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Pressure mapping has garnered considerable interest in the healthcare and robotic industries. Low-cost and large-area compliant devices, as well as fast and effective computational algorithms, have been proposed in the last few years to facilitate distributed pressure sensing. One approach is to use electrical impedance tomography (EIT) to reconstruct the contact pressure distribution of piezoresistive materials. While tremendous success has been demonstrated, conventional algorithms may be unsuitable for real-time monitoring due to its computational demand and runtime. Moreover, the low resolution of reconstructed images is a well-known issue related to the regularization strategies typically employed for traditional EIT methods. Therefore, in this study, two different supervised machine learning (ML) approaches, namely, radial basis function networks and deep neural networks, were employed to efficiently solve the inverse EIT problem and improve the resolution of reconstructed pressure maps. The demonstration of high-resolution pressure mapping, specifically, for identifying pressure hotspots, was achieved using a carbon nanotube-based thin film integrated with foam.

The advent of parallel computing capabilities, further boosted through the exploitation of graphics processing units, has resulted in the surge of new, previously infeasible, algorithmic schemes for structural health monitoring (SHM) tasks, such as the use of convolutional neural networks (CNNs) for vision-based SHM. This work proposes a novel approach for crack recognition in digital images based on coupling of CNNs and suited image processing techniques. The proposed method is applied on a dataset comprising images of the welding joints of a long-span steel bridge, collected via high-resolution consumer-grade digital cameras. The studied dataset includes photos taken in sub-optimal light and exposure conditions, with several noise contamination sources such as handwriting scripts, varying material textures, and, in some cases, under presence of external objects. The reference pixels representing the cracks, together with the crack width and length, are available and used for training and validating the proposed model. Although the proposed framework requires some knowledge of the "damaged areas", it alleviates the need for precise labeling of the cracks in the training dataset. Validation of the model by means of application on an unlabeled image set reveals promising results in terms of accuracy and robustness to noise sources.

One of the most discussed aspects of vibration-based structural health monitoring (SHM) is how to link identified parameters with structural health conditions. To this aim, several damage indexes have been proposed in the relevant literature based on typical assumptions of the operational modal analysis (OMA), such as stationary excitation and unlimited vibration record. Wireless smart sensor networks based on low-power electronic components are becoming increasingly popular among SHM specialists. However, such solutions are not able to deal with long data series due to energy and computational constraints. The decentralization of processing tasks has been shown to mitigate these issues. Nevertheless, traditional damage indicators might not be suitable for onboard computations. In this paper, a robust damage index is proposed based on a damage sensitive feature computed in a decentralized fashion, suitable for smart wireless sensing solutions. The proposed method is tested on a numerical benchmark and on a real case study, namely the S101 bridge in Austria, a prestressed concrete bridge that has been artificially damaged for research purposes. The results obtained show the potential of the proposed method to monitor the conditions of civil infrastructures.

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