A stacked ensemble learning model is developed to predict the modal parameters of space grid steel structures under environmental effects. Potential damage is detected via statistical analysis of the prediction residuals. For this purpose, five standalone heterogeneous machine learning models were trained for predicting natural frequencies; each model used the principal components of the environmental data as input parameters. Next, a stacked ensemble learner was built using the outputs of the five standalone models as its inputs. Finally, a damage indicator combining the predicted residuals of multiple orders of natural frequencies is proposed and statistically analyzed for accurate damage detection. To verify the effectiveness of the proposed method, a space grid model was created in the field environment and measured for a period. Dynamic and environmental data were collected, such as ambient temperature, humidity, wind speed and direction, and structural surface temperature. An automated procedure of the covariance-driven stochastic subspace identification method was conducted to identify bulk mode. The environmental dependence of the natural frequencies, damping ratios, and vibration modes was analyzed. Then, the method was validated based on short-term monitoring data from the baseline health state and unknown future states. The results show that the natural frequencies and damping ratios of space grid structures fluctuate significantly on a daily basis due to environmental influences. Stacked ensemble learning utilizes predictions from multiple heterogeneous models to produce a better predictive model. The statistical analysis of the prediction residuals by ensemble learning effectively removes the environmental influences, allowing for timely structural damage detection.

Under the framework of Bayesian theory, a probabilistic method for damage diagnosis of latticed shell structures based on temperature-induced strain is proposed. First, a new damage diagnosis index is proposed based on the correlation between temperature-induced strain and structural parameters. Then, Markov Chain Monte Carlo is adopted to analyze the newly proposed diagnosis index, based on which the frequency distribution histogram for the posterior probability of the diagnosis index is obtained. Finally, the confidence interval of the damage diagnosis is determined by the posterior distribution of the initial state (baseline condition). The damage probability of the unknown state is also calculated. The proposed method was validated by applying it to a latticed shell structure with finite element developed, where the rod damage and bearing failure were diagnosed based on importance analysis and temperature sensitivity analysis of the rod. The analysis results show that the proposed method can successfully consider uncertainties in the strain response monitoring process and effectively diagnose the failure of important rods in radial and annular directions, as well as horizontal (x- and y-direction) bearings of the latticed shell structure.