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Forensic Analysis of Concrete Through Image Processing

Project description

Revolutionising concrete forensics with AI

Concrete deterioration poses a significant challenge to infrastructure safety, with issues like cracks, spalling, and corrosion compromising structural integrity. Traditional forensic analysis (FA) methods, such as manual inspection and core cutting, are time-consuming and often lack precision, making it difficult to assess concrete conditions accurately. Inconsistent results and human error further hinder the effectiveness of these evaluations, leading to potential safety risks. With the support of the Marie Skłodowska-Curie Actions programme, the FACIP project introduces an innovative approach to forensic concrete analysis. By integrating image processing (IP) and convolutional neural networks (CNNs), FACIP automates defect detection and characterisation, enhancing accuracy and efficiency. FACIP will ensure more reliable and timely forensic investigations.

Objective

The proposal is about the Forensic Analysis of Concrete through Image Processing (FACIP), which offers several improvements over traditional methods of forensic analysis (FA) of concrete. Unlike manual inspection, the proposed method automates and streamlines the process, reducing human effort and evaluation time. FA of concrete is commonly used to determine particle size, shape, aggregate ratio, porosity, and other physical characteristics that affect concrete performance. This project aims to develop an innovative approach that utilizes image processing (IP) and Convolutional Neural Networks (CNN) to enhance FA of concrete structures. By accurately and efficiently detecting, characterizing, and quantifying various concrete defects such as cracks, spalling, corrosion, and delamination, the project aims to extract intricate patterns and features from concrete images through deep learning, improving FA accuracy. This ambitious objective pushes the boundaries of current research in FA of concrete, opening the door to discoveries and advancements. By combining IP and CNN for FACIP, the research aims to develop innovative methodologies and tools that surpass the limitations of existing techniques, such as Core Cutting, Schmidt Hammer, and UPVs, providing accurate and reliable analysis results. This contributes to developing more effective concrete maintenance, repair, and structural integrity assessment strategies. Ultimately, the project aims to provide concrete forensic experts with advanced, reliable, and time-efficient analysis methods, enhancing concrete forensic investigations' overall accuracy and effectiveness.

Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) HORIZON-MSCA-2023-PF-01

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Coordinator

OSLOMET - STORBYUNIVERSITETET
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 226 751,04
Address
PILESTREDET 46
0167 Oslo
Norway

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Region
Norge Oslo og Viken Oslo
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

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