arXiv subjects

Michael Haist

Publications and source records attributed to Michael Haist.

7 recordsLinked to original sources

arXiv2026

Visualizing shear-induced structures in carbon black gels by tomo-rheoscopy

Suspensions of attractive particles form space-spanning networks that endow the suspension with solid-like behavior at rest. The microstructure of these colloidal gels depends sensitively on the shear history and on the path followed across the sol-gel transition, resulting in viscoelastic properties that can be tuned by shear. Here, we report in situ X-ray tomo-rheoscopy experiments on carbon black gels whose elastic properties exhibit a non-monotonic dependence on the shear intensity applied prior to flow cessation. By directly imaging the gel microstructure under a well-controlled rheological protocol, we reveal the emergence of pronounced structural heterogeneities extending from tens to hundreds of microns -- length scales far larger than those accessible by conventional scattering techniques such as Ultra-Small Angle X-ray Scattering. In particular, we show that only the low-shear reinforcement of elasticity correlates with a growing mesoscale correlation length, while high-shear strengthening occurs without detectable mesoscale reorganization. These observations demonstrate that flow memory in colloidal gels is not solely governed by local particle rearrangements, but is also encoded in a mesoscale structural organization extending up to 100 times the particle size. More broadly, this work highlights the power of X-ray tomo-rheoscopy to uncover large-scale structural signatures of flow history in soft materials, opening new perspectives to tailor their mechanical properties.

cond-mat.soft↗

arXiv2025

Impact of early-age exposure to power ultrasound on the micromechanical properties of hardened cement paste

Cement paste serves as the universal binder in concrete, formed by mixing water with Ordinary Portland Cement (OPC). In its fresh state, cement paste is a suspension in which the initial particle inventory continuously dissolves, while simultaneously, a growing population of colloidal calcium silica-hydrate (C-S-H) particles forms and progressively builds a percolated structure that densifies and hardens through hydration. Like most colloidal gels, cement paste is sensitive to external stimuli such as mechanical vibrations and temperature variations. Here, we show that power ultrasound (PUS) applied to OPC paste during the very early stages of hydration significantly alters the mechanical properties of the hardened material. Freshly prepared cement pastes are exposed to PUS at varying durations and intensities before curing for 28 days. Micro-indentation testing at the scale of tens of microns reveals that increasing PUS amplitude and prolonged PUS exposure degrade the micro-mechanical properties of hardened cement paste, making it more ductile. This softening behavior evolves continuously with exposure conditions and is consistent with an increase in porosity, likely caused by micro-cracks induced by PUS. This scenario is further supported by micro-scratch testing, which shows higher levels of acoustic emission in PUS-exposed samples. Finally, nano-indentation confirms that the properties of the individual phases composing the hardened paste -- low-density and high-density C-S-H -- remain largely unaffected. These findings offer valuable experimental insights into novel pathways for modifying the mechanical properties of reactive colloidal gels.

cond-mat.soft↗

arXiv2024

Image-based Deep Learning for the time-dependent prediction of fresh concrete properties

Increasing the degree of digitisation and automation in the concrete production process can play a crucial role in reducing the CO$_2$ emissions that are associated with the production of concrete. In this paper, a method is presented that makes it possible to predict the properties of fresh concrete during the mixing process based on stereoscopic image sequences of the concretes flow behaviour. A Convolutional Neural Network (CNN) is used for the prediction, which receives the images supported by information on the mix design as input. In addition, the network receives temporal information in the form of the time difference between the time at which the images are taken and the time at which the reference values of the concretes are carried out. With this temporal information, the network implicitly learns the time-dependent behaviour of the concretes properties. The network predicts the slump flow diameter, the yield stress and the plastic viscosity. The time-dependent prediction potentially opens up the pathway to determine the temporal development of the fresh concrete properties already during mixing. This provides a huge advantage for the concrete industry. As a result, countermeasures can be taken in a timely manner. It is shown that an approach based on depth and optical flow images, supported by information of the mix design, achieves the best results.

cs.CV↗

arXiv2022

ConsInstancy: Learning Instance Representations for Semi-Supervised Panoptic Segmentation of Concrete Aggregate Particles

