Research and thoughts on the collaborative management system of architecture laboratories in the digital age: A case study of the Architecture Laboratory at Nanjing Tech University

In the digital age, architecture laboratories are crucial hubs for integrating design, technology, and construction processes; however, they face systemic management inefficiencies. This study aims to address the core challenge of insufficient management effectiveness across the experimental workflow by identifying and resolving key bottlenecks in traditional models, including resource scheduling conflicts, inaccurate environmental control, data governance barriers, and lagging equipment maintenance. Primarily, this study aims to propose an innovative “experimental full-process” linkage management system that can enhance resource utilization, ensure precise environmental parameters, facilitate multisource data integration, and implement predictive maintenance. Using Nanjing Tech University as a case study, this research provides a scalable solution to help architecture schools improve laboratory efficiency, support interdisciplinary innovation, and align with digital transformation trends. This study emphasizes a shift from fragmented, experience-based management to an integrated, data-driven approach, fostering a collaborative ecosystem for education and research.

This study employs a case-based methodology, focusing on Nanjing Tech University’s architecture laboratories, beginning with an in-depth analysis of current management issues, identified through empirical observation and literature review. Key problems are then categorized into four phases: pre-experiment resource scheduling, in-experiment environmental control, post-experiment data management, and equipment maintenance. The causes are systematically examined, including passive scheduling mechanisms, lack of intelligent environmental monitoring, absence of unified data standards, and reactive maintenance practices. Based on this examination, this study designs a linkage management framework structured around the “full experimental process,” covering the pre-experiment, experiment, post-experiment, and support stages. This framework integrates four dimensions: physical systems for hardware and sensing, platform systems for resource and data coordination, security systems for risk management, and normative systems for standardization. This methodology combines qualitative assessment with conceptual modeling to develop actionable implementation paths, thereby ensuring practicality for digital-era challenges.

The findings reveal that traditional management in architecture laboratories can experience inefficiencies due to uncoordinated scheduling, inadequate environmental monitoring, disparate data sources, and delayed equipment upkeep. For example, resource scheduling conflicts can arise from passive booking systems without dynamic prioritization, leading to underuse of spaces like model workshops and overuse of devices such as laser cutters. Moreover, environmental control issues stem from manual adjustments and from failing to meet precision requirements for experiments such as acoustic testing. Data fragmentation also persists due to heterogeneous formats and the lack of integration standards, hindering cross-project analysis. Finally, equipment maintenance remains reactive, resulting in increased downtime and safety risks. In response, this study proposes a holistic linkage management system comprising an intelligent scheduling platform for real-time resource allocation, an Internet of Things-based environmental monitoring network for automated parameter adjustment, a unified data platform to harmonize diverse data types, and a predictive maintenance mechanism to mitigate equipment risk.

The “experimental full-process” linkage management system effectively addresses digital-age challenges in architecture laboratories. By transitioning from isolated practices to an integrated approach, management efficacy is enhanced, promoting optimal resource use, precise environmental control, seamless data fusion, and proactive equipment maintenance. This framework addresses immediate issues, such as scheduling conflicts and data silos, while also supporting broader goals, including interdisciplinary collaboration and digital advancement.