A Building Information Modeling-Life Cycle Cost Analysis Integrated Model to Enhance Decisions Related to the Selection of Construction Methods at the Conceptual Design Stage of Buildings

Life Cycle Cost Analysis (LCCA) provides a systematic approach to assess the total cost associated with owning, operating, and maintaining assets throughout their entire life. BIM empowers architects and designers to perform real-time evaluations to explore various design options. However, when integrated with LCCA, BIM provides a comprehensive economic perspective that helps stakeholders understand the long-term financial implications of design decisions. This study presents a methodology for developing a model that seamlessly integrates BIM and LCCA during the conceptual design stage of buildings. This integration allows for a comprehensive evaluation and analysis of the design process, ensuring that the development aligns with the principles of low carbon emissions by employing modular construction, 3D concrete printing methods, and different building design alternatives. The model considers the initial construction costs in addition to all the long-term operational, maintenance, and salvage values. It combines various tools and data through different modules, including energy analysis, Life Cycle Assessment (LCA), and Life Cycle Cost Analysis (LCCA) to execute a comprehensive assessment of the financial implications of a specific design option throughout the lifecycle of building projects. The development of the said model and its implementation involves the creation of a new plug-in for the BIM tool (i.e., Autodesk Revit) to enhance its functionalities and capabilities in forecasting the life-cycle costs of buildings in addition to generating associated cash flows, creating scenarios, and sensitivity analyses in an automatic manner. This model empowers designers to evaluate and justify their initial investments while designing and selecting potential construction methods for buildings, and enabling stakeholders to make informed decisions by assessing different design alternatives based on long-term financial considerations during the early stages of design.

An integrated spatial planning of the mountainous landscapes for ski sports in a case area at the eastern Türkiye

Mountainous regions have disadvantages in economic development because of harsh physical and climatic conditions.However,winter tourism activities are one of the key components for supporting economic development in the highlands.Establishing a ski resort area supports direct and indirect employment in a region,and it stops immigration from mountainous regions to other places.This research aimed to assess the potential ski areas using a multi criteria evaluation technique in the Van region which is located in the eastern part of Türkiye.In this context,snow cover duration,sun effect,slope,slope length,elevation,population density,distance from main roads and lake visibility were used as input factors in the decision making process.Each factor was standardized using a fuzzy technique based on existing well-known ski centers in Türkiye.The weight of inputs was defined by applying a survey to the professional skiers.The most important factors were detected as transportation opportunities and snow covers whereas,the least important factors were elevation and population density.Additionally,lake visibility was very important to make a difference from other existing facilities in the region.Therefore,it was included as constraints and lake visible areas were extracted at the final stage of the research.Potential ski areas were mapped in three levels as professional,intermediate and beginner skiers.One of the suitable areas was selected as a sample projection and for the 3D simulation of the ski investment area.Potential costs and benefits were discussed.It was found that a ski tourism area investment can be amortized in 3 years in the region.

基于曲线决策融合的SDN饱和攻击检测方法

针对软件定义网络(SDN)交换机和控制器的饱和攻击是SDN中的主要安全问题。在使用集成学习方法检测饱和攻击时,现有方法通常使用距离或熵值等简单的信息计算方法修正证据,可能存在信息丢失问题,降低饱和攻击检测精度。为解决上述问题,提出一种基于曲线决策融合的饱和攻击检测方法(SACOIN)。SACOIN首先计算多分类器概率矩阵的混乱程度修正多分类器内证据;随后将多分类器概率矩阵转换为曲线并去除噪声,提取重构小波的信号特征组成特征矩阵;然后计算特征矩阵行内互信息,基于上述互信息修正多分类器间证据;最后使用D-S证据理论融合修正证据,得到最终检测结果。实验结果表明,SACOIN在检测针对SDN交换机和控制器的饱和攻击时的准确率、精确率、召回率、F1值分别为92.3%、93%、92.1%、91.3%。

D型人格和应对方式对急性心肌梗死患者就医决策延迟的影响

目的 了解急性心肌梗死(AMI)患者的就医决策延迟现状,并探讨D型人格和应对方式与就医决策延迟之间的关系。方法 采用一般资料调查表、D型人格量表(DS-14)及医学应对问卷(MCMQ)对232例AMI患者进行调查,并随机抽取患者调查问卷与病历,以保证收集时间的一致性。结果 232例患者就医决策延迟时间的中位数为128 min,共133例患者发生延迟,发生率为57.3%。DS-14总分及各维度应对方式中回避应对与就医决策延迟时间呈正相关(均P<0.01),面对应对与就医决策延迟时间呈负相关(P<0.05)。回避应对在D型人格与就医决策延迟时间关系中起部分中介作用,中介效应占总效应的34.5%。结论 D型人格和应对方式与AMI患者的就医决策延迟有关,医护人员应重视D型人格对就医决策时间的影响,重点关注AMI高危人群,早期筛查与识别,以便尽早采取针对性干预措施,促进其积极应对,最终缩短就医决策延迟时间。

