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Micromechanics-based analysis of PVA–ECC after thermal exposure

Zhang Zhigang, Liu Jin-Cheng, Li Junxia, Qin Fengjiang, Di Jin

Archives of Civil and Mechanical Engineering

2023

Vol. 23, no. 3

art. no. e213, 2023

In this paper, the thermal effects on mechanical properties of polyvinyl alcohol fiber-reinforced engineered cementitious composites (PVA–ECC) were investigated systematically from perspective of multi-scales. At composite level, the compressive strength increases from 38 to 50 MPa as the samples were heated from 30 to 200 °C, whereas it declines to 20 MPa at 800 °C. In respect of tensile performance, at range of 30– 200 °C, the ultimate tensile stress and strain of ECC showed a decrease tendency with rising temperature, but still remained strain-hardening behavior at 200 °C. In addition, the elevated temperature exposures are adverse to multiple-cracking behavior of ECC. At micro-scale, it was found that the fiber/matrix interfacial bond reduces as exposure temperature rises, which is supposed to avail the fiber slippage, and thereby ductility of ECC. Nonetheless, through micromechanics-based analysis, the enhanced matrix toughness and severe deteriorated fiber strength prevailed over the above positive effect, which resulted in the decayed tensile properties of ECC.

A machine learning approach to predict explosive spalling of heated concrete

Liu Jin-Cheng, Zhang Zhigang

Archives of Civil and Mechanical Engineering

2020

Vol. 20, no. 4

535--559

Explosive spalling is an unfavorable phenomenon observed in concrete when exposed to heating load. It is a great potential threat to safety of concrete structures subjected to accidental thermal loads. Therefore, assessing explosive spalling risk of concrete is important for fire safety design of concrete structures. This paper proposed a popular machine learning approach, i.e., artificial neural network (ANN), to assess explosive spalling risk of concrete. Besides, the decision tree method was also used to execute the same mission for a comparison purpose. Twenty-eight groups of heating tests were conducted to validate the proposed ANN model. The ANN model behaved well in assessing explosive spalling of concrete, with a prediction accuracy of 82.1%. This study shows that ANN is a promising method for adequate classification of concrete as material resistant or not resistant to thermal explosive spalling.