Beneficial utilization of ultra-fine dredged sand from Yangtze River channel as a concrete material based on the minimum paste theory
Source abstract
Abstract
Ultra-fine sands are dredged periodically in order to maintain the normal activities of the Yangtze River Channel. Finding a new solution for managing these ultra-fine dredged sand becomes a challenge for channel managers. In this study, ultra-fine dredged sand and manufactured sand were used as the concrete fine aggregates. The concrete mix ratio was obtained based on the minimum paste theory by setting different target strengths, Average Paste Thickness (APT) between aggregates and dredged sand contents. The feasibility of applying the minimum paste theory in the design of ultra-fine dredged sand concrete mix ratio was verified by compressive strength tests. The influences of APT and dredged sand content on the mechanical properties and workability of concrete were analyzed. The results showed that with the increase of the APT, the compressive strength initially was increased and then decreased and the workability was improved. The workability of the concrete was improved with more content of ultra-fine dredged sand through the analysis of the microstructure of the fine aggregates. The concrete with a mass ratio 25% ultra-fine dredged sand had a lower harmful porosity which was verified by mercury intrusion porosimetry (MIP) and SEM, and has a compressive strength value of 43.8 MPa at 28 days which was 11.4% and 10.61% higher than that of the control group with the content ratio of 0% and 50%, respectively. The test results proved that the use of the minimum paste theory in the design of the ultra-fine dredged sand concrete mix ratio is expected to obtain concrete with fewer paste content and proper working performance.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Innovative concrete reinforcement materials · Concrete and Cement Materials Research · Innovations in Concrete and Construction Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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