Quantification of the Post-Fire Strength Retention Factors for Selected Standard Duplex and Lean Duplex Stainless Steel Grades.

The experimental quantification of retention factors related to the post-fire strength as well as the post-fire ductility of intentionally selected stainless steel grades applied in construction is the objective of the research presented here. These steel grades are characterized by a two-phase austenitic-ferritic microstructure of the duplex type. In this context, two mutually corresponding chromium-nickel-molybdenum steel grades are subjected to analysis, namely X2CrNiMoN22-5-3 steel belonging to the standard duplex group (DSS 22% Cr) and X2CrMnNiN21-5-1 steel belonging to the lean duplex group (LDSS). The similarities and differences in the mechanical properties exhibited by these steel grades after effective cooling, following more or less prolonged simulated fire action conforming to several development scenarios, are identified and indicated. The resistance of a given steel grade to permanent structural changes induced by the heating program proved to be the critical factor determining these properties and resulting in many cases in increased susceptibility to brittle fracture. The results obtained experimentally seem to confirm the quantitative estimates of post-fire retention factors forecast by Molkens and his team, specified for the steels exhibiting a duplex-type structure and tested by us. However, several of these estimates might be considered somewhat risky. Nevertheless, our results do not confirm the significant post-fire strengthening of steel grades belonging to the LDSS group following prior heating at a sufficiently high temperature, as reported earlier by Huang Yuner and B. Young.

Impact Fracture Surfaces as the Indicators of Structural Steel Post-Fire Susceptibility to Brittle Cracking.

The results of experimental research on forecasting post-fire resistance to brittle failure of selected steel grades used in construction are presented and discussed in this paper. The conclusions are based on detailed analysis of fracture surfaces obtained in instrumented Charpy tests. It has been shown that the relationships formulated based on these tests agree well with conclusions drawn based on precise analysis of appropriate F-s curves. Furthermore, other relationships between lateral expansion LE and energy Wt required to break the sample constitute an additional verification in both qualitative and quantitative terms. These relationships are accompanied here by values of the SFA(n) parameter, which are different, depending on the character of the fracture. Steel grades differing in microstructure have been selected for the detailed analysis, including: S355J2+N-representative for materials of ferritic-pearlitic structure, and also stainless steels such as X20Cr13-of martensitic structure, X6CrNiTi18-10-of austenitic structure and X2CrNiMoN22-5-3 duplex steel-of austenitic-ferritic structure.

Influence of Long-Term Subcritical Annealing on the Unalloyed Steel Welded Joint Microstructure.

The article presents changes in the microstructure of hot-rolled unalloyed structural steel after the arc welding process and in the state after long-term exposure to 600 °C during operation. These studies enable a clear assessment of the effects of long-term exposure to elevated temperature relative to the as-welded condition, which has not been reported. The microstructure examination was carried out on welded joints in eight different zones of the joint. Studies have shown that the welding thermal cycle causes significant changes in the microstructure in the area of the base material heated above the A1 temperature-the heat-affected zone (HAZ)-and in the weld area in the case of multi-pass welding. The long-term exposure of the subcritical temperature of 600 °C on the welded joint leads to the phenomenon of cementite spheroidization in the pearlite in all zones of the joint, while preserving the band structure of the steel after rolling and the structural structure. In the case of the weld, acicular and side-plate ferrite disappearance was observed.

Duplex Steels Used in Building Structures and Their Resistance to Chloride Corrosion.

Welded structures made of duplex steels are used in building applications due to their resistance to local corrosion attack initiated by chlorides. In this paper, the material and technological factors determining the corrosion resistance are discussed in detail. Furthermore, recommendations are formulated that allow, in the opinion of the authors, to obtain a maximum corrosion resistance for welded joints. The practical aspects of corrosion resistance testing are also discussed, based on the results of qualification tests. This work is of a review character. The conclusions and practical recommendations are intended for contractors and investors of various types of structures made of the duplex steel. The recommendations concern the selection and use of duplex steels, including the issues of metallurgy, welding techniques, and corrosion protection.

