Article: Integrating artificial intelligence and the internet of things for smart laboratory engineering construction and management team optimisation Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.48 pp.40 - 59 Abstract: Artificial intelligence (AI) and internet of things (IoT) convergence brings immense opportunity to convert the laboratory environment into intelligent, adaptive systems. This study proposes an integrated AI-IoT framework for smart laboratory engineering construction and engineering management team optimisation, which overcomes the current shortcomings in resource efficiency, task scheduling, and environmental control to some extent. In this system, real-time IoT sensor networks monitor ecological and operational conditions; meanwhile, LSTM models are applied for predictive environmental control, genetic algorithms for dynamic task scheduling, and SVM classifiers for human activity recognition. The framework was deployed in a research laboratory for six months, and the system achieved substantial improvements: energy consumption was reduced by 28.48%, equipment downtime by 54.37%, and task overlap and average task duration were significantly minimised. Additionally, predictive maintenance accuracy reached approximately 93.2%, eliminating passive interventions and improving equipment availability. Since intelligent task allocation incorporates fault tolerance considerations, workload imbalance in task execution is alleviated, and staff satisfaction is enhanced. Our results demonstrate that a collaborative AI-IoT approach can effectively improve infrastructure efficiency and worker productivity. In this context, the proposed framework provides a scalable, sustainable, and context-aware solution for next-generation laboratory environments in academic and industrial domains. Inderscience Publishers - linking academia, business and industry through research

Title: Integrating artificial intelligence and the internet of things for smart laboratory engineering construction and management team optimisation

Authors: Weiwei Zhang; Weihong Chen; Qiurong Wang; Junliang Ma; Fang Zhang

Addresses: School of Civil Engineering, Shaoxing University, Shaoxing, 312000, China; Tongchuang Engineering Design Co., Ltd., Shaoxing, 312000, China ' Tongchuang Engineering Design Co., Ltd., Shaoxing, 312000, China ' Tongchuang Engineering Design Co., Ltd., Shaoxing, 312000, Zhejiang, China ' Zhejiang Anke Engineering Testing Co., Ltd., Shaoxing, 312000, Zhejiang, China ' School of Civil Engineering, Shaoxing University, Shaoxing, 312000, China

Abstract: Artificial intelligence (AI) and internet of things (IoT) convergence brings immense opportunity to convert the laboratory environment into intelligent, adaptive systems. This study proposes an integrated AI-IoT framework for smart laboratory engineering construction and engineering management team optimisation, which overcomes the current shortcomings in resource efficiency, task scheduling, and environmental control to some extent. In this system, real-time IoT sensor networks monitor ecological and operational conditions; meanwhile, LSTM models are applied for predictive environmental control, genetic algorithms for dynamic task scheduling, and SVM classifiers for human activity recognition. The framework was deployed in a research laboratory for six months, and the system achieved substantial improvements: energy consumption was reduced by 28.48%, equipment downtime by 54.37%, and task overlap and average task duration were significantly minimised. Additionally, predictive maintenance accuracy reached approximately 93.2%, eliminating passive interventions and improving equipment availability. Since intelligent task allocation incorporates fault tolerance considerations, workload imbalance in task execution is alleviated, and staff satisfaction is enhanced. Our results demonstrate that a collaborative AI-IoT approach can effectively improve infrastructure efficiency and worker productivity. In this context, the proposed framework provides a scalable, sustainable, and context-aware solution for next-generation laboratory environments in academic and industrial domains.

Keywords: artificial intelligence; AI; internet of things; IoT; smart laboratory; engineering management; predictive maintenance; task scheduling; environmental monitoring.

DOI: 10.1504/IJICT.2025.150949

International Journal of Information and Communication Technology, 2025 Vol.26 No.48, pp.40 - 59

Received: 11 Jul 2025
Accepted: 16 Aug 2025
Published online: 05 Jan 2026 *

Title: Integrating artificial intelligence and the internet of things for smart laboratory engineering construction and management team optimisation

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