Search

UHPFRC Precast Segments for Sustainable and Resource‐Efficient Concrete Bridge Construction Many of the concrete bridges currently in use in Germany are in a deficient condition due to their age or design. However, increased traffic loads also highly contribute to the fact that many of the existing bridges will have to be replaced in the medium term. Due to the many advantages of concrete structures, it can be assumed that concrete bridges will continue to be designed and built in the future. In the light of the increasing consequences of climate change and the pressing need to reduce CO2 emissions also in the building industry as well, there is an urgent need for research into climate‐ and resource‐friendly as well as sustainable but also economical concrete construction methods. A promising approach to fast, effective and resource‐optimized as well as CO2‐efficient construction is the use of high‐performance materials such as UHPFRC in combination with precast segmental construction. In this paper, starting from a monolithic box girder bridge made of normal strength concrete, numerical calculations are used to investigate how much material can be saved in segmented box girder bridges by varying the concrete compressive strength (normal and high‐strength concrete as well as UHPFRC). The life cycle assessment subsequently carried out on this basis for life cycle phases A1 to A3 showed that, when the material is fully utilized, the use of UHPFRC leads to very resource‐efficient and sustainable structures compared with normal‐ and high‐strength concretes. [ABSTRACT FROM AUTHOR]

Image‐based quality conctrol – part 3: homogeneity of fresh concrete Homogeneity – i.e. the uniformity of the composition and properties of concrete in its fresh and hardened state – is a central requirement for concrete as a building material. Variations in homogeneity usually expressed in local changes, e.g. in the modulus of elasticity, strength or durability. Especially in the case of highly flowable (≥ F5) and self‐compacting concretes (SCC) but also, for example, in the case of air entrained concretes, homogeneity is a time‐dependent variable and changes due to segregation processes such as sedimentation and bleeding or as a result of unsuitable processing. An evaluation of the homogeneity of the fresh concrete is only possible visualy with the current testing methods on construction site. Building on the assessable properties already presented in the first two parts of this article, the range of properties of image‐based quality control is extended to include the homogeneity of the fresh concrete. For this purpose, visually detectable properties of the fresh concrete are used within the scope of the flow table test – paste separation in the edge area and geometric distribution of the aggregate on the surface. [ABSTRACT FROM AUTHOR]

Image‐based quality control – part 2: granulometric properties The changes in concrete technology necessary to achieve the climate protection targets will lead to a significant change in the concrete mixtures typically used. The raw materials used are becoming more diverse and complex. The use of recycled raw materials will also significantly increase the fluctuations in the properties of the raw materials. An Accurate and time‐efficient determination of the granulometric properties of the aggregate is of crucial importance, especially for "eco‐concretes", in order to ensure consistent quality characteristics. As a natural raw material, the aggregate is subject to certain fluctuations to which such optimized concrete compositions can react relatively sensitively. In the future, it will be necessary to determine possible quality deviations of the fresh concrete resulting from fluctuations in the raw materials in a time‐efficient manner in order to be able to make appropriate adjustments in the production process as quickly as possible. This paper presents an image‐based quality control for the digital evaluation of granulometric properties of the aggregate as a part oft he flow table test. Accordingly, the range of properties of the image‐based method already presented in Part 1 of this paper is extended to a fully comprehensive digital quality control. This paper presents the image‐based determination of the granulometric properties of the aggregate as a part oft he flow table test. The determination is based on the image‐based quality control described in part 1 of this paper. [ABSTRACT FROM AUTHOR]

Image‐based quality conctrol – part 1: consistency and paste content Today, only very simple empirical test methods are available for quality conctrol of fresh concrete on the construction site or in the production plant. Corrective actions in the case of quality deviations are based on a subjective visual inspection and thus on the experience of the inspector. The standard test method in Europe is the flow table test. The consistency of the concrete is derived from the diameter of the spread out fresh concrete. In order to overcome the deficiencies of this subjective fresh concrete testing, an image‐based method for determining (fresh) concrete properties was developed and validated. In addition to the diameter, extensive information can be derived from the surface properties of fresh conrete. In this paper, the image‐based determination of the consistency and the paste content is explained. The image‐based determination of granulometric properties (part 2) and the homogeneity of fresh concrete (part 3) will be presented in planned follow‐up articles. [ABSTRACT FROM AUTHOR]

