Efficient two-step procedure for parameter identification and uncertainty assessment in model updating problems

The model updating procedures employed in vibration-based health monitoring need to be reliable and computationally efficient. The computational time is a fundamental task if the results are used to evaluate, in quasi-real-time, the safe or the unsafe state of strategic and relevant structures. The paper presents an efficient two-step procedure for the identification of the mechanical parameters and for the assessment of the corresponding uncertainty in model updating problems. The first step solves a least squares problem, providing a first estimate of the unknown parameters. The second (iterative) step produces a refinement of the solution. Moreover, by exploiting the error propagation theory, this article presents a direct (non-iterative) procedure to assess the uncertainty affecting the unknown parameters starting from the experimental data covariance matrix. To test the reliability of the procedure as well as to prove its applicability to structural problems, the methodology has been applied to two test-bed case studies. Finally, the procedure has been used for the damage assessment in an existing building. The results provided in this article indicate that the procedure can accurately identify the unknown parameters and properly localize and quantify the damage

Can the plasma PD-1 levels predict the presence and efficiency of tumor-infiltrating lymphocytes in patients with metastatic melanoma?

Background: The immune response in melanoma patients is locally affected by presence of tumor-infiltrating lymphocytes (TILs), generally divided into brisk, nonbrisk, and absent. Several studies have shown that a greater presence of TILs, especially brisk, in primary melanoma is associated with a better prognosis and higher survival rate. Patients and Methods: We investigated by enzyme-linked immunosorbent assay (ELISA) the correlation between PD-1 levels in plasma and the presence/absence of TILs in 28 patients with metastatic melanoma. Results: Low plasma PD-1 levels were correlated with brisk TILs in primary melanoma, whereas intermediate values correlated with the nonbrisk TILs, and high PD-1 levels with absent TILs. Although the low number of samples did not allow us to obtain a statistically significant correlation between the plasma PD-1 levels and the patients' overall survival depending on the absence/presence of TILs, the median survival of patients having brisk type TILs was 5 months higher than that of patients with absent and nonbrisk TILs. Conclusions: This work highlights the ability of measuring the plasma PD-1 levels in order to predict the prognosis of patients with untreated metastatic melanoma without a BRAF mutation at the time of diagnosis

Parametric and numerical modeling tools to forecast hydrogeological impacts of a tunnel

The project of interest involving a hydroelectrical diversion tunnel through a crystalline rock massif in the Alps required a detailed hydrogeological study to forecast the magnitude of water inflows within the tunnel and possible effects on groundwater flow The tunnel exhibits a length of 9.5 km and is located on the right side of the Toce River in Crevoladossola (Verbania Province, Piedmont region, northern Italy). Under the geological framework of the Alps, the tunnel is located within the Lower Penninic Frappes in the footwall of the Simplon Normal Fault, and the geological succession is mostly represented by Antigorio gneiss (metagranites) and Baceno metasediments (metacarbonates). Due to the presence of important mineralized springs for commercial mineral water purposes, the above mentioned hydrogeological study focused on both quantity and quality aspects via rainfall data analysis, monitoring of major spring flow rates, monitoring of hydraulic heads and pumping rates of existing wells/boreholes, hydrochemical and isotopic analysis of springs and boreholes and hydraulic tests (Lefranc and Lugeon). The resulting conceptual model indicated dominant low-permeability (aquitard) behavior of the gneissic rock masses, except under conditions of intense fracturing due to tectonization, and aquifer behavior of the metasedimentary rocks, particularly when interested by dissolution. Groundwater flow systems are mainly controlled by gravity. The springs located near the Toce River were characterized by high mineralization and isotopic ratios, indicating long groundwater flow paths. Based on all the data collected and analyzed, two parametric methods were applied: 1) the Dematteis method, slightly adapted to the case study and the available data, which allows assessment of both potential inflows within the tunnel and potential impacts on springs (codified as the drawdown hazard index; DHI); 2) the Cesano method, which only allow assessment of potential inflows within the tunnel, thereby discriminating between major and minor inflows. Contemporarily, a groundwater flow model was implemented with the equivalent porous medium (EPM) approach in MODFLOW-2000. This model was calibrated under steady-state conditions against the available data (groundwater levels inside wells/piezometers and elevation and flow rate of springs). The Dematteis method was demonstrated to be more reliable and suitable for the site than was the Cesano method. This method was validated considering a tunnel through gneissic rock masses, and this approach considered intrinsic parameters of rock masses more notably than morphological and geomorphological factors were considered. The Cesano method relatively overestimated tunnel inflows, considering variations in the topography and overburden above the tunnel. Sensitivity analysis revealed a low sensitivity of these parametric methods to parameter values, except for the rock quality designation (RQD) employed to represent the fracturing degree. The numerical model was calibrated under ante-operam conditions, and sensitivity analysis evaluated the influence of uncertainties in the hydraulic conductivity (K) values of the different hydrogeological units.The hydraulic head distribution after tunnel excavation was forecasted considering three scenarios, namely, a draining tunnel, tunnel as a eater loss source, and tunnel sealed along its aquifer sectors, considering 3 levels of K reduction. Tunnel impermeabilization was very effective, thus lowering the drainage rate and impact on springs. The model quantitatively defined tunnel inflows and the effects on spring flow at the surface in terms of flow rate decrease. The Dematteis method and numerical model were combined to obtain a final risk of impact on the springs. This study likely overestimated the risk because all the values assigned to the parameters were chosen in a conservative way, and the steady-state numerical simulations were also very conservative (the transient state in this hydrogeological setting supposedly lasts 1-3 years). Monitoring of the tunnel and springs during tunnel boring could facilitate the feedback process

