高层民用建筑柴油发电机组设计若干问题

介绍高层民用建筑中柴油发电机容量的确定,指出柴油发电机机组容量应按消防负荷及非消防重要负荷之间的较大者确定;阐述柴油发电机机房的布置及机房噪音、机组尾气的处理方法。     

某超高层建筑的电气节能设计

通过某超高层建筑的设计实例,从供配电系统节能、照明节能、楼宇设备控制、计量及管理等方面较全面地论述了超高层建筑中的电气节能设计要点。     

冷热电三联供系统在民用建筑中的应用

结合某办公楼中冷热电三联供系统的应用,介绍冷热电三联供的原理、相关规范标准、民用建筑中应用冷热电三联供系统的运行方案、技术经济分析、发电机组规模的确定原则及配电系统的主接线形式。     

基于模糊控制技术的水泥分解炉温度控制

针对干法水泥生产过程中管理众多、操作复杂、调节不准确等问题,本文提出了采用模糊控制系统的解决方法。本文应用模糊控制技术的模糊化、模糊推理和逆模糊化方法,建立了分解炉温度控制系统。利用水泥窑炉燃烧的关键参数点,并通过模糊控制系统分析、判断、处理以及时准确的匹配最佳运行的风、煤、料及窑转速,使系统稳定在正常生产的工况下,避免异常发生。本文所研制的分解炉温度控制器已成功用于水泥厂。     

基于模糊自适应PI控制的电梯矢量控制系统仿真研究

根据模糊自适应PID控制理论,对电梯变频调速矢量控制系统中的转速PI调节器进行优化设计。并应用电动机矢量控制系统的仿真模型进行仿真分析。仿真结果表明,采用模糊自适应PI控制器的变频调速系统具有响应时间快、超调量小等优点。     

基于组合赋权与改进模糊的临建设施绿色度评价研究

临建设施;组合赋权法;改进模糊综合评价;绿色度     

社会责任视角下建筑企业财务绩效的提升路径——基于 fsQCA 方法的组态效应研究

为探讨多维度的社会责任对建筑企业提升财务绩效的联动效应及路径选择, 基于利益相关者框架,运用模糊集定性比较分析（fsQCA）方法,对 2013~2017 年中国上市建筑企业的截面数据进行分析。研究发现:建筑企业财务绩效提升路径具有“异途同归” 的特点, 即不同维度的社会责任组合都可以实现提升建筑企业财务绩效的目标,且单一维度的企业社会责任不是构成高财务绩效的必要条件; 顾客、环境和社区等外部利益相关者的社会责任对建筑企业高财务绩效贡献显著; 在资源匮乏的情况下,提升社区社会责任水平可以有效缓解其他维度社会责任对建筑企业财务绩效的负面影响。     

村镇砌体建筑群信息智能获取与震害预测

我国村镇地区砌体结构分布广、数量大、抗震性能普遍较差,且具有结构形式多样、无数据和随机性强等特点,获取其建筑信息是实现震害预测的前提和基础,但传统人工检测技术效率低、不经济。为此,提出了一种图像识别与模糊推理相结合的村镇砌体建筑群信息智能获取方法。建立了基于卷积神经网络的砌体建筑图像识别模型,结合图像测量技术获取建筑几何信息;提出了基于模糊推理的砌体结构隐蔽信息确定方法,可有效提取村镇砌体结构墙体属性、构造措施、材料强度等隐蔽属性信息;依据获取的建筑信息与场地信息,分别建立单体结构力学模型与空间地震动场模型,通过结构地震动力响应分析实现村镇区域砌体建筑群的震害预测。最后,对北方地区某村落进行群体建筑信息获取和震害预测,结果表明,基于影像数据的图像识别和图像测量技术可以有效获取村镇建筑结构类型以及外观尺寸信息,提出的模糊推理方法可以高效、准确地识别砌体建筑群的隐蔽信息,进一步通过对震害场景的数值模拟,实现村镇区域群体建筑震害的有效预测。     

Decision support model for selection of rehabilitation methods of water mains

The deteriorating condition of water mains in Canada and US calls for rehabilitation strategies that accounts mainly for budget and level of service constraints. These water mains have received ‘D’ grade in the two countries. Decision support models can assist decision makers regarding when to rehabilitate and whether to repair, renovate or replace section(s) of water mains. The literature indicates that decision models should account for life cycle cost, uncertainty, long-term planning, targeted levels of service and budget constraints. The objectives of this paper are to: identify and group rehabilitation methods, present decision support model to rank and select most suitable rehabilitation method(s), and study the impact of rehabilitation methods on the functional and structural performance of water mains. The developed decision support model accounts for life cycle cost of each competing scenario along with the associated uncertainty. The model, unlike available models, can effectively account for vagueness, qualitative assessments and human judgment associated with input data. A case study of a water main network was analysed in order to demonstrate the use of the developed model and to illustrate its essential features. The results obtained indicate that the model can support the generation of well-informed decisions in a timely manner.     

Hybrid intelligent inference model for enhancing prediction accuracy of scour depth around bridge piers

Bridge-pier scouring is a main cause of bridge failures. Thus, accurately predicting the scour depth around bridge piers is critical, both to specify adequate depths for new bridge foundations and to assess/monitor the safety of existing bridges. This study proposes a novel artificial intelligence (AI) model, the intelligent fuzzy radial basis function neural network inference model (IFRIM), to estimate future scour depth around bridge piers. IFRIM is a hybrid of the radial basis function neural network (RBFNN), fuzzy logic (FL), and the artificial bee Cclony (ABC) algorithm. In the IFRIM, FL is used to handle the uncertainties in input information, RBFNN is used to handle the fuzzy input–output mapping relationships, and the ABC search engine employs optimisation to identify the most suitable tuning parameters for RBFNN and FL based on minimal error estimation. A 10-fold cross-validation method finds that the IFRIM model achieves at least 21% and 14.5% reductions in root mean square error and mean absolute error values, respectively, compared with other AI techniques. Study results support the IFRIM as a promising new tool for civil engineers to predict future scour depth around bridge piers.