Identifying steel elements for reuse using advanced analysis from AI and engineering

Backed by David Lang
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  • $15
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  • 1%
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  • 19
    days left

About This Project

Demolition in construction remains a significant challenge, particularly in managing material waste. I believe that combining in-situ structural testing with AI can accurately identify structural steel suitable for reuse before demolition occurs. This project aims to develop a prototype tool to test this hypothesis, integrating it into circular economy principles and structural design workflows. The goal is to support scalable circular construction practices and reduce embodied carbon.

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What is the context of this research?

Steel is one of the most used construction materials, yet tons are discarded during demolition even when still structurally reusable. Buildings account for 34% of global energy use and 37% of related greenhouse gas emissions (UNEP, 2022, https://www.unep.org/). A growing share is embodied carbon: from material extraction, manufacture, construction, maintenance, demolition, and waste processing (UK Green Building Council, 2021, https://www.ukgbc.org/; RICS, 2017, https://www.rics.org/). While recycling is common, reuse saves more energy, cuts emissions, and reduces waste. Currently, no simple method exists to identify reusable steel before demolition, and post-demolition testing is often costly, sometimes exceeding the price of new steel. This project hypothesizes that combining in-situ mechanical testing with AI analysis can accurately and efficiently identify reusable steel, improving decisions and reducing errors compared to traditional methods.

What is the significance of this project?

Steel is a cornerstone of the UK construction sector, contributing £2 billion annually and supporting over 33,000 jobs (UK Steel, 2023, makeuk.org). While 91% of demolition steel in the UK is recycled, most scrap is exported rather than reused locally, missing opportunities for job growth and carbon reduction. Globally, steel production accounts for about 9% of CO2 emissions, with UK steelmaking responsible for 12 million tonnes in 2019 alone (World Steel Association, 2020, worldsteel.org). Global steel demand is expected to reach 2.5 billion tonnes by 2050 (IEA, 2021, iea.org). The IEA estimates that improving material efficiency, including reuse, could contribute significantly to emissions reductions in the steel sector, potentially accounting for around 30% of combined emissions cuts across key materials by 2060 (IEA, 2021, iea.org). This project aims to support circular economy goals by enabling accurate identification of reusable steel.

What are the goals of the project?

The project aims to develop a prototype digital tool for identifying reusable structural steel prior to demolition. Mechanical data will be collected through in-situ tests measuring strength, hardness, corrosion and structural context information. Laboratory testing on steel samples will be conducted to validate these measurements. An AI model will be developed using machine learning algorithms, trained with positive and negative controls to accurately classify steel reuse potential. The model’s predictions will be validated against laboratory results and expert assessments from structural engineers and sustainability consultants. The prototype will be designed to integrate with digital workflows, allowing engineers and contractors to input data and receive guidance on reusable steel elements, thereby supporting circular construction practices and waste reduction.

Budget

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This budget supports the first year of research and development for a tool to identify structural steel suitable for reuse before demolition. It covers mechanical testing and lab access to generate essential data, alongside AI and software resources to develop the digital prototype. Expert consultants provide critical support in three key areas: AI, testing, and market strategy, ensuring the most effective methods as AI evolves rapidly. Additional consultants in structural engineering and circular economy principles guide technical accuracy and sustainability alignment. Travel funds enable on-site data collection to validate the tool in real conditions. Researcher support includes living expenses to enable full-time dedication during this self-funded phase. These funds are essential to ensure thorough development, accurate validation, and successful integration: critical for the project’s overall success.

Endorsed by

I fully support this brilliant project by my friend and former classmate Kawtar Louahbi. We studied MSc Civil and Structural Engineering together at City, University of London, where her passion for sustainable design was clear. Her work using AI to identify reusable steel before demolition is exactly the kind of innovation our industry needs. I’m confident in her ability to make a real impact in advancing circular construction. Projects like this are exactly what the future of engineering demands.
This project explores one of the most urgent challenges in sustainable structural design: how to safely reuse steel from existing structures using engineering insight and AI. The potential impact is significant, especially in retrofitting and the reuse of elements that don't meet 100% of original design specs but can still be safely employed. I fully support this work and the researcher behind it—she has both the technical skills and the vision to push this field forward.

Project Timeline

The project began in April 2025. By August, steel data collection from demolition sites will be complete. Mechanical testing and lab validation will follow, concluding by December. An AI model will then be trained and validated. By March 2026, the prototype will be finalized and tested. Feedback will guide integration. Key uncertainties will be addressed through iterative testing and shared updates.

Apr 01, 2025

Project started

Jun 09, 2025

Project Launched

Aug 31, 2025

Data Collection Completed

Dec 31, 2025

Complete mechanical testing and field data gathering on steel samples.

Mar 31, 2026

Build and validate the initial AI model to classify steel reuse potential.

Meet the Team

Kawtar Louahbi ,M.Sc and PhD Candidate in Structural Engineering
Kawtar Louahbi ,M.Sc and PhD Candidate in Structural Engineering
PhD Candidate in Structural Engineering

Affiliates

City St. George’s University of London​
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Kawtar Louahbi ,M.Sc and PhD Candidate in Structural Engineering

Kawtar Louahbi is a PhD researcher in structural engineering at City, University of London (City St. George’s partnership), specializing in sustainable construction and material reuse. Her doctoral research focuses on developing a practical, data-driven tool to identify reusable structural steel elements before demolition, a key challenge in advancing circular economy practices in construction. The project aims to reduce waste, carbon emissions, and inefficiencies by enabling informed decisions before materials are discarded.

Kawtar combines technical depth in structural mechanics with applied research in digital technologies, including AI for materials assessment. Her approach integrates laboratory testing, real-world site data, and algorithmic modelling to bridge research and industry needs. She has self-initiated and self-funded this project, demonstrating a high level of commitment and drive to deliver real-world impact.

She is supported academically and technically by Professor Konstantinos Daniel Tsavdaridis, Director of the 3D Modular Buildings for Circularity (3DMBC) Doctoral Centre, whose expertise in digital design and sustainable structures underpins the research methodology.

Her academic profile can be viewed at:

🔗 ResearchGate – Kawtar Louahbi 🔗 3D-MBC Doctoral Centre Profile 🔗 Professor Tsavdaridis – University Profile

In the future Kawtar will be supported by a network of expert advisors contributing AI, structural testing, and strategy guidance throughout the research. The project leverages academic and professional networks to ensure rigorous validation and relevance to industry.

Lab Notes

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