Shouhua Liu

This study used calcium hydroxide (CH) to simulate the alkaline environment of cement and to activate lithium slag (LS), aiming to reveal the mechanism of LS in cement. The early-age hydration of LS blended with 10 wt.% CH was monitored via isothermal calorimetry (ICC) at ambient temperature, followed by a comparative analysis of phase assemblage, microstructure, and macroscopic properties under standard and steam curing conditions. The results show that LS exhibits superior early reactivity within the first 9 h, which is attributed to abundant ettringite formation. Two distinct exothermic peaks were identified during LS-CH hydration, corresponding to (i) ettringite formation accompanied by LS dissolution and C–S–H precipitation, and (ii) CaCO₃ crystallization and renewed ettringite formation. The hydrated paste consists of abundant AFt, CaCO₃ polymorphs, unreacted LS particles, and a small amount of C–S–H gel with a low Ca/Si ratio and incorporating Al and S. This unique phase assemblage results in a coarser pore structure and lower specific surface area compared with conventional cement paste. Nevertheless, the system achieves a relatively high 28-day compressive strength, highlighting the promise of LS-CH blends as sustainable cementitious materials.

In recent years, there has been a growing use of cold-formed steel (CFS) structures in the field of civil engineering. The objective of this study is to utilize gene expression programming (GEP) in order to forecast the ultimate bearing capacity of cold-formed steel columns. The buckling resistance of built-up back-to-back cold-formed (BCF) thin-walled tube columns under axial compression, and of cold-formed thick-walled steel columns under combined axial compression and bending, is examined in this paper. The data were collected from various studies to develop and verify the proposed model, with training and testing sets of 160 and 14, and 2000 and 500, respectively. The performance of the genetically developed GEP models was evaluated and compared with that of the mechanical models specified in American and Chinese specifications. The GEP models demonstrated significantly better performance compared with that of the code-specified models. The results generated by the GEP models demonstrate stronger alignment with both experimental data and analytical predictions. This study also demonstrates the capability of the GEP models to calculate the ultimate bearing capacity, with the proposed mechanical models being used as a reference for calculations.

The mechanical properties of soil, resulting from the weathering of rocks through physical and chemical processes, exhibit spatial variability. This variability introduces uncertainties in the design and characteristics of excavation projects. To address these uncertainties caused by soil spatial variability, safety factors are commonly used in excavation design. However, using the same safety factor for different indicators of soil spatial variability is illogical. Therefore, specialized research on the characteristics of deep excavations in the context of soil spatial variability is necessary, as it provides the theoretical basis for rational excavation design. In this study, we assumed that soil parameters follow a lognormal distribution, while spatial correlation adheres to a Gaussian function. We developed a random finite element algorithm for deep excavations, which incorporated Python programming and the ABAQUS computational platform. This algorithm was created within the framework of random field theory and Monte Carlo simulation. The results of our study indicate that, influenced by soil spatial variability, the lateral wall movements and ground surface settlements exhibit discrete distributions near the deterministic results. The maximum deformation of the excavation follows a normal distribution, while the pattern of ground surface settlements demonstrates diversity and chaotic characteristics. The extent to which soil spatial variability affects deep excavations is correlated with indicators of this variability. As the coefficient of soil spatial variability increases, the diversity and chaotic characteristics of ground surface settlements become more prominent. The locations of maximum ground surface settlement and maximum deformation becomes more scattered. Consequently, the probability of excavation failure increases, and the reliability index of the excavation decreases. In summary, soil spatial variability significantly impacts deformation prediction and safety control during the design and construction stages of deep excavations. Therefore, it is crucial to consider the influence of soil spatial variability when designing deep excavations, based on the variability indicators.