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Duplex stainless steel (DSS) exhibits good mechanical properties and corrosion resistance, and has attracted more and more attention within the fields of both science and technology. However, the increasing levels of N and of Cr, Mo, etc., as alloying elements in DSS increase production difficulty. In particular, the N element increases the risk of Cr2N precipitation, which can seriously deteriorate the thermal plasticity of DSS, while increasing its strength. For this reason, a low-N-content 25Cr-type DSS was designed in order to adapt additive manufacturing processes. With regard to the nano-inclusions of oxide precipitation and effective grain refinement, and considering the benefits of selective laser melting fabrication, a low-N 25Cr-type duplex stainless steel with a 0.09 wt.% N content achieved high mechanical properties, with a yield strength of 712 MPa and an elongation of 27.5%, while the V-notch impact toughness was 160 J/cm2. The microstructure evolution and the reasons behind the improvement in mechanical properties will be discussed in detail. [ABSTRACT FROM AUTHOR]

The formation mode of isolated austenite (IA) formed during high-temperature cooling and its effect on pitting corrosion resistance of S32750 DSS were investigated. As the structure formed during the high-temperature cooling process after hot rolling in S32750 duplex stainless steel (DSS), the precipitation of isolated austenite (IA) can be considered as the combined production of temperature and cooling rates. IA generated at the ferrite (α) grain boundaries or internal α and performed the K-S crystallographic relation with the α N element enriched in IA. The redistribution of elements not only increased the concentration gradient between α/IA, but also reduced the pitting resistance equivalent number (PREN) of original austenite (γ). Scanning Kelvin probe (SKP) and Gaussian fitting proved that with prolonged corrosion time, the potential difference enlarged and local corrosion aggravated. Measured by the weight loss method, pitting resistance decreased to one-third of the solid solution state due to the existence of IA; following, the quantities, sizes, and depth of corrosion pits increased significantly. In addition to α/γ boundaries and α/α boundaries, corrosion pits preferred to initiate around IA. The IA that appeared at α grain boundaries deteriorated the pitting corrosion resistance severely. Furthermore, original austenite with decreased PREN tended to be corroded. In general, the order of pitting occurrence is displayed as follows: α-γ-IA. [ABSTRACT FROM AUTHOR]

Accurate forecasts of construction waste are important for recycling the waste and formulating relevant governmental policies. Deficiencies in reliable forecasting methods and historical data hinder the prediction of this waste in long- or short-term planning. To effectively forecast construction waste, a time-series forecasting method is proposed in this study, based on a three-layer long short-term memory (LSTM) network and univariate time-series data with limited sample points. This method involves network structure design and implementation algorithms for network training and the forecasting process. Numerical experiments were performed with statistical construction waste data for Shanghai and Hong Kong. Compared with other time-series forecasting models such as ridge regression (RR), support vector regression (SVR), and back-propagation neural networks (BPNN), this paper demonstrates that the proposed LSTM-based forecasting model is effective and accurate in predicting construction waste generation. [ABSTRACT FROM AUTHOR]