Grant: Alliance Grants
Concordia University
$274,450.00
Evidence-based monitoring of construction projects through effective, reliable, and credible RFI analysis and management
EF 2023-24
Résumé
The construction industry is frequently characterized as 'adversarial,' due to its tendency to experience delays, cost overruns, conflicts, and even hostility. The root cause of the problem is the lack of collaborative behaviours among stakeholders. To address this issue, stakeholders are required to use Requests for Information (RFIs) throughout a project to identify and resolve design defects, discrepancies, and mistakes, making RFIs a critical measure of project health and a valuable source of evidence in dispute resolution. Therefore, the ultimate goal of this project is to improve collaborative behaviours and culture in the construction industry by "weaponizing" the RFIs. Two specific aims are proposed to achieve the project goal: 1) Develop advanced machine learning technologies to efficiently and effectively detect stakeholders' adversarial behaviours from the RFIs they produce during a project; and 2) Provide an RFI-centered construction project collaboration platform to facilitate stakeholder adoption of collaborative behaviours and production of high-quality RFIs.The two specific aims will be achieved through the following three research objectives: 1) RO1: Develop an RFI analytics engine that assesses project health and provides early warnings of stakeholder behaviour anomalies; 2) RO2: Develop an RFI mechanism to shape stakeholders' habits toward collaborative behaviours; and 3) RO3: Develop a trustworthy RFI-based digital collaboration platform for collecting, managing, and presenting evidence to foster collaborative behaviours in construction projects.Drawing on best practices in mHealth interventions to deliver a disruptive software solution that promotes the healthy implementation of construction projects, this project will involve expertise in construction management, artificial intelligence, information security, and behaviour design science. The project will train three postdoctoral fellows (PDFs), one research associate (RA), five Ph.D. and nine MASc students in a rotation, in transdisciplinary research, development, and practices.
Programme
Alliance Grants
Bénéficiaire
Emplacement
Quebec, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
585937-2023
Name nom
Zeng, Yong Y
Department département
Concordia Institute for Information Systems Engineering (CIISE)
Organization id
70
Institution établissement
Concordia University
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2023
Award amount
274450
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
600
Area of application group en
Construction, urban and rural planning
Area of application group fr
Construction, urbanisme et aménagement rural
Area of application en
Construction, urban and rural planning
Area of application fr
Construction, urbanisme et aménagement rural
Research subject code
1001
Research subject group en
Civil engineering
Research subject group fr
Construction engineering and management
Research subject en
Génie civil
Research subject fr
Génie et gestion du bâtiment
Application title
Evidence-based monitoring of construction projects through effective, reliable, and credible RFI analysis and management
Keyword
behaviour change; collaboration mechanism; construction project; information security; project anomaly; project health; RFIs (Request for information); RFI template; stakeholder behaviour; trustworthy evidence
Application summary
The construction industry is frequently characterized as 'adversarial,' due to its tendency to experience delays, cost overruns, conflicts, and even hostility. The root cause of the problem is the lack of collaborative behaviours among stakeholders. To address this issue, stakeholders are required to use Requests for Information (RFIs) throughout a project to identify and resolve design defects, discrepancies, and mistakes, making RFIs a critical measure of project health and a valuable source of evidence in dispute resolution. Therefore, the ultimate goal of this project is to improve collaborative behaviours and culture in the construction industry by "weaponizing" the RFIs. Two specific aims are proposed to achieve the project goal: 1) Develop advanced machine learning technologies to efficiently and effectively detect stakeholders' adversarial behaviours from the RFIs they produce during a project; and 2) Provide an RFI-centered construction project collaboration platform to facilitate stakeholder adoption of collaborative behaviours and production of high-quality RFIs.The two specific aims will be achieved through the following three research objectives: 1) RO1: Develop an RFI analytics engine that assesses project health and provides early warnings of stakeholder behaviour anomalies; 2) RO2: Develop an RFI mechanism to shape stakeholders' habits toward collaborative behaviours; and 3) RO3: Develop a trustworthy RFI-based digital collaboration platform for collecting, managing, and presenting evidence to foster collaborative behaviours in construction projects.Drawing on best practices in mHealth interventions to deliver a disruptive software solution that promotes the healthy implementation of construction projects, this project will involve expertise in construction management, artificial intelligence, information security, and behaviour design science. The project will train three postdoctoral fellows (PDFs), one research associate (RA), five Ph.D. and nine MASc students in a rotation, in transdisciplinary research, development, and practices.
