It is often computationally expensive to monitor structural health using computer models. This time-consuming process can be relieved using surrogate models, which provide cheap-to-evaluate metamodels to replace the original expensive models. Because of their high accuracy, simplicity, and efficiency, Artificial Neural Networks (ANNs) have gained considerable attention in this area. This paper reviews the application of ANNs as surrogates for structural health monitoring in the literature. Moreover, the review contains fundamental information, detailed discussions, wide comparisons, and suggestions for future research. Surrogates in this literature review are divided into parametric and nonparametric models. In the past, nonparametric models dominated this field, but parametric models have gained popularity in the recent decade. A parametric surrogate is commonly supplied with metaheuristic algorithms, and can provide high levels of identification. Recurrent networks, instead of traditional ANNs, have also become increasingly popular for nonparametric surrogates.

Computational cost reduction and best model updating method seeking are the key issues during model updating for different kinds of bridges. This paper presents a combined method, Kriging model and Latin hypercube sampling method, for finite element (FE) model updating. For FE model updating, the Kriging model is serving as a surrogate model, and it is a linear unbiased minimum variance estimation to the known data in a region which have similar features. To predict the relationship between the structural parameters and responses, samples are preselected, and then Latin hypercube sampling (LHS) method is applied. To verify the proposed algorithm, a truss bridge and an arch bridge are analyzed. Compared to the predicted results obtained by using a genetic algorithm, the proposed method can reduce the computational time without losing the accuracy.

Micro-electro-mechanical system (MEMS) accelerometer-based inclinometers are widely used to measure deformations of civil structures. To further improve the measurement accuracy, a new calibration technique was proposed in this paper. First, a single-parameter calibration model was constructed to obtain accurate angles. Then, an image-processing-based method was designed to obtain the key parameter for the calibration model. An ADXL355 accelerometer-based inclinometer was calibrated to evaluate the feasibility of the technique. In this validation experiment, the technique was proven to be reliable and robust. Finally, to evaluate the performance of the technique, the calibrated MEMS inclinometer was used to measure the deflections of a scale beam model. The experimental results demonstrate that the proposed technique can yield accurate deformation measurements for MEMS inclinometers.

Location localization technology is used in a number of industrial and civil applications. Real time location localization accuracy is highly dependent on the quality of the distance measurements and efficiency of solving the localization equations. In this paper, we provide a novel approach to solve the nonlinear localization equations efficiently and simultaneously eliminate the bad measurement data in range-based systems. A geometric intersection model was developed to narrow the target search area, where Newton’s Method and the Direct Search Method are used to search for the unknown position. Not only does the geometric intersection model offer a small bounded search domain for Newton’s Method and the Direct Search Method, but also it can self-correct bad measurement data. The Direct Search Method is useful for the coarse localization or small target search domain, while the Newton’s Method can be used for accurate localization. For accurate localization, by utilizing the proposed Modified Newton’s Method (MNM), challenges of avoiding the local extrema, singularities, and initial value choice are addressed. The applicability and robustness of the developed method has been demonstrated by experiments with an indoor system.