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Carbide-free bainitic steels with the bainitic ferrite (BF) as matrix constituent have been widely used and manufactured. The mixed microstructure of pre-formed martensite (M), BF, and retained austenite (RA) was obtained by quenching for a short time below the martensitic transformation temperature followed by austempering. A similar amount of fresh martensite was obtained by interrupted austempering at the same temperature. Results show that the carbon distribution among these constituents in the mixed microstructure plays a major role in controlling mechanical properties. The carbon content of M and BF in the M-BF-RA microstructure is lower than that of BF-M-RA microstructure, while that of RA was reversed. Mechanical properties of the M-BF-RA microstructure exhibit higher toughness, higher product of strength and elongation as well as higher yield ratio, rather than strength, compared to that of the BF-M-RA microstructure. It can be attributed to toughened pre-formed martensite with low carbon content and mechanical stable RA with high carbon content. In addition, the pre-formed martensite decreases the block size, leading to the increase of high angle grain boundaries, which further improves toughness. The high carbon fresh martensite BF increased the strength of the BF-M-RA microstructure, but sharply decreases ductility and toughness.

Susceptibility analysis is important in any study of debris flows. This paper presents a model for debris flow susceptibility analysis using a combination weighting and an improved fuzzy C-means algorithm. Twelve factors of influence were acquired by 3S technologies. Analytic hierarchy process (AHP) and entropy method were performed to obtain subjective and objective weighting of the factors, respectively. Game theory was carried out to determine the combination weighting since it provides analytical tools to model interactions among factors. An improved fuzzy C-means clustering analysis was applied to determine the susceptibility level of debris flows. This method is based on a particle swarm optimization algorithm, which is an evolutionary algorithm that can achieve global optimization, and is not sensitive to the initial cluster centers. Results showed that the susceptibility levels for one of the debris flow catchments was high, four were moderate, and five were low. Our quantitative assessments based on these nonlinear methods were consistent with field investigations.