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Structural reliability analysis aims at assessing the safety of structures which often operate under uncertain conditions. Approximation-, simulation- or surrogate-based methods are used in this context to estimate the failure probability. Surrogate-based methods are the least computationally intensive and consist in building a cheaper proxy of the original limit-state function, which is calibrated using a limited set of samples known as the experimental design. The latter is sequentially enriched to increase the accuracy of the surrogate in areas of interest, hence allowing for an accurate estimation of the failure probability. A large number of such techniques has been recently developed in the literature (e.g., active Kriging - Monte Carlo simulation). However most of these techniques consider a single limit-state function. These methods lose efficiency when used to solve system reliability problems, where failure is defined by a non-trivial combination of multiple limit-states. This is due to some peculiarities of the system problem such as the presence of disjoint failure domains or the uneven contribution of each limit-state to the overall failure. In this work, we propose an efficient algorithm combining Kriging/PC-Kriging and subset simulation to solve system reliability problems in their most general setting, i.e., with an arbitrary combination of components. We devise a new learning function which first identifies candidate samples and then selects for enrichment the specific limit-state that contributes the most to system failure. The algorithm is validated on a set of analytical functions and compared with existing methods in the literature., RSUQ-Report, 2022-008

The safety of structures is generally assessed using structural reliability analysis within a probabilistic framework. The parameters describing the system, which are affected by uncertainties, are represented by random variables and a set of so-called limit-state functions determine whether the system fails. The goal of the analysis is then to estimate the probability of failure of the system. Many techniques have been developed to achieve this goal and can be classified into approximation-, simulation- and surrogate-based methods. While approximation methods often lack of accuracy, simulation-based methods are computationally expensive. Surrogate-based methods, on the other hand, are an efficient alternative, especially when used in the so-called active learning framework. In active learning reliability, an inexpensive proxy of the limit-state function is built by sparsely evaluating the original limit-state function. This results in an experimental design that can be iteratively enriched so as to improve the accuracy of the approximated limit-state surface. Combined with an appropriate reliability estimation algorithm, this ultimately leads to accurately estimating the failure probability within a moderate computational cost. This idea has been largely investigated in the recent years and a plethora of active learning methods has been proposed in the literature as shown in recent reviews [1,2]. However, most of these contributions are limited to component reliability problems, i.e. when there is only a single limit-state function. System reliability, where there are multiple limit-state functions combined in a non-trivial way, is indeed more complex to solve. This is due to the presence of multiple (possibly disjoint) failure domains and their uneven contribution to system failure. In this work, we propose an active learning reliability method for system reliability problems. We devise a framework combining PC-Kriging and subset simulation. We make use of density-based clustering and Sobol’ sensitivity analysis to identify the most important points to add to the experimental design and the most relevant limit-states to refine. Furthermore, the proposed method is cost-sensitive, i.e., it factors in the relative cost of evaluating each limit-state function. The algorithm is illustrated and validated on a set of analytical functions and eventually applied to a network of transmission towers. References: [1] M Moustapha, S Marelli, and B Sudret. A generalized framework for active learning reliability: survey and benchmark. Structural Safety , 2022 (In press). [2] R. Teixeira, M. Nogal, and A. O’Connor. Adaptive approaches in metamodel-based reliability analysis: A review. Structural Safety, 89:102019, 2021.

A major problem in assessing the likelihood of survival of patients with hepatocellular carcinoma (HCC) arises from a lack of models capable of predicting outcome accurately.To compare the ability of the Italian score (CLIP), the French classification (GRETCH) and the Barcelona (BCLC) staging system in predicting survival in patients with HCC.We included 406 consecutive patients with cirrhosis and HCC. Seventy-eight per cent of patients had hepatitis C. Independent predictors of survival were identified using the Cox model.One-hundred and seventy-eight patients were treated, while 228 were untreated. The observed mortality was 60.1% in treated patients and 84.9% in untreated patients. Among treated patients, albumin, bilirubin and performance status were the only independent variables significantly associated with survival. Mortality was independently predicted by bilirubin, alpha-fetoprotein and portal vein thrombosis in untreated patients. CLIP achieved the best discriminative capacity in the entire HCC cohort and in the advanced untreatable cases, while BCLC was the ablest in predicting survival in treated patients.Overall predictive ability of BCLC, CLIP and GRETCH staging systems was not satisfactory, and was not uniform for treated patients and untreated patients. None of the scoring systems provided confident prediction of survival in individual patients.

PERCUTANEOUS RADIOFREQUENCY THERMAL ABLATION 8 RFTA) OF SMALL HEPATOCELLUALR CARCINOMA: A PROSPECTIVE STUDY Data: 2004 Dettaglio tipologia d'Ateneo: 3a - Articoli su riviste ISI (anche on line)

The aetiological pattern of hepatocellular carcinoma ( HCC) in Italy is changing.

Radiofrequency thermoablation is a locoregional procedure based on the use of electromagnetic waves that induce movement and consequently the production of heat. This is the basis for the coagulative necrosis produced in the tissues. The method, experimented with and developed in the '90s, is today extensively used in the treatment of focal hepatic lesions, both primary and secondary. The factors that condition the procedure are related essentially to the characteristics of the tumours and to the possibility of obtaining a predictable area of necrosis that guarantees the radicality of the treatment. As regards primary neoplasms, it should be stressed that the treatment for hepatocellular carcinoma is implemented only in a small percentage of cases due to both the stage of the cirrhosis and because a wide resection is not justified for small-sized lesions or for recurrences after resection. As far as liver metastases are concerned, particularly those from the colon-rectum, surgery is becoming increasingly indicated. Obviously the conditions are decidedly less demanding than those of surgery in liver parenchyma with cirrhosis. Radiofrequency thermal ablation, even in this condition, has a place as an alternative or in combination with liver resection, particularly during recurrences or in cases of multiple metastases that require extensive extirpative operations. The authors present their experience over the period from February 1999 to December 2002 in 82 patients, 71 with hepatocellular carcinoma and 11 with metastases, treated with radiofrequency thermoablation. Depending upon the site, the number of lesions and the Child-Pugh functional classes in patients with cirrhosis, 117 treatments were carried out: the percutaneous approach was used in 77 cases and the open procedure in 36, while the thermoablation was combined with surgical resection in 4 cases. The results are examined on the basis of perioperative morbidity and mortality and local recurrence, and the time of hospitalisation is compared in the different patient groups. The authors conclude favourably for this procedure, which is associated with an extremely low percentage of complications and with an almost total lack of perioperative mortality, and in some cases may be a valid alternative and in other cases a useful complement to resection treatment.