Health assessment and monitoring of engineered systems have become one of the fastest growing multi-disciplinary research areas over the last two decades. One of the largest concerns in structural health monitoring is how to infer structural conditions from the measurements and the data collected by sensors. The ultimate aim is to detect the structural damages with a high level of certainty and hence to extend the life of structures. In this study, a new strategy for structural damage detection is proposed using least square support vector machines based on a new combinational kernel. Thin plate spline Littlewood–Paley wavelet kernel function introduced in this article is a novel combinational kernel function, which combines thin plate spline radial basis function kernel with local characteristics and a modified Littlewood–Paley wavelet kernel function with global characteristics. During the process of structural damage detection, a social harmony search algorithm optimizes the parameters of least square support vector machine and the thin plate spline Littlewood–Paley wavelet kernel. The results obtained by this method are compared with least square support vector machine based on the other combinational and conventional kernels. These results show that the accuracy of damage detection based on least square support vector machine with thin plate spline Littlewood–Paley wavelet kernel is higher than other methods that utilize conventional kernels under similar conditions. In comparison with other combinational kernels, least square support vector machine with thin plate spline Littlewood–Paley wavelet kernel possesses a better dissemination and learning ability by incorporating the advantages of radial basis function kernel and wavelet kernel functions.

A critical problem facing data collection in structural health monitoring, for instance via sensor networks, is how to extract the main components and useful features for damage detection. A structural dynamic measurement is more often a complex time-varying process and therefore, is prone to dynamic changes in time-frequency contents. To extract the signal components and capture the useful features associated with damage from such non-stationary signals, a technique that combines the time and frequency analysis and shows the signal evolution in both time and frequency is required. Wavelet analyses have proven to be a viable and effective tool in this regard. Wavelet transform (WT) can analyze different signal components and then comparing the characteristics of each signal with a resolution matched to its scale. However, the challenge is the selection of a proper wavelet since various wavelets with varied properties that are to analyze the same data may result in different results. This article presents a study on how to carry out a comparative analysis based on analytic wavelet scalograms, using structural dynamic acceleration responses, to evaluate the effectiveness of various wavelets for damage detection in civil structures. The scalogram’s informative time-frequency regions are examined to analyze the variation of wavelet coefficients and show how the frequency content of a signal changes over time to detect transient events due to damage. Subsequently, damage-induced changes are tracked with time-frequency representations. Towards this aim, energy distribution and sharing information are investigated. The undamaged and damaged simulated comparative results of a structure reveal that the damaged structure were shifted from the undamaged structure. Also, the Bump wavelet shows the best results than the others.

A critical problem encountered in structural health monitoring of civil engineering structures, and other structures such as mechanical or aircraft structures, is how to convincingly analyze the nonstationary data that is coming online, how to reduce the high-dimensional features, and how to extract informative features associated with damage to infer structural conditions. Wavelet transform among other techniques has proven to be an effective technique for processing and analyzing nonstationary data due to its unique characteristics. However, the biggest challenge frequently encountered in assuring the effectiveness of wavelet transform in analyzing massive nonstationary data from civil engineering structures, and in structural health diagnosis, is how to select the right wavelet. The question of which wavelet function is appropriate for processing and analyzing the nonstationary data in civil engineering structures has not been clearly addressed, and no clear guidelines or rules have been reported in the literature to show how the right wavelet is chosen. Therefore, this study aims to address an important question in this regard by proposing a new framework for choosing a proper wavelet that can be customized for massive nonstationary data analysis, disturbances separation, and extraction of informative features associated with damage. The proposed method takes into account data type, data and wavelet characteristics, similarity, sharing information, and data recovery accuracy. The novelty of this study lies in integrating multi-criteria which are associated directly with features that correlated well with change in structures due to damage, including common criteria such as energy, entropy, linear correlation index, and variance. Also, it introduces and considers new proposed measures, such as wavelet-based nonlinear correlation such as cosh spectral distance and mutual information, wavelet-based energy fluctuation, measures-based recovery accuracy, such as sensitive feature extraction, noise reduction, and others to evaluate various base wavelets’ function capabilities for appropriate decomposition and reconstruction of structural dynamic responses. The proposed method is verified by experimental and simulated data. The results revealed that the proposed method has a satisfactory performance for base wavelet selection and the small order of Daubechies and Symlet provide the best results, especially order 3. The idea behind our proposed framework can be applied to other structural applications.

Modal macro strain-based damage identification is a promising approach since it has the advantages of high sensitivity and effectiveness over other related methods. In this paper, a basalt fiber-reinforced polymer (BFRP) pipeline system is used for analysis by using long-gauge distributed fiber Bragg grating (FBG) sensors. Dynamic macro strain responses are extracted to form modal macro strain (MMS) vectors. Both longitudinal distribution and circumferential distribution plots of MMS are compared and analyzed. Results show these plots can reflect damage information of the pipeline based on the previous work carried out by the authors. However, these plots may not be good choices for accurate detection of damage information since the model is 3D and has different flexural and torsional effects. Therefore, by extracting MMS information in the circumferential distribution plots, a novel deep neural network is employed to train and test these images, which reflect the important and key information of modal variance in the pipe system. Results show that the proposed Deep Learning based approach is a promising way to inherently identify damage types, location of the excitation load and support locations, especially when the structural types are complicated and the ambient environment is changing.