Comparative Study of Supervised Machine Learning Algorithms for Predicting the Compressive Strength of Concrete at High Temperature.

High temperature severely affects the nature of the ingredients used to produce concrete, which in turn reduces the strength properties of the concrete. It is a difficult and time-consuming task to achieve the desired compressive strength of concrete. However, the application of supervised machine learning (ML) approaches makes it possible to initially predict the targeted result with high accuracy. This study presents the use of a decision tree (DT), an artificial neural network (ANN), bagging, and gradient boosting (GB) to forecast the compressive strength of concrete at high temperatures on the basis of 207 data points. Python coding in Anaconda navigator software was used to run the selected models. The software requires information regarding both the input variables and the output parameter. A total of nine input parameters (water, cement, coarse aggregate, fine aggregate, fly ash, superplasticizers, silica fume, nano silica, and temperature) were incorporated as the input, while one variable (compressive strength) was selected as the output. The performance of the employed ML algorithms was evaluated with regards to statistical indicators, including the coefficient correlation (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual models using DT and ANN gave R2 equal to 0.83 and 0.82, respectively, while the use of the ensemble algorithm and gradient boosting gave R2 of 0.90 and 0.88, respectively. This indicates a strong correlation between the actual and predicted outcomes. The k-fold cross-validation, coefficient correlation (R2), and lesser errors (MAE, MSE, and RMSE) showed better performance than the ensemble algorithms. Sensitivity analyses were also conducted in order to check the contribution of each input variable. It has been shown that the use of the ensemble machine learning algorithm would enhance the performance level of the model.

Post-Fire Susceptibility to Brittle Fracture of Selected Steel Grades Used in Construction Industry-Assessment Based on the Instrumented Impact Test.

The change in the value of the breaking energy is discussed here for selected steel grades used in building structures after subjecting the samples made of them to episodes of heating in the steady-state heating regime and then cooling in simulated fire conditions. These changes were recorded based on the instrumented Charpy impact tests, in relation to the material initial state. The S355J2+N, 1H18N9T steels and also X2CrNiMoN22-5-3 duplex steel were selected for detailed analysis. The fire conditions were modelled experimentally by heating the samples and then keeping them for a specified time at a constant temperature of: 600 °C (first series) and 800 °C (second series), respectively. Two alternative cooling variants were investigated in the experiment: slow cooling of the samples in the furnace, simulating the natural fire progress, without any external extinguishing action and cooling in water mist simulating an extinguishing action by a fire brigade. The temperature of the tested samples was set at the level of -20 °C and alternatively at the level of +20 °C. The conducted analysis is aimed at assessing the risk of sudden, catastrophic fracture of load-bearing structure made of steel degraded as a result of a fire that occurred previously with different development scenarios.

Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches.

To avoid time-consuming, costly, and laborious experimental tests that require skilled personnel, an effort has been made to formulate the depth of wear of fly-ash concrete using a comparative study of machine learning techniques, namely random forest regression (RFR) and gene expression programming (GEP). A widespread database comprising 216 experimental records was constructed from available research. The database includes depth of wear as a response parameter and nine different explanatory variables, i.e., cement content, fly ash, water content, fine and coarse aggregate, plasticizer, air-entraining agent, age of concrete, and time of testing. The performance of the models was judged via statistical metrics. The GEP model gives better performance with R2 and ρ equals 0.9667 and 0.0501 respectively and meet with the external validation criterion suggested in the previous literature. The k-fold cross-validation also verifies the accurateness of the model by evaluating R2, RSE, MAE, and RMSE. The sensitivity analysis of GEP equation indicated that the time of testing is the influential parameter. The results of this research can help the designers, practitioners, and researchers to quickly estimate the depth of wear of fly-ash concrete thus shortening its ecological susceptibilities that push to sustainable and faster construction from the viewpoint of environmentally friendly waste management.