Roadmap for climate‐optimized (ready‐mix) concrete – Quantified consideration of options for action to increase sustainability and resource efficiency The construction industry, and in particular the cement and concrete industry, has an important role to play both in limiting global warming and in protecting natural resources. The central question is how to reduce the CO2 emissions associated with the production, use and recycling of concrete while at the same time closing the resource cycle. A wide variety of paths to climate‐optimized and resource‐friendly concrete are conceivable and possible. However, the individual paths differ relatively strongly in the technical measures required, the normative prerequisites and, last but not least, the necessary investments. This article gives an overview on the current status of climate‐friendly and sustainable concrete construction and illustrates potentials for climate and resource optimization in the concrete industry. The focus of the article is on the ready‐mix concrete sector or in‐situ concrete construction and the current status of climate and resource protection in this sector is illustrated. The potential of various processes or measures for CO2 reduction and resource protection is quantified not only for a single type of concrete, but also on the basis of a typical German ready‐mix concrete plant with an assumed annual concrete production volume of approx. 21.300 m3, using literature data on the effectiveness of individual measures to enable plant operators to easily assess the effectiveness of individual or combined measures. [ABSTRACT FROM AUTHOR]

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 unlabelled 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 labelled data. Furthermore, we exceed the OA achieved by state-of-the-art semi-supervised methods by up to 1.5%. [ABSTRACT FROM AUTHOR]

Fair-faced concrete designs undergoing a revival since the beginning of the new century until now. In the building practice there are over and over again unwanted effects on a fair-faced concrete surface, despite the continuous further development. The "system fair-faced concrete" with varied influences from formwork, concrete, concrete processing and aftertreatment including the weather conditions is an extremely demanding one. The control of this system presents a particular art. This paper will show the possibilities of the concrete technology and concrete processing for a nice, individual and predictable fair-faced concrete surface. [ABSTRACT FROM AUTHOR]

The global production of concrete, being one of the most commonly utilised materials in the world, is associated with significant CO 2 -emissions and a substantial depletion of mineral resources. In order to improve sustainability, concretes thus are increasingly produced using recipes containing a large variety of different raw materials, including e.g. CO 2 reduced cements or recycled materials and industrial wastes. However, these actions result in heightened susceptibility of the concrete to variations in raw material characteristics, consequently diminishing the concrete's resilience and decreasing the concrete's robustness. Against this background, the quality control of fresh concrete before casting becomes of significant importance. However, current quality control is mainly conducted based on analogous, empirical, and often subjective test approaches. In this paper, a novel method is introduced for automatically and comprehensively evaluating the quality of fresh concrete at construction sites. In particular, the paper presents an end-to-end trainable framework for the image-based characterisation of fresh concrete properties. More specifically, a camera-based setup is proposed, in which image sequences of the discharge process of a mixing truck are acquired, on the basis of which the fresh concrete properties such as the consistency and rheology are determined. In this context, this paper introduces the concept of Spatio-Temporal Flow Fields , which provide a compact representation of the concrete's flow behaviour and serve as input to a multi-task convolutional neural network (CNN) predicting the target parameters describing the fresh concrete's properties. A thorough examination of the performance of the suggested method is carried out using highly challenging real-world data, showcasing extremely compelling outcomes with average prediction errors for the concrete properties of only about 6%. [Display omitted] • Novel strategy for camera-based characterisation of fresh concrete from flow observations. • Proposal of Spatio-Temporal Flow Fields as compact representation of the open-channel concrete flow. • A multi-task CNN predicting both, the fresh concrete's consistency and rheology • Highly promising results on a challenging real-world data set. [ABSTRACT FROM AUTHOR]