Parameter estimation and uncertainty quantification of a fiber-reinforced concrete model by means of a multi-level Bayesian approach

The paper presents a procedure for the stochastic calibration of a cracked hinge model on the basis of an extensive experimental campaign performed on a large group of nominally identical fiber-reinforced specimens. The calibration is carried out in a multi-level Bayesian framework that allows to quantify and separate several uncertainty contributions affecting model parameters. Indeed, the variability in the experimental response for nominally identical specimens due to the material heterogeneity represents a significant uncertainty contribution as well as model error. The former can be quantified at the hyper-parameter level of the multi-level framework. The presented results highlight the good agreement of the numerical predictions with the experimental data and the superior performance of the multi-level framework compared to that of the classical single-level framework. We also perform analyses to explore the impact of the prior parameter model conditioned on hyper-parameters and assess the minimum number of specimen datasets needed to quantify the inherent variability of model parameters

A statistical approach for modeling individual vertical walking forces

This paper proposes a statistical approach for modeling vertical walking forces induced by single pedestrians. To account for the random nature of human walking, the individual vertical walking force is modeled as a series of steps and the gait parameters are assumed to vary at each step. Walking parameters are statistically calibrated with respect to the results of experimental tests performed with a force plate system. Results showed that the walking parameters change during walking and are correlated with each other. The force model proposed in this paper is a step-by-step model based on the description of the multivariate distribution of the walking features through a Gaussian Mixture model. The performance of the proposed model is compared to that of a simplified load model and of two force models proposed in the literature in a numerical case study. Results demonstrate the importance of an accurate modeling of both the single step force and the variability of the individual walking force

Surrogate-based bayesian model updating of a historical masonry tower

This paper presents the surrogate-based Bayesian model updating of a historical masonry bell tower. The finite element model of the structure is updated on the basis of the modal properties experimentally identified thanks to a vibration test. In a general context, model updating results are highly affected by several uncertainties, regarding both the experimental measures and the model. Stochastic approaches to model updating, as the one based on Bayes' theorem, enable to quantify the uncertainties associated to the updated parameters and, consequently, to increase the reliability of the identification. The major drawback of Bayesian model updating is the high computational effort requested to compute the posterior distribution of parameters. For this reason, the paper proposes to integrate the classical procedure with a surrogate model. A Gaussian surrogate is employed for the approximation of the posterior distribution of parameters and the performances of the proposed method are compared to those of an Bayesian numerical method proposed in literature

Raveguard: A noise monitoring platform using low-end microphones and machine learning

Urban noise is one of the most serious and underestimated environmental problems. According to the World Health Organization, noise pollution from traffic and other human activities, negatively impact the population health and life quality. Monitoring noise usually requires the use of professional and expensive instruments, called phonometers, able to accurately measure sound pressure levels. In many cases, phonometers are human-operated; therefore, periodic fine-granularity city-wide measurements are expensive. Recent advances in the Internet of Things (IoT) offer a window of opportunities for low-cost autonomous sound pressure meters. Such devices and platforms could enable fine time\u2013space noise measurements throughout a city. Unfortunately, low-cost sound pressure sensors are inaccurate when compared with phonometers, experiencing a high variability in the measurements. In this paper, we present RaveGuard, an unmanned noise monitoring platform that exploits artificial intelligence strategies to improve the accuracy of low-cost devices. RaveGuard was initially deployed together with a professional phonometer for over two months in downtown Bologna, Italy, with the aim of collecting a large amount of precise noise pollution samples. The resulting datasets have been instrumental in designing InspectNoise, a library that can be exploited by IoT platforms, without the need of expensive phonometers, but obtaining a similar precision. In particular, we have applied supervised learning algorithms (adequately trained with our datasets) to reduce the accuracy gap between the professional phonometer and an IoT platform equipped with low-end devices and sensors. Results show that RaveGuard, combined with the InspectNoise library, achieves a 2.24% relative error compared to professional instruments, thus enabling low-cost unmanned city-wide noise monitoring