Managing the effect of weather on infrastructure projects through advanced analytics
Lead Research Organisation:
University of Reading
Department Name: Built Environment
Abstract
The student is addressing questions that inform on how uncertainty in weather forecasting can be incorporated into project planning on large construction/infrastructure projects that are weather-sensitive. Questions such as:
> What are the appropriate mathematical approaches to describing project tasks and interactions for handling delay uncertainties?
> What methods of statistical inference are best applied to the problem?
> How does the inclusion of probabilistic decision-making for weather delays impact on engineering project duration?
will be addressed. The novel contribution of the project comes from the application of mathematical models that allow the combination of discrete and continuous data as part of statistical inference of weather delays to construction projects. The student is, therefore, developing understanding of statistical modelling approaches through model development and application, as well as the sensitivity of construction/infrastructure projects to weather.
Aim: To develop a stochastic network model for making better (time- and cost-saving) planning and operational decisions for weather-sensitive infrastructure construction and operation project activities.
Objectives:
1. Identify relate isolated and combined weather events responsible for disruption to pre-planned construction and operation activities that may ultimately result in project delays. This will be performed by conducting an exhaustive literature review while counting on the records/knowledge of past and current projects made available through the industry partner.
2. Develop a statistical network model of construction project activity which enables uncertainty in weather influence, as well as uncertainty in forecast information to be used to evaluate the probability of project delay associated with weather. Evaluate the models ability to address weather impacts on construction project completion.
3. Expand the network model to account for spatial variation of weather impacts for dispersed projects and demonstrate impact on project delay when optimising project sequencing for weather.
4. Incorporate ensemble forecast products to the models to address the impact of different forecast lead times to project planning.
> What are the appropriate mathematical approaches to describing project tasks and interactions for handling delay uncertainties?
> What methods of statistical inference are best applied to the problem?
> How does the inclusion of probabilistic decision-making for weather delays impact on engineering project duration?
will be addressed. The novel contribution of the project comes from the application of mathematical models that allow the combination of discrete and continuous data as part of statistical inference of weather delays to construction projects. The student is, therefore, developing understanding of statistical modelling approaches through model development and application, as well as the sensitivity of construction/infrastructure projects to weather.
Aim: To develop a stochastic network model for making better (time- and cost-saving) planning and operational decisions for weather-sensitive infrastructure construction and operation project activities.
Objectives:
1. Identify relate isolated and combined weather events responsible for disruption to pre-planned construction and operation activities that may ultimately result in project delays. This will be performed by conducting an exhaustive literature review while counting on the records/knowledge of past and current projects made available through the industry partner.
2. Develop a statistical network model of construction project activity which enables uncertainty in weather influence, as well as uncertainty in forecast information to be used to evaluate the probability of project delay associated with weather. Evaluate the models ability to address weather impacts on construction project completion.
3. Expand the network model to account for spatial variation of weather impacts for dispersed projects and demonstrate impact on project delay when optimising project sequencing for weather.
4. Incorporate ensemble forecast products to the models to address the impact of different forecast lead times to project planning.
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| NE/W502923/1 | 01/04/2021 | 31/03/2022 | |||
| 1942842 | Studentship | NE/W502923/1 | 01/10/2017 | 23/04/2026 | Josephine Lloyd-Papworth |