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The shape sensing, i.e. the reconstruction of the displacement field of a structure from discrete strain measures, is a key tool for the support and development of the modern Structural Health Monitoring frameworks and has received a huge attention in the last few decades. The parallel increase in the use of composite materials in the aerospace industry has consequently generated the need to investigate the applicability of the shape sensing methods to this peculiar kind of materials. In fact, the manufacturing complexity of the composite materials can result in a significant variability in the characteristics of the material. Therefore, a study on the propagation of this kind of uncertainty on the performance of the shape sensing methods is paramount. The uncertainties in the strain measurements can influence the shape sensing results and must be also considered. This paper, for the first time, investigates the propagation of these two sources of inputs' uncertainty on the performance of three shape sensing methods, the inverse Finite Element Method (iFEM), the Modal Method (MM) and the Ko's Displacement theory. Using the Monte Carlo Simulation (MCS) with Latin Hypercube Sampling (LHS), the robustness of the three methods with respect to the inputs' variability is evaluated on the reconstruction of the displacement field of a composite wing box. The MM shows a significant robustness and the iFEM, although more affected by the uncertainties, is the method that achieves the best accuracy. The Ko's displacement theory, on the other hand, is the less accurate and the less robust. [ABSTRACT FROM AUTHOR]

• Presented a probabilistic tensor learning approach for SHM data imputation and forecasting. • Proposed an efficient incremental learning scheme to deal with streaming data. • Developed a Bayesian tensor decomposition framework for missing data recovery. • Integrated tensor decomposition and vector autoregression for response forecasting. • Demonstrated effectiveness of the approach on a concrete bridge with over three-year records. There has been increased interest in missing sensor data imputation, which is ubiquitous in the field of structural health monitoring (SHM) due to discontinuous sensing caused by sensor malfunction. Recent development in Bayesian temporal factorization models for high-dimensional time series analysis has provided an effective tool to solve both imputation and prediction problems. However, for large datasets, the default Bayesian temporal factorization model becomes inefficient since the model has to be fully retrained when new data arrives. A potential solution is to train the model using a short time window covering only most recent data; however, by doing so, we may miss some critical dynamics and long-term dependencies which can only be identified from a longer time window. To address this fundamental issue in temporal factorization models, this paper presents an incremental Bayesian matrix/tensor learning scheme to achieve efficient imputation and prediction of structural response in long-term SHM. In particular, a spatiotemporal tensor is first constructed followed by Bayesian tensor factorization that extracts latent features for missing data imputation. To enable structural response forecasting based on long-term and incomplete sensing data, we develop an incremental learning scheme to effectively update the Bayesian temporal factorization model. The performance of the proposed approach is validated on continuous field-sensing data (including strain and temperature records) of a concrete bridge, based on the assumption that strain time histories are highly correlated to temperature recordings. The results indicate that the proposed probabilistic tensor learning framework is accurate and robust even in the presence of large rates of random missing, structured missing and their combination. The effect of rank selection on the imputation and prediction performance is also investigated. The results show that a better estimation accuracy can be achieved with a higher rank for random missing whereas a lower rank for structured missing. [ABSTRACT FROM AUTHOR]

• A dictionary is proposed for CS of wind speed using correlation. • A new sliding window is used considering lag, and window size is suggested. • The performance is evaluated using two large-scale spatial structures. • The performance is compared with Fourier basis and linear interpolation. • The influences of relevant critical parameters are comprehensively explored. The real-time wind monitoring is widely used to evaluate the wind effect on the large-scale spatial structures. Wireless sensor network (WSN) is usually the first choice for the large-scale spatial structures to collect wind monitoring data because of its super-large size. Compressive sensing (CS) has great potential in solving the energy problem of WSN and reduces the difficulty in transmission of massive data based on sparsity. However, wind signals are often not naturally sparse on the traditional bases (e.g., Fourier basis). This paper proposes a new method of constructing a dedicated dictionary for wind speed signals using the time-shift strategy. With this proposed dictionary, the signals can be compressed by random sampling and recovered by ℓ 1 -norm sparse regularization. The performance of the improved CS methodology is evaluated using two large-scale spatial structures. The results show that the proposed CS methodology has better performance than the traditional CS algorithm with the Fourier basis and the linear interpolation method. Furthermore, the influences of the relevant critical parameters (regularization parameter, lag, sliding window size, and compression ratio) of the improved CS methodology are comprehensively explored. [ABSTRACT FROM AUTHOR]