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Effects of autogenous shrinkage microcracks on UHPC:Insights from a machine learning based crack quantification approach
Owing to its high pozzolanic reactivity and contribution to packing density, silica fume is almost an indispensable part of UHPC, but it brings potentially serious problem of high autogenous shrinkage. Nanocellulose (NC) is very effective in controlling shrinkage but would also result in different microstructure, whose impact is not clear. This study aims to explore the compensatory effect of NC on high shrinkage of UHPC. An image process method based on machine learning and stereological methods is proposed to quantify the autogenous shrinkage induced microcracks. Results show that the addition of NC reduces the crack width and area by 57.45∼70.55% and 63.2–83.8%, respectively. The MIP analysis reveals that the incorporation of NC introduces a larger proportion of pores. In terms of mechanical properties, the higher content of pores brought by NC has a negative effect on compressive strength, however, the enhancement of flexural strength by NC can reach 66.02%. Excellent correlations between 0 and 50 nm porosity and compressive strength, crack density and flexural strength are observed with R 2 of 0.94 and 0.98 respectively. This study provides a theoretical basis for the potential control of porosity and cracks of UHPC to meet the different mechanical performance requirements of components.
Effects of autogenous shrinkage microcracks on UHPC:Insights from a machine learning based crack quantification approach
Owing to its high pozzolanic reactivity and contribution to packing density, silica fume is almost an indispensable part of UHPC, but it brings potentially serious problem of high autogenous shrinkage. Nanocellulose (NC) is very effective in controlling shrinkage but would also result in different microstructure, whose impact is not clear. This study aims to explore the compensatory effect of NC on high shrinkage of UHPC. An image process method based on machine learning and stereological methods is proposed to quantify the autogenous shrinkage induced microcracks. Results show that the addition of NC reduces the crack width and area by 57.45∼70.55% and 63.2–83.8%, respectively. The MIP analysis reveals that the incorporation of NC introduces a larger proportion of pores. In terms of mechanical properties, the higher content of pores brought by NC has a negative effect on compressive strength, however, the enhancement of flexural strength by NC can reach 66.02%. Excellent correlations between 0 and 50 nm porosity and compressive strength, crack density and flexural strength are observed with R 2 of 0.94 and 0.98 respectively. This study provides a theoretical basis for the potential control of porosity and cracks of UHPC to meet the different mechanical performance requirements of components.
Effects of autogenous shrinkage microcracks on UHPC:Insights from a machine learning based crack quantification approach
Zeng, Xiaolan (author) / Deng, Qian (author) / Li, Shaohua (author) / Gao, Hongbo (author) / Yu, Qingliang (author)
2024-05-17
Zeng, X, Deng, Q, Li, S, Gao, H & Yu, Q 2024, 'Effects of autogenous shrinkage microcracks on UHPC : Insights from a machine learning based crack quantification approach', Construction and Building Materials, vol. 428, 136400. https://doi.org/10.1016/j.conbuildmat.2024.136400
Article (Journal)
Electronic Resource
English