AIM | CIM

An Asset Information Model (AIM) may be defined as an organised collection of information that supports asset management and maintenance decisions throughout the operational lifecycle of a built asset.[1]

In this context, information models provide structured data that may contribute to representing infrastructure systems within digital twin environments.

Key concepts

Both AIM and CIM provide foundational datasets for digital twins by ensuring that the physical world is accurately represented in the virtual environment:

• Real-Time Updates: IoT sensors integrated into AIM/CIM enable continuous updates to the digital twin, ensuring it reflects current conditions.

• Data Contextualization: By linking spatial (CIM) or asset-specific (AIM) data with operational insights, they provide actionable intelligence.

• Collaboration Across Stakeholders: Centralized repositories facilitate collaboration between engineers, planners, operators, and policymakers.

In summary, AIM provides detailed asset-level information while CIM focuses on broader urban-scale integration. Together, they form complementary pillars for acquiring high-quality data that powers digital twins across industries and cities.

AIM / CIM contribute to the ability of digital twins to represent infrastructure systems as interconnected environments in which multiple operational systems exchange information across different spatial or organisational contexts. Infrastructure services often rely upon data generated by different platforms that may represent assets or connectivity in different ways.

City Information Modelling (CIM) integrates Geographic Information Systems (GIS), Building Information Modelling (BIM), and Internet of Things (IoT) data to establish a multidimensional framework for urban data management and intelligent decision‑making.[2]

Understanding how infrastructure components are described both at asset scale and environmental scale may therefore support decision‑making activities relating to operational coordination, resource management or infrastructure planning.

Infrastructure systems are influenced not only by their physical characteristics, but also by how information relating to those systems is structured and exchanged. Incorporating structured data models within digital twin environments enables infrastructure systems to be analysed in relation to operational or spatial contexts.

City Information Models are often described as precursors for the transition toward Urban Digital Twins, supporting the integration of BIM and GIS into a shared geospatial framework.[4]

By representing infrastructure systems using consistent information structures across asset and urban scales, digital twins can support coordinated planning and operational activities across organisational and sectoral boundaries.

Mechanisms

Role of AIM in Data Acquisition

Asset Information Modelling focuses on creating a comprehensive digital representation of individual assets (e.g., buildings, infrastructure) throughout their lifecycle. AIM supports data acquisition in the following ways:

• Centralized Repository for Asset Data
  AIM consolidates all asset-related information—design models, operational data, maintenance records, and IoT sensor inputs—into a single source of truth. This ensures that all stakeholders have access to accurate and up-to-date data.

• Integration of IoT Sensors
  By incorporating IoT devices, AIM enables real-time monitoring of asset conditions (e.g., temperature, energy usage, structural integrity). These sensors continuously feed data into the digital twin for ongoing updates.

• Lifecycle Data Management
  AIM captures data across an asset's lifecycle—from design and construction (via Project Information Models or PIMs) to operation and maintenance. This ensures seamless handovers between phases and provides a rich dataset for analysis.

• Standardization and Compliance
  AIM adheres to standards like ISO 19650 to ensure consistency in data formats and processes. This standardization facilitates interoperability between systems and enhances the reliability of acquired data.

• Predictive Maintenance
  With real-time data acquisition from sensors and historical records stored in AIM, digital twins can predict when maintenance is required, reducing downtime and optimizing operations.

Role of CIM in Data Acquisition

City Information Modelling extends the principles of AIM to urban environments, focusing on integrating geospatial data with building information models (BIM) to represent cities or districts as dynamic systems. CIM supports data acquisition as follows:

• Integration of GeoBIM
  CIM combines Geographic Information Systems (GIS) with BIM to create "GeoBIM" models. This allows for the collection of spatial data (e.g., topography, infrastructure layout) alongside building-level details.

• Dynamic Data Collection from Urban Sensors
  CIM leverages IoT devices, remote sensing technologies (e.g., drones), and city-wide networks to acquire real-time environmental data such as traffic flow, air quality, or energy consumption.

• Cross-Sector Data Aggregation
  CIM enables the integration of diverse datasets from multiple sectors (e.g., utilities, transportation) into a unified platform. This breaks down silos between organizations and provides a holistic view of urban systems.

• Simulation and Scenario Planning
  By collecting real-time data from urban sensors and historical datasets, CIM supports simulations for urban planning scenarios such as disaster response or infrastructure optimization.

• Standardized Frameworks for Interoperability
  CIM uses frameworks like the Common Information Model (CIM) to unify datasets from different sources into a consistent format that can be easily shared across stakeholders.

Information models may be acquired through operational systems and integrated within digital twin environments to support further analysis. Once incorporated into the system, structured inputs may be interpreted within the Analytics Layer to identify patterns in system performance or integrated within the Modelling and Simulation Layer to explore how infrastructure systems respond to different operational conditions.

City Information Models may provide digital representations and simulations of urban environments composed of large quantities of geospatial data obtained through BIM and GIS integration.[3]

Insights derived from modelling infrastructure components may also inform Service Layer activities such as risk management or resource allocation by enabling stakeholders to understand how infrastructure systems are likely to perform under varying conditions.

Examples

In infrastructure asset management, Asset Information Models may provide structured operational data relating to equipment performance, maintenance schedules or ownership attributes that support lifecycle decision‑making.[1]

At urban scale, City Information Models may integrate geospatial and infrastructure data to support planning or simulation activities in smart city environments through the use of BIM, GIS and IoT technologies.[2]

Organizations developing digital twins must establish robust processes for defining, procuring, and assuring asset information before it can support operational use or form a reliable digital twin foundation. The Environment Agency's approach demonstrates this principle through its Data Store Rules and Visualization (DRV) service. When the agency receives asset data from suppliers, it is validated against specific information requirements using a central rules library containing over 170 business, technical, and spatial rules. Data that passes assurance is then imported into a master repository ready for visualization, analytics, and reuse—creating a system of record where asset information is traceable and trustworthy. This structured approach to information management ensures that the quality and completeness of asset data directly supports better decision-making across the organization, all while laying essential groundwork for digital twin implementations.