Yongzhi Lei

An accurate finite element model (FEM) plays a critical role in the structural damage identification. However, due to the existence of the uncertainties, such as material properties and modeling errors, it always exists some gaps between the analytical FEM and experimental structure. While an artificial neural network (ANN)-based model updating methods have been widely adopted to narrow the gap and obtain a baseline FEM, it still faces inaccurate results and fails to meet the physical law. In this regard, the study proposes a novel physics-based loss function inspired by modal sensitivity analysis and incorporates it into the residual neural network, thereby forming a novel physics-guided neural network (PGNN) method. The mapping relationship between the input of structural responses and output of model updating variables is constrained to retain its physical meaning by guiding the training process instead of pure data association, which aims to improve the accuracy of the ANN-based method and achieve accurate and high-efficiency model updating. An experimental example of a continuous rigid frame bridge is adopted to verify the feasibility of the proposed method. Additionally, other common model updating methods, including moth-flame optimization and regularization method, are used to make a comparison. The noise-robustness of the proposed method is investigated as well. Compared to the existing method, the results illustrate that the proposed PGNN method can achieve better model updating and good noise-robustness under high uncertainties, which means the introduction of the physics-based loss function significantly enhances the parameters updating ability of the neural network. The proposed method exhibits high efficiency and promising potential for large-scale bridge structure model updating.

In this study, a novel Bayesian empowered piecewise multi-objective function is developed, in which a traditional objective function is applied to realize the rough optimization in the first stage to determine the approximate results. Then, a sparse Bayesian learning-based objective function is applied to realize refined optimization with the obtained approximate results in the second stage. On the other hand, considering the sparsity of the structural damage identification, two simple but effective calculation frameworks, the colony initial sparsification and elite clustering framework, are integrated into the evolution, making the algorithm adaptable to handle the defined sparse optimization problem. Therefore, the proposed calculation framework is more efficient and robust while no initial conditions are needed. We will carry out a numerical example on a truss and an experimental validation on a fixed-end beam with a single-sensor measurement system to verify the method.

The most commonly used method for sampling damage parameters from the posterior distribution is the Markov chain Monte Carlo (MCMC) method. The population MCMC method as one of the MCMC methods has been utilized for damage identification by some researchers recently. Nevertheless, for the conventional population MCMC methods, these sampling methods often require significant computational resources and tuning of a large number of algorithm parameters. Aiming at the problem of difficulty in selecting the proposal distribution and low computational efficiency in the conventional MCMC method, this paper proposed a simple population Metropolis–Hastings (SP-MH) algorithm for the damage identification, which is realized by exchanging information among chains in a relatively small population and using tuning-free strategy. Then, a numerical cantilever beam and an experimental frame are utilized to verify the effectiveness and feasibility of the proposed algorithm, it can be seen that the convergence rate of the SP-MH algorithm is faster than that of the Differential Evolution Monte Carlo (DE-MC) algorithm, and in a small population state, the SP-MH algorithm can still maintain convergence, saving plenty of computing time for damage identification. The results show that the SP-MH algorithm is feasible and accurate in practice damage identification, and the SP-MH algorithm performs better than the DE-MC algorithm. Compared with the DE-MC algorithm, the SP-MH algorithm is simple and convenient for damage identification due to its tuning-free strategy and relatively small population.

In civil engineering structures, modal changes produced by environmental conditions, especially temperature, can be equivalent to or greater than the ones produced by damage. Therefore, it is necessary to distinguish the variations in structural properties caused by environmental changes from those caused by structural damages. In this paper, we present a review of the technical literature concerning variations in the vibration properties of civil structures under varying temperature conditions and damage identification methods for bridge structures. First, the literature on the effect of temperature on vibration properties is roughly divided into experimental and theoretical studies. According to the classification of theoretical research methods, the progress in research on the probability analysis method, the artificial intelligence method, and the optimization algorithm method in this field is reviewed. Based on the different methods of experimental research employed in this field, the experimental research is reviewed according to qualitative and quantitative analyses. Then, damage identification methods for bridge structures are reviewed, considering data-based and model-based methods. Finally, different research methods are summarized.

In this paper, a new kind of steel-polyvinyl alcohol (PVA) hybrid fiber concrete with high early strength and better road performance is proposed for use in the anchorage zone of a bridge expansion and contraction installation (BECI). Firstly, a comprehensive test design method is adopted to evaluate the mechanical properties of the concrete by adding different percentages of fibers, and the optimal percentages of steel and PVA fiber additives are determined. The results of the compressive and flexural strength test indicated that the concrete with added 1.5 % steel fiber and 0.12 % PVA fiber has a higher early strength. Secondly, to evaluate the performance of the improved concrete, the finite-element method was adopted to model the anchorage zone concrete of the BECI, and the loading location of maximum stress was determined and some factors, such as overload and horizontal force coefficient, were considered. The maximum stresses of the anchorage zone are less than the ultimate stresses of the steel-PVA hybrid fiber concrete. It can be concluded that the improved concrete has better road performance. Finally, a case study of the actual bridge was carried out to verify the practical value of the steel-PVA hybrid fiber concrete. Although there is some reduction in the compressive strength, the new concrete is more suitable than the conventional concrete to meet the requirements for the construction of concrete in the anchorage zone of the BECI, which can reduce the construction period and show great practical application value.

Civil structures are affected by some environmental factors, such as traffic, ambient temperature, and noises. The change of structural dynamic characteristics arising from these factors may cover up those coming from structural damage, which makes the evaluation of structural health conditions based on vibration data more difficult. To overcome this difficulty, a novel method based on the support vector machine (SVM) and moth-flame optimization (MFO) is proposed to identify the damage of structures considering temperature variations. First of all, SVM is adopted to determine temperature variations and possible damage locations using the first six natural frequencies, and MFO is exploited to locate and quantify the damage accurately through the objective function constructed with a frequency-based multiple damage location assurance criterion (FMDLAC) and modal strain energy-based index (MSEBI). The combination of MFO and SVM can promote the efficiency of damage identification and accurately analyze environmental effects, which is a creative method with good robustness to solve the issue of damage identification considering environmental factors. To verify the effectiveness of the proposed method, a numerical simply-supported beam example considering temperature variations, as well as random noise, is investigated, and the optimal parameters for the method are acquired. Finally, a practical engineering example, the I-40 Bridge, is adopted to confirm the feasibility of the method further. The results demonstrate that the proposed approach is of a good optimization performance and can identify the damage of large complex structures considering temperature variations, which is of great practical application value.