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

• URL: http://arxiv.org/abs/2204.03333v1
• Date: Thu, 7 Apr 2022 10:04:05 GMT
• Title: Learning to Sieve: Prediction of Grading Curves from Images of Concrete Aggregate
• Authors: Max Coenen and Dries Beyer and Christian Heipke and Michael Haist
• Abstract summary: 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.
• Score: 1.6249267147413522
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Characterizing segregation in blast rock piles a deep-learning approach leveraging aerial image analysis [7.211586388797869]
  This study introduces an advanced image analysis methodology to characterize such segregation of rock fragments. The accurate delineation of detailed rock fragment size distributions was achieved through the analysis of drone-captured imagery.
  arXiv  Detail & Related papers  (2024-06-06T15:13:56Z)
• Size-invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection [133.66006666465447]
  Current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. We develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes.
  arXiv  Detail & Related papers  (2024-05-16T03:01:06Z)
• IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion [97.58125811599383]
  Temporal Knowledge Graphs (TKGs) incorporate a temporal dimension, allowing for a precise capture of the evolution of knowledge. We propose a novel Multi-curvature shared and specific Embedding (IME) model for TKGC tasks. IME incorporates two key properties, namely space-shared property and space-specific property.
  arXiv  Detail & Related papers  (2024-03-28T23:31:25Z)
• Image-based Deep Learning for the time-dependent prediction of fresh concrete properties [0.5018974919510383]
  This paper makes it possible to predict the properties of fresh concrete during the mixing process based on stereoscopic image sequences. A Convolutional Neural Network (CNN) is used for the prediction, which receives the images supported by information on the mix design as input. It is shown that an approach based on depth and optical flow images, supported by information of the mix design, achieves the best results.
  arXiv  Detail & Related papers  (2024-02-09T18:42:30Z)
• Sustainable Concrete via Bayesian Optimization [4.149122595744125]
  Eight percent of global carbon dioxide emissions can be attributed to the production of cement. The discovery of lower-carbon concrete formulae is therefore of high significance for sustainability.
  arXiv  Detail & Related papers  (2023-10-27T17:25:12Z)
• Probabilistic selection and design of concrete using machine learning [0.0]
  Making reliable property predictions with machine learning can facilitate performance-based specification of concrete. We apply the methodology to specify a concrete mix that has high resistance to carbonation, and another concrete mix that has low environmental impact. Our generic methodology enables the exploitation of noise in machine learning, which has a broad range of applications in structural engineering.
  arXiv  Detail & Related papers  (2023-04-21T19:20:40Z)
• Concrete Score Matching: Generalized Score Matching for Discrete Data [109.12439278055213]
  "Concrete score" is a generalization of the (Stein) score for discrete settings. "Concrete Score Matching" is a framework to learn such scores from samples.
  arXiv  Detail & Related papers  (2022-11-02T00:41:37Z)
• Accelerated Design and Deployment of Low-Carbon Concrete for Data Centers [13.376376442187922]
  We use conditional variational autoencoders (CVAEs) to discover concrete formulas with desired properties. Our model is trained just using a small open dataset from the UCI Machine Learning Repository. We report on how these formulations were used in the construction of buildings and structures in a Meta data center in DeKalb, IL, USA.
  arXiv  Detail & Related papers  (2022-04-11T20:40:13Z)
• Learning Conditional Invariance through Cycle Consistency [60.85059977904014]
  We propose a novel approach to identify meaningful and independent factors of variation in a dataset. Our method involves two separate latent subspaces for the target property and the remaining input information. We demonstrate on synthetic and molecular data that our approach identifies more meaningful factors which lead to sparser and more interpretable models.
  arXiv  Detail & Related papers  (2021-11-25T17:33:12Z)
• Featurized Density Ratio Estimation [82.40706152910292]
  In our work, we propose to leverage an invertible generative model to map the two distributions into a common feature space prior to estimation. This featurization brings the densities closer together in latent space, sidestepping pathological scenarios where the learned density ratios in input space can be arbitrarily inaccurate. At the same time, the invertibility of our feature map guarantees that the ratios computed in feature space are equivalent to those in input space.
  arXiv  Detail & Related papers  (2021-07-05T18:30:26Z)
• Interpolation and Learning with Scale Dependent Kernels [91.41836461193488]
  We study the learning properties of nonparametric ridge-less least squares. We consider the common case of estimators defined by scale dependent kernels.
  arXiv  Detail & Related papers  (2020-06-17T16:43:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.