Skip to content
#edge computing Open access

A cyber-physical production control paradigm for site waste mitigation: Synchronizing digital twin technology and lean construction principles through nanotechnology

Oct 2026 · Experimental and Theoretical NANOTECHNOLOGY · 0 citations · 6 references

Abstract

The construction industry continues to suffer from substantial productivity losses due to inefficient operations, wasteful use of materials, broken equipment and a lack of cohesive production management. Lean Construction is a good way to think about getting rid of waste and it remains difficult to implement because information is often delayed or not complete. Similarly, Digital Twin technology creates virtual, moving versions of construction sites, but it does not inherently offer a predictable way to reduce waste. This paper introduces a Digital Twin-Lean Management (Nano-DTLM) framework that uses nanotechnology to optimize Cyber-physical systems, Internet of Things (IoT), Digital Twin technology, Lean Construction and smart nanosensor technologies for real-time production enhancement. The proposed framework is based on edge-computing analytics, graphene-based strain sensors, nano-enabled RFID tags, nanoscale environmental sensors, and enhanced sensing for more accurate decision making, structure monitoring and foresight. To test the framework using 4D BIM, UWB positioning, RFID, telematics, and nano-enabled sensing technologies, a controlled field experiment is conducted in a 15-story commercial building project in Dubai, UAE. When tested, the experimental implementation reduced crew idle time by 29.7%, equipment downtime by 30.9%, distance that materials had to be transported by 21.9% and increased worker productivity by 21.4%. Statistics demonstrated significant improvements (p < 0.05; Cohen's d = 0.82). Nanotechnology significantly improved the sensing accuracy, reliability of data, ability to predict maintenance needs and responsiveness of cyber-physical systems. The proposed Nano-DTLM framework provides a smart, self-monitoring and predictive production management paradigm that helps to make Construction 4.0 and the next generation of smart construction sites more sustainable.

Read PDF

Similar papers

#computer vision Review Sep 2017

Agile Software Development Methods: Review and Analysis

This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.

P. Abrahamsson, O. Salo, Jussi Ronkainen et al. · 727 citations · ⚡54
#computer vision Jun 2008

The impact of agile practices on communication in software development

The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.

M. Pikkarainen, Jukka Haikara, O. Salo et al. · 401 citations · ⚡48
#machine learning Review Open access Oct 2014

Software development in startup companies: A systematic mapping study

The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.

Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al. · 394 citations · ⚡54

Related blog posts

Microsoft Research Blog Oct 6, 2026

What AI gets wrong and what failure teaches us

Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity.  The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.