<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-03T15:52:43Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/DF55BFD7-F854-437F-AE41-2FAF5E1118BD" ns1:id="DF55BFD7-F854-437F-AE41-2FAF5E1118BD"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/20FCC8CC-E427-4980-80CC-83A276C3F040" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/7331B40A-628C-4414-8F50-9BD5B9D42437" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/851482B4-A4DD-482C-803E-4B084B90BADF" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/7331B40A-628C-4414-8F50-9BD5B9D42437" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/CE545469-A59F-4D04-AD8A-96102A9621CA" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2024-02-29T00:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/B9C559EF-5876-4CD3-AA25-244ECF480C2F" ns1:rel="FUND" ns1:start="2023-09-30T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10077663</ns2:identifier></ns2:identifiers><ns2:title>A Generative AI-Enabled Design Tool: Analysing problematic projects for improved productivity and cost efficiency</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Collaborative R&amp;D</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>This proposal is a **feasibility study** exploring a novel Generative AI approach addressing the critical construction challenge of time and cost overruns caused by re-work.

**Project entails the following activities**:

* Explore feasibility of developing an AI-enabled design tool as a solution using data from previous projects to analyse problematic projects identifying issues requiring additional oversight and management to avoid re-work and time/cost overruns.
* Provide AI expertise in developing/deploying Applied AI solutions for construction industry through collaboration between a party in need of the solution and a party that can develop it.

Critical factors limiting improvements in productivity in the construction industry are time and cost overruns due to unforeseen challenges and re-work. This project has the potential to unlock huge productivity improvements.

**Areas of focus**

Our project focuses on the following themes:

**Data Driven Decision Making:**

* **_Better project delivery_**: using past project data and industry wide benchmarks to develop estimators for accurate project plans; reducing likelihood of delays/cost overruns.
* **_Enhanced safety_**: using data to identify safety risks and proactive mitigating measures.
* _**Quality control**_: using data to monitor quality of workmanship on construction projects to meet quality standards and avoid rework.

**Design:**

* _**Generative design**_: using AI to generate new design options based on set parameters/constraints.
* **_Analysing problematic projects_**: using AI to analyse data from previous construction projects to identify patterns and predict future outcomes.Innovations

Our project sets the foundations for developing a Generative AI-enabled design tool to analyse problematic projects using data recorded by construction companies; offering valuable insights enables them to take proactive measures, allocate resources effectively, make informed decisions to mitigate risks; resulting in overall improved outcomes, productivity, and cost efficiency.

Our solution provides a step-change on how AI solutions are applied in the construction industry from a siloed problem-specific approach to a holistic system-based approach.

**Relevance**

Our project explores suitability of using Generative AI-enabled systems for the following capabilities:

1. **Data analysis**: analyse large volumes of project data; identifying patterns/anomalies.
2. **Risk prediction**: analyse historical project data; identifying/predicting risks/hazards likely leading to problematic projects.
3. **Real-Time Monitoring**: monitoring ongoing construction projects by integrating data; detecting deviations from the project plan.
4. **Natural Language Processing**: analyse textual project documentation data; extracting valuable insights.
5. **Benchmarking/Comparative Analysis**: compare project data with industry benchmarks/best practices; identifying projects performing below expectations or at higher cost/time overrun risks.
6. **Human-in-the-Loop**: explore inclusion of humans in guiding AI processes and influencing results.</ns2:abstractText></ns2:project>