Work

Hyperspectral Signatures for Detecting the Concrete Hydration Process Using Neural Networks

Year: 2025

Type: article

Abstract: The curing process of a concrete sample has a significant influence on hydration and its strength. This means that inadequate curing conditions lead to a loss of concrete quality and negative consequences in structural engineering. In addition, different state-of-the-art (SOTA) curing surface treatments and hydration periods have a significant effect on durability. This paper introduces an innovative non-destructive method to detect the development of the hydration process under different treatment conditions. Hyperspectral imaging is a non-contact measurement technique that provides detailed information on hydration characteristics within an electromagnetic wavelength range. A comparative laboratory measurement was conducted on twelve concrete samples, subjected to three curing treatments and four curing surface treatments, over a hydration period from the 1st to the 56th day. Additionally, artificial neural networks and convolutional neural networks have achieved classification accuracies of 67.8% (hydration time), 83.3% (curing regime), and 87.6% (surface type), demonstrating the feasibility of using neural networks for hydration monitoring. In this study, the results revealed differences in near-infrared spectral signatures, representing the type of curing treatment, curing surface, and hydration time of the concrete. The dataset was classified and analyzed using neural networks. For each hydration treatment, three different models were developed to achieve better prediction performance for hyperspectral imaging analysis. This method demonstrated a high level of reliability in investigating curing surface treatments, curing treatments, and hydration time. A recommended method for using hyperspectral imaging to evaluate the cured quality of concrete will be developed in future research.

Source: Infrastructures

DOI: 10.3390/infrastructures10070172

Authors: Shiming Li, Alfred Strauss, Damjan Grba, Maximilian Granzner, Benjamin Täubling-Fruleux, Thomas Zimmermann

Institutions: Technical University of Munich, BOKU University, Magistrat der Stadt Wien

Language: English

Topic: Spectroscopy and Chemometric Analyses

Subfield: Analytical Chemistry

Field: Chemistry

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,172

Funders: Bundesministerium für Klimaschutz, Umwelt, Energie, Mobilität, Innovation und Technologie, Österreichische Forschungsförderungsgesellschaft