Mapping the Underworld of Buried Utilities - A Hybrid Sensing Approach

绘制地下公用设施的地下世界地图 - 混合传感方法

• 批准号: 1462638
• 负责人: Hubo Cai
• 金额: $ 21.65万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-15 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1462638&HistoricalAwards=false
• 关键词: Mapping; Underworld; Buried; Utilities; Hybrid

This grant will pioneer a novel hybrid sensing approach to accurately map underground utilities using both sensor and non-sensor data. For the massive underground utilities, knowing their locations, dimensions, spatial configurations, and materials is critical to effectively maintaining, restoring, and upgrading them to provide essential services to the society: water, electricity, gas, and the Internet. Lack of accurate utility location information is the main reason for over six million incidents of excavation damage to utilities every year, causing injuries, fatalities, and property damage, on the order of billions of dollars. Current sensor-based utility mapping methods are less effective in the utility-congested urban environment where many utility lines occupy a tight space, causing sensor signal interferences and consequent erroneous utility information. The anticipated approach will create a method to supplement textual data from utility regulations to digital sensor data, enabling automated and more accurate mapping of underground utilities. With accurate utility information, 22% of the excavation-related incidents could be avoided. Therefore, results from this research will benefit society through cost savings, reduced hazards to citizens, consistency in services supplied by critical underground lifelines, and improved sustainability of urban communities. Research outcomes will be integrated into engineering curriculum, K-12 education, and industry workshops to promote subsurface utility engineering as a specialization area.The objective of this research is to create a hybrid, multi-sensing method to automate the process of interpreting and fusing heterogeneous utility data to generate accurate utility maps. This novel approach is expected to shift the current paradigm in mapping and labeling underground utilities in utility-congested urban areas by devising methods that exploit utility specifications and plans as a "virtual sensor." This approach will provide contextual information for underground utilities. This information will be integrated with information from the processing of ground penetrating radar(GPR) scans. The research team will create algorithms to automatically detect multiple and transformed signatures from underground utilities created with the GPR scans, estimate utility locations, dimensions, and spatial configurations, and recognize pipe materials. Natural language processing algorithms will be devised to mine the textual data in utility specifications and plans. These algorithms will enable extraction of spatial rules constraining utilities and their surroundings, which will be fed into a spatial reasoning algorithm. This use of utility specifications and plans for this purpose is revolutionary. A novel framework for utility data/information fusing will be created to address spatial inference and data correspondence computation problems in utility mapping applications that are as of yet untapped. The credibility of the system and methods will be validated at each research phase through experiments.

这项资助将开创一种新的混合传感方法，使用传感器和非传感器数据准确地绘制地下公用设施。对于庞大的地下公用设施，了解它们的位置，尺寸，空间配置和材料对于有效地维护，恢复和升级它们以向社会提供基本服务至关重要：水，电，气和互联网。缺乏准确的公用设施位置信息是每年超过600万起公用设施挖掘损坏事件的主要原因，造成数十亿美元的伤害、死亡和财产损失。当前基于传感器的效用映射方法在公用事业拥挤的城市环境中不太有效，其中许多公用事业线路占据了狭小的空间，导致传感器信号干扰和随之而来的错误的公用事业信息。预期的方法将创建一种方法，将公用事业法规的文本数据补充到数字传感器数据中，从而实现地下公用事业的自动化和更准确的映射。通过准确的公用事业信息，可以避免22%的挖掘相关事故。因此，这项研究的结果将通过节省成本、减少对公民的危害、关键地下生命线提供的服务的一致性以及提高城市社区的可持续性来造福社会。研究成果将被整合到工程课程，K-12教育和行业研讨会，以促进地下公用事业工程作为一个专业领域。本研究的目标是创建一个混合，多传感方法，自动化的过程中解释和融合异构公用事业数据，以生成准确的公用事业地图。这种新的方法有望改变目前的模式，在公用事业拥挤的城市地区的地下公用事业的绘图和标记的设计方法，利用公用事业规范和计划作为一个“虚拟传感器”。“这种方法将为地下公用事业提供背景信息。这一信息将与处理探地雷达扫描的信息相结合。研究团队将创建算法，以自动检测使用GPR扫描创建的地下公用设施的多个和转换的签名，估计公用设施位置，尺寸和空间配置，并识别管道材料。自然语言处理算法将被设计用于挖掘公用事业规范和计划中的文本数据。这些算法将使提取的空间规则约束公用事业和他们的周围环境，这将被送入一个空间推理算法。为此目的使用公用事业规范和计划是革命性的。一个新的框架，效用数据/信息融合将创建解决空间推理和数据对应计算问题的效用映射应用程序，尚未开发。在研究的各个阶段，通过实验验证系统和方法的可信性。