Optimum design of equivalent accelerated life testing plans based on proportional hazards-proportional odds model

The optimum design of equivalent accelerated life testing plan based on proportional hazards-proportional odds model using D-optimality is presented. The defined equivalent test plan is the test plan that has the same value of the determinant of Fisher information matrix. The equivalent test plan of step stress accelerated life testing （SSALT） to a baseline optimum constant stress accelerated life testing （CSALT） plan is obtained by adjusting the censoring time of SSALT and solving the optimization problem for each case to achieve the same value of the determinant of Fisher information matrix as in the baseline optimum CSALT plan. Numer- ical examples are given finally which demonstrate the equivalent SSALT plan to the baseline optimum CSALT plan reduces almost half of the test time while achieving approximately the same estimation errors of model parameters.

A Multi-Attribute Decision Making for Investment Decision Based on D Numbers Methods

Investment decision is a traditional multi-attribute decision making (MADM) problem since it has many uncertainty factors and incomplete information such as investment value, cost, sales, etc. D numbers theory is a useful tool to deal with uncertainty factors and incomplete information. In this paper, interval number and D numbers theory are revealed in the uncertain factor and incomplete information of investment decision. The weights of uncertain factors are calculated using entropy weight method. Thus, a new MADM model for investment decision based on D numbers theory is proposed. Numerical example is used to illustrate the efficiency of the proposed method.

Decision Support Scheme for Lishan Landslide Prewarning System

This study combines the analytical model to build a landside monitoring decision support system of the Web GIS. The landslide area of Lishan is a case study for the research. The analysis of the risk degree for the landslide area in Lishan is based on the three-layer architecture of Fuzzy Analytic Hierarchical Process （FAHP）. There are four fuzzy model structures used in monitoring devices： rainfall, groundwater level, Time Domain Reflectometry （TDR） monitored the subsurface deformation, and Global Positioning System （GPS） monitored ground displacement. These structures are relative to four membership functions that are used to classify four states, including safety, attention, warning, and danger. The risk degree of the landslide area can be obtained through the fuzzy rules by determining management criteria. Calculating the total scores of historical monitoring record of the rainfall, groundwater level, TDR, and GPS through the fuzzy theory can determine the analytical results of risk degrees in Lishan landslide area. In this whole area, management criterion is in the state of attention when the total score is larger than 72, in the state of warning when total score is larger than 95, and in the state of danger when total score is larger than 113. The system provides real-time monitoring data, and prewarning decision support in order to announce and prevent the disaster at the earliest time.

高校智慧图书馆边缘计算场景下基于MOEA/D算法的卸载决策

智慧图书馆子系统是高校智慧校园系统的核心部件,它融合了云计算技术、边缘计算技术、物联网技术、人工智能技术。借助边缘计算技术,可以通过把计算资源和存储资源下沉到网络边缘的边缘服务器上。智能终端设备把任务卸载到边缘服务器上来处理,可以降低智能终端设备的任务处理时延和能源消耗,从而满足读者的随时随地的阅读需求。针对高校智慧图书馆边缘计算场景下的任务卸载决策问题,建立了系统模型、时延模型、能耗模型和多目标优化模型,给出了一种基于MOEA/D算法的卸载方案,并与本地执行的方案、随机卸载方案、基于遗传算法的卸载方案进行了对比实验。实验结果表明:给出的任务卸载方案能够有效降低智能终端设备的任务卸载时延和能耗,能满足智慧图书馆的任务卸载需求。

HY-1C/DCZI多光谱影像云覆盖与耀斑区域绿潮自动提取方法研究

针对多光谱影像受云、雾、太阳耀斑等因素的影响,难以实现高精度的绿潮自动提取的问题,本文以我国的HY-1C/D卫星CZI载荷多光谱影像为数据源,采用数据挖掘技术,通过探索绿潮区域与非绿潮区域的光谱分布差异,提出一种适用于HY-1C/DCZI影像的高精度、全自动绿潮提取方法。首先,分析有云区域和无云区域样本的光谱差异,给出厚云去除规则;其次,选取绿潮和非绿潮区域的样本,采用决策树算法生成绿潮提取规则;然后,针对薄云和厚云边界区域常常会出现误检绿潮的问题,设计了5种错误类别修正策略。为验证方法的有效性,收集2021年黄海区域绿潮暴发周期内的25景HY-1C/D CZI影像,开展绿潮自动检测实验。结果表明,与传统的NDVI方法、VB-FAH方法等指数方法以及ResNet50、U-Net等深度学习方法相比,本文方法在准确度、Kappa系数、F1-Score和MIoU等指标上均优于其他方法,而且能够实现在厚云、薄云、无云、云斑和耀斑区域复杂情况下的绿潮的高精度自动提取。

