Reference number : GC-2017-Q1-03423, amendment number : , agreement type : G, recipient legal name : El-Diraby, Tamer (University of Toronto), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Managing our urban infrastructure systems decision making is increasingly becoming complex. A study found that in one municipality, the water management system spans 200 business processes. In Ontario, the cost of environmental assessment process rose 340% between 2010 and 2014. Average duration jumped from 19 to 27 months. According to one study, about 40% of expenditure in infrastructure construction can be saved if owners utilize advanced tools and deploy best practices. 
 
The goal of this proposal is to study , model and develop an environment for using social-semantic network analysis of project networks to infuse best practices in the decision making system. Project networks (PN) encompass the social networks of decision makers and citizens, networks of project documents, networks of physical project elements as represented in BIM (Building Information Modeling), their cost network (budget) and activity network (schedule). 
Students, through engagement in actual projects, will build at least 10 case studies. First, they will capture, through surveys, key attributes of each project&mdash;such as its actual social network of each (who communicates with who), decision paths, levels of actor influence. They will also collect (through Twitter interface) social media debates by citizens. Students will collect project documents, which form a semantic network of concepts. They will survey project teams to define top topics, performance levels, and the sentiment regarding both. 
Second, students will use relevant social and semantic network analysis algorithms to extract indicators for the same key attributes for each project. 
The two sets of key attributes (driven from surveys and from algorithms) will be compared, which can help define the suitability of social and semantic network analysis algorithms to the infrastructure domain. 
 
Through case studies and comparative analysis of surveys and algorithmic analysis students can develop models for: 
 Formation of sub groups or what is known communities of interest: the aim is to contrast formal and informal decision paths. 
 Opinion dynamics : to measure changes of stakeholders&rsquo; (including local communities) position and sentiment regarding main project topics. 
 Analysis of trust : monitor trust formation between major project players. 
 Ontology for PN : enumerating appropriate algorithms and their potential usage in infrastructure systems. 
 BIM dashboard : develop a portal to embed the results of PN analytics into the practice of project management. 
 
The contribution of this research work is full understating of PN and how to ingrain knowledge gained from their analysis into decision making cycles. This can help in 1) improve community engagement, 2) enhance decision making process, 3) reduce costs. All project participants can benefit from the results&mdash;particularly public officials and project decision makers as well as members of communities., program purpose french : Managing our urban infrastructure systems decision making is increasingly becoming complex. A study found that in one municipality, the water management system spans 200 business processes. In Ontario, the cost of environmental assessment process rose 340% between 2010 and 2014. Average duration jumped from 19 to 27 months. According to one study, about 40% of expenditure in infrastructure construction can be saved if owners utilize advanced tools and deploy best practices. 
 
The goal of this proposal is to study , model and develop an environment for using social-semantic network analysis of project networks to infuse best practices in the decision making system. Project networks (PN) encompass the social networks of decision makers and citizens, networks of project documents, networks of physical project elements as represented in BIM (Building Information Modeling), their cost network (budget) and activity network (schedule). 
Students, through engagement in actual projects, will build at least 10 case studies. First, they will capture, through surveys, key attributes of each project&mdash;such as its actual social network of each (who communicates with who), decision paths, levels of actor influence. They will also collect (through Twitter interface) social media debates by citizens. Students will collect project documents, which form a semantic network of concepts. They will survey project teams to define top topics, performance levels, and the sentiment regarding both. 
Second, students will use relevant social and semantic network analysis algorithms to extract indicators for the same key attributes for each project. 
The two sets of key attributes (driven from surveys and from algorithms) will be compared, which can help define the suitability of social and semantic network analysis algorithms to the infrastructure domain. 
 
Through case studies and comparative analysis of surveys and algorithmic analysis students can develop models for: 
 Formation of sub groups or what is known communities of interest: the aim is to contrast formal and informal decision paths. 
 Opinion dynamics : to measure changes of stakeholders&rsquo; (including local communities) position and sentiment regarding main project topics. 
 Analysis of trust : monitor trust formation between major project players. 
 Ontology for PN : enumerating appropriate algorithms and their potential usage in infrastructure systems. 
 BIM dashboard : develop a portal to embed the results of PN analytics into the practice of project management. 
 
The contribution of this research work is full understating of PN and how to ingrain knowledge gained from their analysis into decision making cycles. This can help in 1) improve community engagement, 2) enhance decision making process, 3) reduce costs. All project participants can benefit from the results&mdash;particularly public officials and project decision makers as well as members of communities., agreement title english : Knowledge Networks For Decision Making in Infrastructure Asset Management, agreement title french : Knowledge Networks For Decision Making in Infrastructure Asset Management, agreement number : RGPIN, agreement value : 140000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada