This programme was initiated to address the growing need for intelligent, data-driven decision-making in the planning, development, and maintenance of infrastructure assets. The programme aimed to harness the power of artificial intelligence and machine learning to build predictive capabilities that would transform how asset managers, engineers, and government agencies forecast costs, assess degradation, and plan lifecycle strategies.

Originally focused on developing predictive models for construction cost estimation and maintenance forecasting, the programme expanded to include conversational interfaces and decision-support tools that integrate seamlessly with inspection data, enterprise systems, and global infrastructure datasets. This expansion enabled a more intuitive and accessible approach to interacting with complex asset data, empowering users to make faster and more informed decisions.

Programme Objectives

Develop Predictive Modelling Capabilities: Create machine learning models to estimate construction costs, forecast maintenance and repair needs, predict asset degradation, and project long-term value and revenue.

Integrate with Inspection and Assessment Data: Ensure interoperability with technical inspection platforms and condition assessment tools to enrich predictive outputs with real-world asset data.

Enable Natural Language Interaction: Build AI-powered conversational interfaces that allow users to query asset data, generate reports, and receive recommendations using intuitive, natural-language inputs.

Support Strategic Planning and Budgeting: Provide actionable insights for infrastructure planning, budgeting, and lifecycle management through dynamic forecasting and scenario analysis.

Ensure Enterprise System Compatibility: Design solutions that integrate with ERP systems, asset management platforms, and other enterprise tools to support seamless data exchange and operational alignment.

Facilitate Research and Education: Offer tools for academic, policy, and business research, including modules that explain asset structures, pricing models, and governance frameworks.

Maintain Real-Time Data Accuracy: Continuously sync with public APIs and official sources to ensure predictive models and insights reflect the most current data available.