基于D-S证据理论的电商服装新品需求预测与订货决策研究

现有服装电商企业对新品需求预测不准确,会导致大量商品滞销或缺货,造成企业亏损。服装新品预测困难的主要原因在于其无历史数据可参考。由于证据理论具有处理多源不确定信息的能力,文中采用该理论建立需求预测模型。首先,构建证据理论模型,运用D-S证据合成法则将多位专家的预测进行融合,并将预测需求量信度函数转换为概率分布;其次,通过报童模型得出最优订货量;最后,以某电商企业新品上新为例进行了算例分析。该方法可提升服装类新品需求初次预测的精准度,辅助电商企业新品订货决策。

组织视角下的核电厂班组决策失误致因分析

为了系统研究班组决策失误规律,识别核电厂班组决策失误的关键路径,从组织视角出发,结合核电厂所处环境与班组决策失误特征对人因分析与分类系统(Human Factor Analysis and Classification System,HFACS)模型进行改进,建立了核电厂班组决策失误致因模型。利用改进后的模型对137份核电厂事件报告进行统计分析,并运用卡方检验与让步比分析班组决策失误的原因及各层次之间的内在关系。结果表明,组织文化缺失、规程不明晰、知识与能力不足、制定备选方案失误是导致班组决策失误的重要因素。改进HFACS模型中上下层级的因素之间存在显著的因果关系,且容易引发班组决策失误的路径有32条,其中发生概率最大的路径为组织设计不当→任务不明晰→压力过大→确定目标失误。由此可见,核电事故的发生是各致因因素相互作用的结果,降低班组决策失误概率需要阻断关键致因路径,加强对关键因素的管控。

基于高度冲突专家意见融合的水利工程应急决策

为解决D-S(dempster-shafer)证据理论无法有效集结高冲突专家意见对水利工程应急决策结果的影响,提出了基于Lance距离和信息熵改进D-S证据理论的水利工程应急决策中的专家意见融合方法。首先,利用改进的直觉模糊熵计算属性权重,充分考虑了决策信息的模糊性,得到各专家对方案的Mass函数;然后,使用Lance距离衡量任意两个专家之间的冲突和差异性得到评价证据的可信度,使用信息熵描述专家决策时的随机性问题得到评价证据的不确定度,并将二者折扣处理后得到专家综合权重,保证修正权重的准确合理;最后使用D-S证据组合规则计算修正后各专家对方案的Mass函数,得到最终决策结果。通过实例分析及方法对比验证了模型的有效性,结果表明,提出的方法可以有效集结高度冲突专家意见,与其他方法相比融合结果精度更高,考虑问题更为科学有效,可为水利工程应急决策提供依据。

基于神经网络的南方夏季蛋鸡舍环境与产蛋性能关系模型研究

为了解决南方地区夏季高温高湿蛋鸡舍环境质量难以精准评价与调控的问题,试验构建了基于神经网络的南方夏季蛋鸡舍环境与产蛋性能关系模型,将采集的蛋鸡舍多环境因子数据作为输入,将依据产蛋率划分的环境质量等级作为输出,对反向传播(BP)神经网络、径向基(RBF)神经网络和广义回归(GRNN)神经网络三种神经网络进行训练验证,得到蛋鸡舍环境质量初步评价结果,然后将归一化后神经网络组的输出作为改进D-S证据理论基本概率分配的输入,利用改进的D-S证据理论方法,融合计算得到蛋鸡舍环境质量的综合评价结果。结果表明:在神经网络初级融合评价有冲突的情况下,改进的D-S证据理论法对蛋鸡舍环境状态为差的支持率为0.796 4,而传统D-S证据理论方法、Murphy方法和邓勇方法对蛋鸡舍环境状态为差的支持率分别为0.698 7,0.687 8,0.694 0。说明试验构建的综合评价模型对蛋鸡舍环境的融合评价精度和准确率更高,可以有效解决融合决策失误问题,可用于南方夏季高温高湿蛋鸡舍环境的精准调控。

