This paper proposes the use of Gaussian Bayesian networks (GBNs) for damage detection of steel truss bridges by using the strain monitoring data. Based on the proposed damage detection procedure, a three-layer GBN model is first constructed based on the load factors, structural deflections, and the stress measurements of steel truss bridges. More specifically, the load factors of the structures are defined as the first-layer network nodes, structural deflections are considered as the second-layer network nodes, and the third-layer nodes of the GBN model are built based on the stress data of the truss elements. To achieve the training for the constructed GBN model, the finite element analysis of the bridge structures under the different load factors is performed. Then, the training of the network is performing by using the maximum likelihood estimation approach, and the optimized network parameters are obtained. Based on the trained network model, the measured load factors and the corresponding stress monitoring data of a limited number of truss elements are considered as input, and the stress measurements of all truss elements of bridges can be accurately estimated by searching the optimized topological information among network nodes. For a steel truss bridge, when the truss elements are damaged, the stress states of the damaged elements will be changed. Therefore, a damage index is further constructed for damage detection of steel truss bridges based on the changed stress states of those damaged elements. To verify the feasible and effective use of the proposed damage detection approach, an 80 m steel truss bridge with various damage cases was conducted as numerical simulations, and the investigation results show that the trained GBN can be accurately used for stress prediction of steel truss bridges, and the proposed damage index with the estimated stress data can be further applied for structural damage localization and quantification with a better accuracy. Furthermore, the results also suggest that the proposed damage detection procedure is accurate and reliable for steel truss bridges under vehicle loads.

This paper proposes using neutral axis locations to monitor and quantify the prestress force in post-tensioned precast segmental beams. Strain measurements are used to obtain the neutral axis locations of specific cross-sections of the precast prestressed segmental beams, based on the plane–remains–plane and linear strain distribution assumption. A theoretical calculation method based on the static equilibrium of a specific cross-section is developed to calculate the prestress force in segmental beams based on the neutral axis location. To verify the accuracy of the proposed method, a post-tensioned prestressed segmental beam is built and tested in the laboratory. A corresponding high-fidelity finite element model is also developed based on the beam design and material properties. Experimental studies and numerical simulations are conducted to verify the feasibility and accuracy of the proposed method in quantifying the prestress force in precast segmental beams. Both experimental and numerical results demonstrate that the proposed method can reliably estimate the prestress force, which can be used to monitor the prestress force loss in post-tensioned structures.

This paper proposes a novel hybrid‐driven digital twin (DT) framework for time‐variant reliability assessment of civil structures, which mainly consists of four modules, including physics model construction, data‐driven model calibration, failure probability calculation, and time‐variant reliability prediction. In the first module, a DT model of a specific structure is constructed to simulate structural dynamic responses. Then, an improved unscented Kalman filter (IUKF) algorithm is performed to continuously calibrate the parameters of DT model. Subsequently, in module 3, the subset simulation (SS) approach is employed to calculate failure probability of structures subjected to various model parameter samples, and the generated input–output samples are further applied for metamodel training. A Kriging metamodeling is used to construct the correlation between model parameters and structural failure probability. Once the metamodel is well trained, the time‐variant reliability assessment of structures can be continuously achieved in module 4. Numerical simulations on a Bouc–Wen model are conducted to validate the feasibility and accuracy of the proposed approach. Furthermore, a scaled column shake table structure is further employed to verify the effectiveness of the proposed approach. Both numerical and experimental results have shown that the proposed approach is capable of conducting time‐variant reliability assessment of civil structures.

In this study, variational Bayesian inference (VBI) with Gaussian mixture models is applied to update models of nonlinear structures, and then, the calibrated model is employed to estimate the failure probability of structures using a subset simulation (SS) algorithm. To improve the computation efficiency of probabilistic nonlinear model updating, a Gaussian Process (GP) model is used to construct a surrogate likelihood function in Bayesian inference using an active learning algorithm, and then, Gaussian mixture models (GMMs) are employed to approximate the unknown posterior probabilistic density functions (PDFs) of model parameters. The optimized hyperparameters of GMMs can be obtained by maximizing the evidence lower bound (ELBO), and the stochastic gradient search method is used to solve this optimization problem. Based on the optimized hyperparameters, the posterior distributions of model parameters can be approximated using a combination of multiple Gaussian components. Subsequently, the SS algorithm is used to calculate the earthquake-induced failure probability of structures based on the calibrated nonlinear model. To verify the feasibility and effectiveness of the proposed method, a numerical simulation of a two-span bridge structure subjected to seismic excitations was developed. Moreover, the proposed strategy is further applied to estimate the failure probability of a scaled monolithic column structure subjected to bi-directional earthquake excitations. Both numerical and experimental results indicate that the proposed method is feasible and effective for probabilistic nonlinear model updates, and the updated model can significantly enhance the accuracy of structural failure probability predictions.

With the rapid development of transportation infrastructures, more and more new-built bridge structures are frequently required to cross over existing traffic lines, significantly increasing constr...