讲座：建成环境的决策支持系统
Lecture: Decision Support Systems for the Built Environment
时间：
2019年10月22日  
15:30-17:00
地点：
四平路校区中德楼三楼中德联合创新实验室
主讲人：
Prof. Anthony G Cohn，英国皇家工程院院士(Fellow of RAE)，英国利兹大学计算机学院人工智能方向教授(University of Leeds)
讲座摘要：
In the talk Prof. Cohn will summarise work conducted at the University of Leeds in collaboration with colleagues on the NetTUN and Assessing the Underworld projects on various aspects of tunelling and underground works, and in particular three Decision Support Systems we have constructed to support such operations. 
(1) A Decision Support System called TULIPS to assist in the operation of a Tunnel Boring Machine (TBM) fitted with seismic and GPR sensors to determine the nature of the ground ahead of the TBM. In order to facilitate the interpretation of the imaging data captured by this system, an automatic event detection and tracking method is presented. This is supported by a novel web-based visualisation platform to help TBM operators efficiently manage, process and visualise the TBM parameters, the geology map created by geo-experts based on boreholes, and especially the  tracked imaging data. 
(2) A Decision Support System called PADTUN to assist in determine the scheduling of maintenance of tunnels using survey data. The core of PADTUN is a family of ontologies which represent the main concepts and relations associated with pathology assessment, and capture the decision process concerning tunnel maintenance. Tunnel inspection data is linked to these ontologies to take advantage of inference capabilities offered by semantic technologies. Machine learning is then used to infer which tunnel segments most urgently require attention.  
(3) A Decision Support System designed to help holistic decision making when undertaking streetworks. Sustainable streetworks requires an integrated approach taking account of the complex inter-asset relationships between human activities, different city infrastructure assets and the environment. To facilitate decision making by relevant stakeholders, an integrated web-based Decision Support System is presented which combines experts’ domain knowledge, multiple geospatial datasets and an inference engine for automated inference of potential consequences.
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