Effect of Steel Slag Powder Content and Curing Condition on the Performance of Alkali Activated Materials Based Ultra-High Performance Concrete (UHPC) Matrix

Conventional ultra-high performance concrete (UHPC) has excellent development potential. However, a significant quantity of CO2 is produced throughout the cement-making process, which is in contrary to the current worldwide trend of lowering emissions and conserving energy, thus restricting the further advancement of UHPC. Considering climate change and sustainability concerns, cementless, eco-friendly, alkali-activated UHPC (AA-UHPC) materials have recently received considerable attention. Following the emergence of advanced prediction techniques aimed at reducing experimental tools and labor costs, this study provides a comparative study of different methods based on machine learning (ML) algorithms to propose an active learning-based ML model (AL-Stacked ML) for predicting the compressive strength of AA-UHPC. A data-rich framework containing 284 experimental datasets and 18 input parameters was collected. A comprehensive evaluation of the significance of input features that may affect compressive strength of AA-UHPC was performed. Results confirm that AL-Stacked ML-3 with accuracy of 98.9% can be used for different general experimental specimens, which have been tested in this research. Active learning can improve the accuracy up to 4.1% and further enhance the Stacked ML models. In addition, graphical user interface (GUI) was introduced and validated by experimental tests to facilitate comparable prospective studies and predictions.

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Mechanical Performance Optimization and Microstructural Mechanism Study of Alkali-Activated Steel Slag–Slag Cementitious Materials

Wang,

Xu,

Zhang

et al. 2024

Buildings

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The optimal proportion of alkali-activated steel slag–slag cementitious materials is investigated by considering the combined effects of steel slag content, alkali content, water glass modulus, and water–binder ratio using the Box–Behnken design in response surface methodology. Qualitative and semi-quantitative analyses of X-ray diffraction (XRD) patterns and scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS) images are conducted. The microstructural mechanism is elucidated based on the chemical composition, surface morphology, and microscale pore (crack) structures of the samples. A microreaction model for the alkali-activated steel slag and slag is proposed. The optimal composition for alkali-activated steel slag–slag cementitious materials is as follows: steel slag content, 38.60%; alkali content, 6.35%; water glass modulus, 1.23; and water–binder ratio, 0.48. The strength values predicted by the response surface model are p1d = 32.66 MPa, p7d = 50.46 MPa, and p28d = 56.87 MPa. XRD analysis confirms that the compressive strength of the sample is not only influenced by the amount of gel formed, but also, to a certain extent, by the CaCO3 crystals present in the steel slag, which act as nucleation sites. The SEM-EDS results confirm that the gel phase within the system comprises a hydrated calcium silicate gel formed through the reaction of volcanic ash and geopolymer gel formed through geo-polymerization. Analysis of the pore (crack) structure reveals that the compressive strength of the specimens is primarily influenced by porosity, with a secondary influence of the pore fractal dimension.

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Effect of Steel Slag Powder Content and Curing Condition on the Performance of Alkali Activated Materials Based Ultra-High Performance Concrete (UHPC) Matrix

Cited by 7 publications

References 35 publications

A Succinct Review on Use of Steel Slag in Mortars and Concrete

A Succinct Review on Use of Steel Slag in Mortars and Concrete

Active learning on stacked machine learning techniques for predicting compressive strength of alkali-activated ultra-high-performance concrete

Mechanical Performance Optimization and Microstructural Mechanism Study of Alkali-Activated Steel Slag–Slag Cementitious Materials

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