Strategies of anomalies detection in bridges and tunnels as a tool for structural health management. New approaches and AI support.

L'abstract è presente nell'allegato / the abstract is in the attachmen

Innovative strategies to preserve the Italian engineering heritage: the historical tunnels.

Italy, a nerve center for Western culture, holds the largest number of artistic and cultural assets declared World Heritage by UNESCO. From the Romans to the present day, an ever-growing infrastructure system, rich in tunnels, bridges and viaducts, has been the expression of a high engineering expertise. For the management of the aforementioned complex infrastructure heritage, the development of automated control and maintenance plans is one of the issues on which the engineering and research community focuses its resources and efforts. In this study, an approach is proposed to automate the process of classifying defects in tunnels using deep learning techniques to protect and maintain the concrete tunnel lining. The acquisition of images from non-destructive monitoring techniques, such as Ground Penetrating Radar, within a supervised learning process allows the creation of an effective tool for the automatic detection of severe defects such as cracks, anomalies, and voids. The obtained results provided for a high degree of accuracy in identifying the tunnels’ structural condition. The use of the developed strategy, based on machine learning and non-invasive inspection techniques, is costeffective for infrastructure managers. Such a procedure reduces both the number of invasive interventions on the tunnel lining and the time and cost associated with employing specialized technicians

AI based bridge health assessment

Starting from the data extracted from a long-term monitoring system installed on a steel bridge, it has been possible to outline the undamaged behaviour of the structure. The structure under monitoring is a steel suspended arch bridge of long span that has been instrumented with several types of sensors, e.g. triaxial accelerometers, load cells and environmental sensors. The records of the measurements during the first period of structural life and the lack of construction problems ensure the good respect of the structural nominal conditions. The accelerometric data stored during this period have been used to extrapolate the dynamic characteristics of the bridge: natural frequencies, damping ratios and modal shapes. The use of a specific stochastic subspace technique (SSI-UPCX), allowed to obtain not only the modal parameters but also their uncertainty. In this way, the range of variation of modal parameters, e.g. affected by environmental factors, has been calculated and a minimum and maximum threshold for each parameter has been determined. Consequently, the assessment and control of structural health is updated and linked to these ranges of variation. In addition, a promising modern approach to tackle the problem is the use of machine learning techniques within the broad field of AI. After the selection/reduction of the parameters that better represent the data, signal detection has been used and the obtained outcomes compared. In the light of both the above approaches, albeit in a different way, it is possible to create a model of the normal operating condition of the structure and consider the deviations from the pattern as an anomaly. The work represents a first step and a benchmark for the wider damage and ageing identification problem to figure out which method is the most appropriate and effective for this specific case of structural assessment, in terms of effort and accuracy

Machine learning approach to the safety assessment of a prestressed concrete railway bridge

Early structural anomalies identification allows to hold maintenance activities that avoid loss of both economic resources and human life. This is extremely important for crucial infrastructures like railway bridges. This paper illustrates the structural health monitoring approach applied to a simply supported prestressed concrete railway bridge. In the framework of long-term monitoring, both static quantities (displacements, strains, and rotations) and environmental measurements (temperatures) have been recorded. Machine learning techniques, Extreme Gradient boosting machine and Multi-Layer Perceptron, have been exploited to build regression correlation models associated with the undamaged structural condition after adequate pre-processing operations. In this way, alarm thresholds based on the expected residuals between the predicted structural quantities and the measured ones, have been defined. The thresholds turned out to be able to catch early-stage anomalies not pointed out by traditional damage thresholds based on the design values. The proposed damage index is chosen as the moving median of the residuals, allowing a significant reduction of false alarms. The used correlation models and the obtained results represent a starting point for the generalization of this approach to the bridges belonging to the same static typology

Epigenetic Variability across Human Populations: A Focus on DNA Methylation Profiles of the KRTCAP3, MAD1L1 and BRSK2 Genes

Natural epigenetic diversity has been suggested as a key mechanism in microevolutionary processes due to its capability to create phenotypic variability within individuals and populations. It constitutes an important reservoir of variation potentially useful for rapid adaptation in response to environmental stimuli. The analysis of population epigenetic structure represents a possible tool to study human adaptation and to identify external factors that are able to naturally shape human DNA methylation variability. The aim of this study is to investigate the dynamics that create epigenetic diversity between and within different human groups. To this end, we first used publicly available epigenome-wide data to explore population-specific DNA methylation changes that occur at macro-geographic scales. Results from this analysis suggest that nutrients, UVA exposure and pathogens load might represent the main environmental factors able to shape DNA methylation profiles. Then, we evaluated DNA methylation of candidate genes (KRTCAP3, MAD1L1, and BRSK2), emerged from the previous analysis, in individuals belonging to different populations from Morocco, Nigeria, Philippines, China, and Italy, but living in the same Italian city. DNA methylation of the BRSK2 gene is significantly different between Moroccans and Nigerians (pairwise t-test: CpG 6 P-value = 5.2*10 (-) (3); CpG 9 P-value = 2.6*10 (-) (3); CpG 10 P-value = 3.1*10 (-) (3); CpG 11 P-value = 2.8*10 (-) (3)). Comprehensively, these results suggest that DNA methylation diversity is a source of variability in human groups at macro and microgeographical scales and that population demographic and adaptive histories, as well as the individual ancestry, actually influence DNA methylation profiles

Destabilisation, aggregation, toxicity and cytosolic mislocalisation of nucleophosmin regions associated with acute myeloid leukemia

