• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >An approach for fusing data from multiple sources to support construction productivity analyses.
【24h】

An approach for fusing data from multiple sources to support construction productivity analyses.

机译：一种融合来自多个来源的数据以支持建筑生产率分析的方法。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Construction companies need to continuously improve construction productivity performance to stay competitive. Frequent productivity monitoring of on-going construction activities helps in assessing a project's performance and enables identification of opportunities for improvement. Such productivity monitoring requires data to be fused from multiple data sources since a single data source provides only a portion of the data necessary to perform a comprehensive productivity analysis.;The advent of reality capture technologies, such as Global Positioning System (GPS) devices, Radio Frequency Identification Tags (RFID), laser scanners, onboard instrumentations (OBI) installed in construction equipment, along with the existing project-specific data sources, such as time-card, schedule and as-designed drawings, provides opportunities for capturing and recording what is happening at a job site. To leverage the available data sources to support construction productivity monitoring, there is a need for a formal and generalized approach for data fusion since existing data fusion approaches in construction management domain are designed to work for a specific task, such as defect detection and tracking of materials at a job-site, and do not necessarily work for tasks that they were not designed for.;The main objective of my PhD research is to develop a formalized multi-source data fusion approach to support construction productivity monitoring. In developing such a formalized approach, I have tried to answer two main research questions. The first research question aims at formalizing the generation of a customized process based on user queries for fusing multiple data sources to support construction productivity analysis. The second research question aims at developing temporal and spatial reasoning mechanisms to support the generation of fused data to address productivity related queries of construction engineers and managers.;In order to develop a customized process for fusing multiple data sources, I have tried to answer three main sub-questions. The first question aims at developing an approach to capture user queries related to productivity monitoring. I developed a declarative language to capture user queries so that a project engineer can express his/her queries in natural language and yet it is computer interpretable, and hence it would be possible to automatically identify data items and their levels of details required in a query. The second sub-question is to identify a set of applicable data sources from a set of available data sources to answer a given user query. In addressing this second sub-question, I have developed a data fusion ontology which captures the following characteristics of a data source: (a) level of detail, (b) representation, (c) reference system and (d) list of data items. I also developed a reasoning mechanism based on graph theory that utilizes the ontology to identify applicable set of data sources.;Once the applicable set of data sources are identified, the next problem is to identify a sequence of steps to generate fused data from the applicable set of data sources. I developed two approaches, based on the GraphPlan and Hierarchical Task Network (HTN) specifically, to generate customized sequence of steps (i.e., data fusion plan) to generate fused data. The second research question is aimed at developing temporal and spatial reasoning mechanisms to synchronize the varying levels of details of data sources to enable the generation of fused data for construction productivity monitoring. I developed two different approaches: (a) interpolation and (b) closest neighbor to deal with synchronization problem of spatial and temporal data sources during merging.;To validate the generality of the query capture language and the developed ontology, I used different types of productivity-related user queries, different types of construction-related data sources having different levels of details, representations, and data items. The developed spatial and temporal reasoning mechanisms are validated based on the accuracy of two different approaches. The main contributions of the my PhD research are: (a) query capture language, (b) data fusion ontology, (c) reasoning mechanism to identify applicable data sources, (d) reasoning mechanisms to generate data fusion plan, and (e) spatial and temporal reasoning mechanisms to generate fused data. The practical implication of my PhD research is that it can possibly assist in leveraging different underutilized and valuable data sources for construction productivity analyses.
机译：建筑公司需要不断提高建筑生产力，以保持竞争力。对正在进行的建筑活动进行频繁的生产率监控有助于评估项目的绩效并确定改进的机会。由于单个数据源仅提供执行全面生产力分析所需的部分数据，因此此类生产力监视要求将数据与多个数据源融合在一起。诸如全球定位系统（GPS）设备之类的现实捕捉技术的问世，安装在建筑设备中的射频识别标签（RFID），激光扫描仪，车载仪器（OBI）以及现有的特定于项目的数据源，例如时间表，时间表和设计图纸，为捕获和记录提供了机会工作现场发生了什么。为了利用可用的数据源来支持建筑生产率监控，需要一种正式且通用的数据融合方法，因为建筑管理领域中的现有数据融合方法被设计用于特定任务，例如缺陷检测和跟踪。在工作现场使用的材料，并且不一定能完成非设计任务。；我的博士学位研究的主要目标是开发一种规范化的多源数据融合方法，以支持建筑生产率监控。在开发这种形式化方法时，我试图回答两个主要的研究问题。第一个研究问题旨在规范化基于用户查询的定制过程的生成，以融合多个数据源以支持建筑生产率分析。第二个研究问题旨在开发时间和空间推理机制，以支持融合数据的生成，以解决建筑工程师和管理人员与生产力相关的问题。为了开发定制的融合多个数据源的过程，我尝试回答三个问题。主要子问题。第一个问题旨在开发一种方法来捕获与生产力监视有关的用户查询。我开发了一种声明性语言来捕获用户查询，以便项目工程师可以用自然语言表达他/她的查询，但是它是计算机可解释的，因此可以自动识别查询中的数据项及其详细程度。第二个子问题是从一组可用数据源中标识一组适用的数据源，以回答给定的用户查询。在解决第二个子问题时，我开发了一种数据融合本体，它捕获了数据源的以下特征：（a）详细程度，（b）表示形式，（c）参考系统和（d）数据项列表。我还基于图论开发了一种推理机制，该机制利用本体论来识别适用的数据源集;一旦确定了适用的数据源集，下一个问题就是要确定一系列步骤，以便从适用的数据源中生成融合数据数据源集。我专门基于GraphPlan和分层任务网络（HTN）开发了两种方法来生成定制的步骤序列（即数据融合计划）以生成融合数据。第二个研究问题旨在开发时间和空间推理机制，以同步数据源详细信息的不同级别，从而能够生成融合数据以进行建筑生产率监控。我开发了两种不同的方法：（a）插值和（b）最近邻以处理合并期间的空间和时间数据源的同步问题;;为了验证查询捕获语言和已开发本体的通用性，我使用了不同类型的与生产率相关的用户查询，具有不同级别的详细信息，表示形式和数据项的与建筑相关的数据源的不同类型。基于两种不同方法的准确性验证了已开发的空间和时间推理机制。我的博士研究的主要贡献是：（a）查询捕获语言，（b）数据融合本体，（c）识别适用数据源的推理机制，（d）生成数据融合计划的推理机制，以及（e）生成融合数据的时空推理机制。我的博士研究的实际意义是，它可能可以帮助利用不同的未充分利用且有价值的数据源进行建筑生产率分析。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号