We present a semi-supervised method for panoptic segmentation based on ConsInstancy regularisation, a novel strategy for semi-supervised learning. It leverages completely unlabelled data by enforcing consistency between predicted instance representations and semantic segmentations during training in order to improve the segmentation performance. To this end, we also propose new types of instance representations that can be predicted by one simple forward path through a fully convolutional network (FCN), delivering a convenient and simple-to-train framework for panoptic segmentation. More specifically, we propose the prediction of a three-dimensional instance orientation map as intermediate representation and two complementary distance transform maps as final representation, providing unique instance representations for a panoptic segmentation. We test our method on two challenging data sets of both, hardened and fresh concrete, the latter being proposed by the authors in this paper demonstrating the effectiveness of our approach, outperforming the results achieved by state-of-the-art methods for semi-supervised segmentation. In particular, we are able to show that by leveraging completely unlabeled data in our semi-supervised approach the achieved overall accuracy (OA) is increased by up to 5% compared to an entirely supervised training using only labeled data. Furthermore, we exceed the OA achieved by state-of-the-art semi-supervised methods by up to 1.5%.

cs.CV↗

arXiv2022

Learning to Sieve: Prediction of Grading Curves from Images of Concrete Aggregate

A large component of the building material concrete consists of aggregate with varying particle sizes between 0.125 and 32 mm. Its actual size distribution significantly affects the quality characteristics of the final concrete in both, the fresh and hardened states. The usually unknown variations in the size distribution of the aggregate particles, which can be large especially when using recycled aggregate materials, are typically compensated by an increased usage of cement which, however, has severe negative impacts on economical and ecological aspects of the concrete production. In order to allow a precise control of the target properties of the concrete, unknown variations in the size distribution have to be quantified to enable a proper adaptation of the concrete's mixture design in real time. To this end, this paper proposes a deep learning based method for the determination of concrete aggregate grading curves. In this context, we propose a network architecture applying multi-scale feature extraction modules in order to handle the strongly diverse object sizes of the particles. Furthermore, we propose and publish a novel dataset of concrete aggregate used for the quantitative evaluation of our method.

cs.CV↗

arXiv2021

Creep in reactive colloidal gels: a nanomechanical study of cement hydrates

From soft polymeric gels to hardened cement paste, amorphous solids under constant load exhibit a pronounced time-dependent deformation called creep. The microscopic mechanism of such a phenomenon is poorly understood in amorphous materials and constitutes an even greater challenge in densely packed and chemically reactive granular systems. Both features are prominently present in hydrating cement pastes composed of calcium silicate hydrate (C-S-H) nanoparticles, whose packing density increases as a function of time, while cement hydration is taking place. Performing nano-indentation tests and porosity measurements on a large collection of samples at various stages of hydration, we show that the creep response of hydrating cement paste is mainly controlled by the inter-particle distance and results from slippage between (C-S-H) nanoparticles. Our findings provide a unique insight into the microscopic mechanism underpinning the creep response in aging granular materials, thus paving the way for the design of concrete with improved creep resistance.

cond-mat.mtrl-sci↗

arXiv2021

Semi-Supervised Segmentation of Concrete Aggregate Using Consensus Regularisation and Prior Guidance

In order to leverage and profit from unlabelled data, semi-supervised frameworks for semantic segmentation based on consistency training have been proven to be powerful tools to significantly improve the performance of purely supervised segmentation learning. However, the consensus principle behind consistency training has at least one drawback, which we identify in this paper: imbalanced label distributions within the data. To overcome the limitations of standard consistency training, we propose a novel semi-supervised framework for semantic segmentation, introducing additional losses based on prior knowledge. Specifically, we propose a light-weight architecture consisting of a shared encoder and a main decoder, which is trained in a supervised manner. An auxiliary decoder is added as additional branch in order to make use of unlabelled data based on consensus training, and we add additional constraints derived from prior information on the class distribution and on auto-encoder regularisation. Experiments performed on our "concrete aggregate dataset" presented in this paper demonstrate the effectiveness of the proposed approach, outperforming the segmentation results achieved by purely supervised segmentation and standard consistency training.

cs.CV↗