Transforming high-rise residential maintenance: a 3D spatio-temporal model utilizing Industry 4.0 and lean principles
Authors: Mehmood Usman , Ujang Uznir , Azri Suhaibah , Choon Tan Liat 
Published: November 2024

Purpose

The purpose of this paper is to develop and demonstrate a comprehensive 3D spatio-temporal maintenance management model for high-rise residential buildings by integrating Industry 4.0 technologies and lean maintenance principles. This model aims to optimize maintenance scheduling, enhance resource utilization and improve decision-making processes. By leveraging advanced data visualization and predictive analytics, this study seeks to address the complexities of building maintenance, ensure timely interventions, reduce downtime and extend the lifespan of building assets, ultimately leading to more efficient and sustainable maintenance management practices.

Design/methodology/approach

Integrating state-of-the-art technologies such as big data analytics and artificial intelligence into the proposed model is geared towards benefiting from optimized maintenance scheduling and resource allocation, hence achieving minimum asset downtime and extension in asset life. This is being done through the digitization of paper maps, the development of 3D building models in AutoCAD and SketchUp and the placing of the developed models into ArcGIS Pro. The PostgreSQL database with PostGIS extension supports optimal storage and management of spatial data towards real-time updates and advanced analyses.

Findings

The results revealed that the model enhances maintenance planning considerably better than traditional methods due to the revelation of meaningful patterns and trends that are not visible in conventional visualization methods. Temporal analysis indicates increasing needs for maintenance through time, whereas spatial analysis can point out the units that require special attention. The spatiotemporal analysis is needed to determine overall maintenance requirements for better decision-making. The work demonstrated that 3D visualization of maintenance activities performed over building representation helps facility managers in better decision-making related to task planning for performance improvement concerning building and tenant satisfaction.

Research limitations/implications

The study’s current limitations include the reliance on specific datasets and technologies, which may need adaptation for broader applications. Future research could explore further integration with additional building types and longitudinal studies to assess long-term impacts.

Practical implications

The 3D visualization of maintenance activities over building representation aids facility managers in better decision-making related to task planning, improving building performance and tenant satisfaction. This integrated approach provides significant benefits in efficiency, resource use and sustainability.

Originality/value

The originality of this paper lies in its innovative integration of 3D spatio-temporal data with Industry 4.0 technologies and lean maintenance principles to create a comprehensive maintenance management model for high-rise residential buildings. Unlike traditional approaches, this model combines advanced data visualization, real-time analytics and predictive maintenance strategies within a unified geographic information system framework. This holistic approach not only enhances maintenance planning and resource allocation but also provides a proactive, data-driven methodology that significantly improves the efficiency and effectiveness of maintenance management, addressing the unique challenges of high-rise residential building maintenance.