@article{LeNguyenLudwig,
  author    = {Le, Ha Thanh and Nguyen, Sang Thanh and Ludwig, Horst-Michael},
  title     = {A Study on High Performance Fine-Grained Concrete Containing Rice Husk Ash},
  series = {International Journal of Concrete Structures and Materials},
  journal   = {International Journal of Concrete Structures and Materials},
  doi       = {10.1007/s40069-014-0078-z},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170425-31477},
  pages     = {301 -- 307},
  abstract  = {Rice husk ash (RHA) is classified as a highly reactive pozzolan. It has a very high silica content similar to that of silica fume (SF). Using less-expensive and locally available RHA as a mineral admixture in concrete brings ample benefits to the costs, the technical properties of concrete as well as to the environment. An experimental study of the effect of RHA blending on workability, strength and durability of high performance fine-grained concrete (HPFGC) is presented. The results show that the addition of RHA to HPFGC improved significantly compressive strength, splitting tensile strength and chloride penetration resistance. Interestingly, the ratio of compressive strength to splitting tensile strength of HPFGC was lower than that of ordinary concrete, especially for the concrete made with 20 \% RHA. Compressive strength and splitting tensile strength of HPFGC containing RHA was similar and slightly higher, respectively, than for HPFGC containing SF. Chloride penetration resistance of HPFGC containing 10-15 \% RHA was comparable with that of HPFGC containing 10 \% SF.},
  subject      = {Hochfester Beton},
  language  = {en}
}
@article{PatzeltErfurtLudwig,
  author    = {Patzelt, Max and Erfurt, Doreen and Ludwig, Horst-Michael},
  title     = {Quantification of cracks in concrete thin sections considering current methods of image analysis},
  series = {Journal of Microscopy},
  volume    = {2022},
  journal   = {Journal of Microscopy},
  number    = {Volume 286, Issue 2},
  doi       = {10.1111/jmi.13091},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20220811-46754},
  pages     = {154 -- 159},
  abstract  = {Image analysis is used in this work to quantify cracks in concrete thin sections via modern image processing. Thin sections were impregnated with a yellow epoxy resin, to increase the contrast between voids and other phases of the concrete. By the means of different steps of pre-processing, machine learning and python scripts, cracks can be quantified in an area of up to 40 cm2. As a result, the crack area, lengths and widths were estimated automatically within a single workflow. Crack patterns caused by freeze-thaw damages were investigated. To compare the inner degradation of the investigated thin sections, the crack density was used. Cracks in the thin sections were measured manually in two different ways for validation of the automatic determined results. On the one hand, the presented work shows that the width of cracks can be determined pixelwise, thus providing the plot of a width distribution. On the other hand, the automatically measured crack length differs in comparison to the manually measured ones.},
  subject      = {Beton},
  language  = {en}
}