A predictive model of concrete compressive strength based on level of contamination of coarse aggregate

A predictive model of concrete compressive strength based on level of contamination of coarse aggregate: Experimental study and relation to the water absorption of the coarse aggregate

Nedelchev, Hari (TU Delft Civil Engineering & Geosciences)

Rem, P.C. (mentor)
Di Maio, F. (graduation committee)
Vahidi, A. (graduation committee)
Kasbergen, C. (graduation committee)

Delft University of Technology

Civil Engineering

2023-02-14

Recycled aggregate concrete is emphasized more and more nowadays due to its importance towards the construction industry and general welfare of our planet. Being such a significant feature with regard to universal sustainable development, every aspect of its properties should be evaluated, examined and optimized so that there is a well-known, well-described and ready to use product. This project aimed at correlating some of the established parameters of conventional concrete such as water absorption of the coarse aggregate fraction and the resulting compressing strength, only applied in the field of recycled aggregate concrete. Along with these two properties, a prediction model was sought for which would be able to forecast the mechanical strength of concrete based on its coarse aggregate composition. A series of experiments were performed on samples fabricated for water absorption tests and compressive strength assessment. In total, 112 water absorption samples and over 250 concrete specimens were prepared and examined for the respective attribute. These samples used natural aggregate as the base of the coarse aggregate portion of the mix design, alongside a series of so-called contaminants which replaced the gravel in certain concentrations. These contaminants included bricks, ceramic tiles, glass, wood, gypsum, plastics, mineral fibers and recycled sand concrete and were entirely based upon the C&DW composition globally. Further investigation was done on the inclusion of air within concrete so that contaminated concrete results could be equated to this constant. All water absorption tests were performed according to the current standards and concrete specimens were crushed after 7 and 28 days of curing in order to obtain their strength in compression, while the predictive model was initiated via the Minitab statistical software.
The results indicated some interesting and promising trends. From both sets of experiments, it was evident that the water absorption of the coarse aggregate fraction was not the main contributor towards the strength development of recycled concrete. It had a minor influence, especially compared to the type of contaminant present and how much volume it took. Furthermore, samples with identical water absorption fabricated concretes with different strengths. Overall, plastics and wood had the most negative effect in terms of compressive strength, while bricks, tiles and glass seemed to affect this aspect in neutral or even slightly positive manner. EPS foam in very limited amounts yielded a notable 30 to 35% strength drop, while on the other hand, brick replacing 20% of the NA improved the strength by approximately 7% after 7 days and 2% after 28 days. In the end, based on all experimental input, a predictive model was developed, optimized and validated in several steps so that it was able to predict the water absorption of coarse aggregate, compressive strength after 7 and 28 days and equivalent air content based on the composition of the coarse aggregates. The back-end of the model is provided within the report as a MATLAB code.

water absorption
compressive strength
concrete
recycled concrete
coarse aggregate
predictive model
WA
CRCA
RCA
recycled coarse aggregate
concrete made from recyclled aggregate
Recycled aggregates
experimental study
contaminants
contaminated concrete
brick
ceramic tiles
wood
glass
plastics
plastics in concrete
fibers
gypsum
cement

http://resolver.tudelft.nl/uuid:a27bf73e-0df3-44d0-a509-6ba23d4dbcb7

Student theses

master thesis

© 2023 Hari Nedelchev