隧道环境内无人驾驶车辆目标决策两级信息融合感知策略

基于隧道内的特殊行驶环境和无人驾驶感知需求,选择合适的传感器及硬件搭建试验车辆,构建了毫米波雷达与摄像头多传感器融合的感知系统;基于YOLOv4目标级信息融合算法和改进D-S证据理论决策级信息融合算法,提出了一种“目标决策”两级信息融合策略;最后,在城市道路隧道环境内开展了感知信息两级融合验证试验,试验结果表明:相比单一的摄像头或毫米波雷达感知效果,基于摄像头与毫米波雷达传感器感知ROI区域关联实现的目标级融合结果可以提高9.51%的识别准确率,弥补了单一传感器在隧道环境内感知技术的不足;基于目标级融合感知结果,利用改进后的D-S证据理论算法再进行决策级融合,相比于单一的目标级融合结果,误检率降低了3.61%,显著提高了检测精度。采取多传感器感知信息目标决策两级融合策略能够满足隧道特殊环境内无人驾驶车辆可靠感知需求,为推动无人驾驶技术落地应用提供了理论与技术支撑。

基于D-S证据理论的决策级多特征融合苹果分级方法

针对单特征苹果分级的不确定性和低正确率,通过图像处理提取大小、形状、颜色和缺陷4类能反映苹果外观品质的主要特征,引入信息融合的思想,以单特征初步分级的结果作为证据,用D-S证据理论的方法进行决策级融合处理,实现苹果的多特征综合分级,进一步提高可靠性和分级正确率。80个测试样本的分级试验表明,苹果分级正确率达92.5%,与单特征分级相比,此方法正确识别率高、稳定性好、效果显著。

基于SVM和D-S证据理论的多特征融合杂草识别方法

针对单一特征识别杂草的低准确率和低稳定性,提出一种支持向量机(SVM)和D-S证据理论相结合的多特征融合杂草识别方法。在对田间植物图像处理的基础上,提取植物叶片的颜色、形状和纹理等3类视觉特征,分别以3类单特征的SVM分类结果作为独立证据构造基本概率指派(BPA),运用D-S证据组合规则进行决策级融合,根据分类判决门限给出最终的识别结果。试验结果表明,多特征决策融合识别方法正确识别率达到97%以上。

D-S证据理论中的基本概率赋值转换概率方法研究

D-S证据理论的基本概率赋值(BPA)能够有效地表示和处理不确定信息,在不确定信息处理中得到了越来越多的应用。但是如何基于BPA做出决策依然是一个有待解决的问题,将BPA转化为概率函数进行决策是一个简单可行的方法。文章基于证据理论中命题的信度函数和似真函数提供的信息,提出了一种新的基本概率赋值转换概率方法,该方法相比于现有经典TBM模型中Pignistic概率转换和基于似真函数的转换,可以更有效地利用系统已知的信息,实现基本概率赋值到概率分布的合理转换。算例表明了所提出方法的有效性。

基于期权定价理论的R&D投资决策思想

当今竞争激烈的市场要求企业重视Ｒ＆Ｄ投资。本文在探讨了单纯运用系统ＤＣＦ方法进行Ｒ＆Ｄ投资决策的局限性后，将Ｒ＆Ｄ投资与金融期权进行类比。

多属性决策及D-S证据理论在底板突水决策中的应用

针对煤矿底板突水包含很多不确定因素和时空随机性的特点,探索多属性决策理论在底板突水预测中应用研究。采用案例统计与专家分析相结合的方法,赋予影响底板突水各种因素的权重,建立突水概率指数法的融合规则,提出突水概率指数法预测采场工作面底板突水的新方法。在获得底板突水概率指数的基础上,基于D-S证据理论,建立由突水、临界、不突水、不确定组成的识别框架,构建以富水性指数、构造指数、隔水层指数、水压指数、矿压指数为证据体的突水融合决策模型。通过在肥城煤田实例应用,说明经证据体的多次融合,能够提高突水概率的可信度,降低不确定性,证明基于多属性决策和D-S证据理论建立的工作面底板突水两级融合决策模型的有效性和可行性。

基于D-S证据理论的群决策专家意见集结方法

将D S证据理论应用于群决策,指出其优势在于应用基本可信度分配函数描述专家意见,并能区分对所描述对象不知道和否定的差异。在专家意见集结上,Dempster证据合成规则有一定的局限性,当证据发生冲突时,会得到不合理的结果。定义了证据间的分歧度,提出一种专家意见集结方法,使得专家意见发生冲突时亦可得到合理的集结结果。