Full-scale dome structures intrinsically have numerous sources of irreducible aleatoric uncertainties. A large-scale numerical simulation of the dome structure is required to quantify the effects of these sources on the dynamic performance of the structure using the finite element method (FEM). To reduce the heavy computational burden, a surrogate model of a dome structure was constructed to solve this problem. The dynamic global sensitivity of elastic and elastoplastic structures was analyzed in the uncertainty quantification framework using fully quantitative variance- and distribution-based methods through the surrogate model. The model considered the predominant sources of uncertainty that have a significant influence on the performance of the dome structure. The effects of the variables on the structural performance indicators were quantified using the sensitivity index values of the different performance states. Finally, the effects of the sample size and correlation function on the accuracy of the surrogate model as well as the effects of the surrogate accuracy and failure probability on the sensitivity index values are discussed. The results show that surrogate modeling has high computational efficiency and acceptable accuracy in the uncertainty quantification of large-scale structures subjected to earthquakes in comparison to the conventional FEM.

High-rise intake towers in high-intensity seismic areas are prone to structural safety problems under vibration. Therefore, effective and low-cost anti-seismic engineering measures must be designed for protection. An intake tower in northwest China was considered the research object, and its natural vibration characteristics and dynamic response were first analyzed using the mode decomposition response spectrum method based on a three-dimensional finite element model. The non-dominated sorting genetic algorithm-II (NSGA-II) was adopted to optimize the anti-seismic scheme combination by comprehensively considering the dynamic tower response and variable project cost. Finally, the rationality of the original intake tower antiseismic design scheme was evaluated according to the obtained optimal solution set, and recommendations for improvement were proposed. The method adopted in this study may provide significant references for designing anti-seismic measures for high-rise structures such as intake towers located in high-intensity earthquake areas.

Concrete is the most commonly used construction material. However, its production leads to high carbon dioxide (CO₂) emissions and energy consumption. Therefore, developing waste-substitutable concrete components is necessary. Improving the sustainability and greenness of concrete is the focus of this research. In this regard, 899 data points were collected from existing studies where cement, slag, fly ash, superplasticizer, coarse aggregate, and fine aggregate were considered potential influential factors. The complex relationship between influential factors and concrete compressive strength makes the prediction and estimation of compressive strength difficult. Instead of the traditional compressive strength test, this study combines five novel metaheuristic algorithms with extreme gradient boosting (XGB) to predict the compressive strength of green concrete based on fly ash and blast furnace slag. The intelligent prediction models were assessed using the root mean square error (RMSE), coefficient of determination (R²), mean absolute error (MAE), and variance accounted for (VAF). The results indicated that the squirrel search algorithm-extreme gradient boosting (SSA-XGB) yielded the best overall prediction performance with R² values of 0.9930 and 0.9576, VAF values of 99.30 and 95.79, MAE values of 0.52 and 2.50, RMSE of 1.34 and 3.31 for the training and testing sets, respectively. The remaining five prediction methods yield promising results. Therefore, the developed hybrid XGB model can be introduced as an accurate and fast technique for the performance prediction of green concrete. Finally, the developed SSA-XGB considered the effects of all the input factors on the compressive strength. The ability of the model to predict the performance of concrete with unknown proportions can play a significant role in accelerating the development and application of sustainable concrete and furthering a sustainable economy.

In this study, a numerical analysis was conducted on aluminum alloy reticulated shells (AARSs) with gusset joints under fire conditions. First, a thermal-structural coupled analysis model of AARSs considering joint semi-rigidity was proposed and validated against room-temperature and fire tests. The proposed model can also be adopted to analyze the fire response of other reticulated structures with semi-rigid joints. Second, a parametric analysis was conducted based on the numerical model to explore the buckling behavior of K6 AARS with gusset joints under fire conditions. The results indicated that the span, height-to-span ratio, height of the supporting structure, and fire power influence the reduction factor of the buckling capacity of AARSs under fire conditions. In contrast, the reduction factor is independent of the number of element divisions, number of rings, span-to-thickness ratio, and support condition. Subsequently, practical design formulae for predicting the reduction factor of the buckling capacity of K6 AARSs were derived based on numerical analysis results and machine learning techniques to provide a rapid evaluation method. Finally, further numerical analyses were conducted to propose practical design suggestions, including the conditions of ignoring the ultimate bearing capacity analysis of K6 AARS and ignoring the radiative heat flux.