Data Management for Building Information Modelling in a Real-Time Adaptive City Platform

TL;DR

This paper presents a data architecture for an Adaptive City Platform that integrates Building Information Modelling, Building Management Systems, and real-time IoT sensor data to enable timely analysis and decision-making in smart buildings.

Contribution

It introduces a novel data architecture that combines BIM, BMS, and sensor data for real-time analysis in smart building environments.

Findings

Enables integrated analysis of BIM and sensor data in real-time.

Addresses interoperability challenges in smart building systems.

Supports faster decision-making in adaptive city platforms.

Abstract

Legacy Building Information Modelling (BIM) systems are not designed to process the high-volume, high-velocity data emitted by in-building Internet-of-Things (IoT) sensors. Historical lack of consideration for the real-time nature of such data means that outputs from such BIM systems typically lack the timeliness necessary for enacting decisions as a result of patterns emerging in the sensor data. Similarly, as sensors are increasingly deployed in buildings, antiquated Building Management Systems (BMSs) struggle to maintain functionality as interoperability challenges increase. In combination these motivate the need to fill an important gap in smart buildings research, to enable faster adoption of these technologies, by combining BIM, BMS and sensor data. This paper describes the data architecture of the Adaptive City Platform, designed to address these combined requirements by enabling integrated BIM and real-time sensor data analysis across both time and space.