑抗倒塌性能的重要性和该类结构的特殊性,在设计的过程中,除了按现行建筑抗震设计规范要求满足结构的承载力和极限能力之外,用基于性能评估的Pushover方法对结构在设防烈度的罕遇地震作用下的性能进行分析和评估,以实现大震不倒的抗震设计目标。     

A neural network model to predict the wastewater inflow incorporating rainfall events

Under steady-state conditions, a wastewater treatment plant usually has a satisfactory performance because these conditions are similar to design conditions. However, load variations constitute a large portion of the operating life of a treatment facility and most of the observed problems in complying with permit requirements occur during these load transients. During storm events upsets to the different physical and biological processes may take place in a wastewater treatment plant, and therefore, the ability to predict the hydraulic load to a treatment facility during such events is very beneficial for the optimization of the treatment process. Most of the hydrologic and hydraulic models describing sewage collection systems are deterministic. Such models require detailed knowledge of the system and usually rely on a large number of parameters, some of which are uncertain or difficult to determine. Presented in this paper, an artificial neural network (ANN) model that is used to make short-term predictions of wastewater inflow rate that enters the Gold Bar Wastewater Treatment Plant (GBWWTP), the largest plant in the Edmonton area (Alberta, Canada). The neural model uses rainfall data, observed in the collection system discharging to the plant, as inputs. The building process of the model was conducted in a systematic way that allowed the identification of a parsimonious model that is able to learn (and not memorize) from past data and generalize very well to unseen data that was used to validate the model. The neural network model gave excellent results. The potential of using the model as part of a real-time process control system is also discussed.     

基于聚类分析和支持向量机的滑坡易发性评价

在将支持向量机(support vector machine,SVM)等机器学习模型用于区域滑坡易发性评价时,大都随机或主观地选取非滑坡栅格单元,不能保证所选的非滑坡栅格单元是真正的"非滑坡"。为解决此问题,提出基于聚类分析和SVM的滑坡易发性评价模型。该模型首先用自组织映射(self-organizing mapping,SOM)神经网络对滑坡易发性进行聚类分析;然后从极低易发区中选择非滑坡栅格单元,确保所选非滑坡栅格单元是高概率的"非滑坡";最后采用SVM模型基于已知滑坡、所选非滑坡和环境因子对滑坡易发性进行评价。将提出的SOM-SVM模型用于三峡库区万州区滑坡易发性评价,并将得到的易发性结果与随机选取非滑坡的单独SVM模型结果做对比。结果显示SOM-SVM模型具有比单独SVM模型更高的成功率和预测率,表明SOM神经网络能更准确地选取非滑坡栅格单元。     

人工神经网络在建筑结构中的应用研究

对日益广泛应用于建筑结构的人工神经网络的基本原理与特征以及误差反向传播的多层感知器网络(BP网络)的多种改进算法进行了介绍,分析了人工神经网络在建筑结构的优化、控制以及损伤诊断等领域中的应用情况,对人工神经网络的推广应用具有一定指导意义。     

Development and validation of regression models to predict monthly heating demand for residential buildings

The present research work concerns development of regression models to predict the monthly heating demand for single-family residential sector in temperate climates, with the aim to be used by architects or design engineers as support tools in the very first stage of their projects in finding efficiently energetic solutions. Another interest to use such simplified models is to make it possible a very quick parametric study in order to optimize the building structure versus environmental or economic criteria. All the energy prediction models were based on an extended database obtained by dynamic simulations for 16 major cities of France. The inputs for the regression models are the building shape factor, the building envelope U-value, the window to floor area ratio, the building time constant and the climate which is defined as function of the sol-air temperature and heating set-point. If the neural network (NN) methods could give precise representations in predicting energy use, with the advantage that they are capable of adjusting themselves to unexpected pattern changes in the incoming data, the multiple regression analysis was also found to be an efficient method, nevertheless with the requirement that an extended database should be used for the regression. The validation is probably the most important level when trying to find prediction models, so 270 different scenarios are analysed in this research work for different inputs of the models. It has been established that the energy equations obtained can do predictions quite well, a maximum deviation between the predicted and the simulated is noticed to be 5.1% for Nice climate, with an average error of 2%. In this paper, we also show that is possible to predict the building heating demand even for more complex scenarios, when the construction is adjacent to non-heated spaces, basements or roof attics.