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A type of ultralight (<300 kg/m3) foamed concrete (FC), which can be used as a new energy-conservation and environmental-protection building material and is particularly suitable for the thermal-insulation engineering of building external walls, was produced. The influences of different mixing amounts of fly ash, fly ash activator, WC (WC) ratio, and foaming agent (FA) on the compressive strength of FC were reported. The experimental study indicated that (1) the addition of fly ash reduced the strength of the FC and that the appropriate mixing amount of fly ash in this ultralight FC system should not exceed 45%; (2) with the increasing of fly ash activator, the strength of the FC sample is notably enhanced and the appropriate mixing amount of fly ash activator is 2.5%; (3) the optimized proportion of WC ratio is 0.45, and the FC that was produced according to this proportion has relatively high compressive strength; (4) by increasing the mixing amount of FA, the compressive strength of the FC notably decreases, and the optimal mixing amount of FA in this experiment is 3.5%.

The objective of present study is to classify and identify damage mechanisms in polyethylene(PE) self‐reinforced composites by acoustic emission (AE) technique. Model specimens including LDPE resin, [90°]laminate, single fiber composite, fiber bundle composite, and [±45°] laminates are fabricated to obtain expected damage mechanisms during tensile testing. First, mechanical behaviors and corresponding AE response of model specimens are studied to validate damage mechanisms in UHMWPE/LDPE laminates. Second, relationship among AE descriptors is investigated by hierarchical cluster analysis, and AE signals are classified by k‐means cluster analysis. Correlations between damage mechanisms and AE are established in terms of amplitude, duration, and peak frequency of AE signals. Finally, an artificial neural network is created and trained by various optimal algorithms to identify damage mechanisms. The results reveal that typical damage mechanisms in PE self‐reinforced composite can be classified in terms of the similarity between AE signals and identified by trained artificial neural network. POLYM. COMPOS., 2011. © 2011 Society of Plastics Engineers