Nucleophosmin (NPM1) is a multifunctional protein that is implicated in the pathogenesis of several human malignancies. To gain insight into the role of isolated fragments of NPM1 in its biological activities, we dissected the C-terminal domain (CTD) into its helical fragments. Here we focus the attention on the third helix of the NPM1-CTD in its wild-type (H3 wt) and AML-mutated (H3 mutA and H3 mutE) sequences. Conformational studies, by means of CD and NMR spectroscopies, showed that the H3 wt peptide was partially endowed with an a-helical structure, but the AML-sequences exhibited a lower content of this conformation, particularly the H3 mutA peptide. Thioflavin T assays showed that the H3 mutE and the H3 mutA peptides displayed a significant aggregation propensity that was confirmed by CD and DLS assays. In addition, we found that the H3 mutE and H3 mutA peptides, unlike the H3 wt, were moderately and highly toxic, respectively, when exposed to human neuroblastoma cells. Cellular localization experiments confirmed that the mutated sequences hamper their nucleolar accumulation, and more importantly, that the helical conformation of the H3 region is crucial for such a localization

Destabilisation, aggregation, toxicity and cytosolic mislocalisation of nucleophosmin regions associated with acute myeloid leukemia

Nucleophosmin (NPM1) is a multifunctional protein that is implicated in the pathogenesis of several human malignancies. To gain insight into the role of isolated fragments of NPM1 in its biological activities, we dissected the C-terminal domain (CTD) into its helical fragments. Here we focus the attention on the third helix of the NPM1-CTD in its wild-type (H3 wt) and AML-mutated (H3 mutA and H3 mutE) sequences. Conformational studies, by means of CD and NMR spectroscopies, showed that the H3 wt peptide was partially endowed with an a-helical structure, but the AML-sequences exhibited a lower content of this conformation, particularly the H3 mutA peptide. Thioflavin T assays showed that the H3 mutE and the H3 mutA peptides displayed a significant aggregation propensity that was confirmed by CD and DLS assays. In addition, we found that the H3 mutE and H3 mutA peptides, unlike the H3 wt, were moderately and highly toxic, respectively, when exposed to human neuroblastoma cells. Cellular localization experiments confirmed that the mutated sequences hamper their nucleolar accumulation, and more importantly, that the helical conformation of the H3 region is crucial for such a localization

Advanced deep learning comparisons for non-invasive tunnel lining assessment from ground penetrating radar profiles

Innovative, automated, and non-invasive techniques have been developed by scientific community to indirectly assess structural conditions and support the decision-making process for a worthwhile maintenance schedule. Nowadays, machine learning tools are in the spotlight because of their outstanding capabilities to deal with data coming from even heterogeneous sources and their ability to extract information from the structural systems, providing highly effective, reliable, and efficient damage classification tools. In the current study, a supervised multi-level damage classification strategy has been developed regarding Ground Penetrating Radar (GPR) profiles for the assessment of tunnel lining conditions. In previous research, the authors firstly considered a convolutional neural network (CNN), adopting the quite popular ResNet-50, initialized through transfer learning. In the present work, further enhancements have been attempted by adopting two configurations of the newest state-of-art advanced neural architectures: the neural transformers. The foremost is the original Vision Transformer (ViT), whose core is an encoder entirely based on the innovative self-attention mechanism and does not rely on convolution at all. The second is an improvement of ViT which merges convolution and self-attention, the Compact Convolution Transformer (CCT). In conclusion, a critical discussion of the different pros and cons of adopting the above-mentioned different architectures is finally provided, highlighting the actual powerfulness of these technologies in the future civil engineering paradigm nevertheless

Post COVID-19 irritable bowel syndrome

Objectives: The long-term consequences of COVID-19 infection on the gastrointestinal tract remain unclear. Here, we aimed to evaluate the prevalence of gastrointestinal symptoms and post-COVID-19 disorders of gut-brain interaction after hospitalisation for SARS-CoV-2 infection. Design: GI-COVID-19 is a prospective, multicentre, controlled study. Patients with and without COVID-19 diagnosis were evaluated on hospital admission and after 1, 6 and 12 months post hospitalisation. Gastrointestinal symptoms, anxiety and depression were assessed using validated questionnaires. Results: The study included 2183 hospitalised patients. The primary analysis included a total of 883 patients (614 patients with COVID-19 and 269 controls) due to the exclusion of patients with pre-existing gastrointestinal symptoms and/or surgery. At enrolment, gastrointestinal symptoms were more frequent among patients with COVID-19 than in the control group (59.3% vs 39.7%, p<0.001). At the 12-month follow-up, constipation and hard stools were significantly more prevalent in controls than in patients with COVID-19 (16% vs 9.6%, p=0.019 and 17.7% vs 10.9%, p=0.011, respectively). Compared with controls, patients with COVID-19 reported higher rates of irritable bowel syndrome (IBS) according to Rome IV criteria: 0.5% versus 3.2%, p=0.045. Factors significantly associated with IBS diagnosis included history of allergies, chronic intake of proton pump inhibitors and presence of dyspnoea. At the 6-month follow-up, the rate of patients with COVID-19 fulfilling the criteria for depression was higher than among controls. Conclusion: Compared with controls, hospitalised patients with COVID-19 had fewer problems of constipation and hard stools at 12 months after acute infection. Patients with COVID-19 had significantly higher rates of IBS than controls. Trial registration number: NCT04691895