Journal articles on the topic 'Ak-mcs'

The aim of this article is to study the reliability analysis, parametric reliability sensitivity analysis and global reliability sensitivity analysis of structures with extremely rare failure events. First, the global reliability sensitivity indices are restudied, and we show that the total effect index can also be interpreted as the effect of randomly copying each individual input variable on the failure surface. Second, a new method, denoted as Active learning Kriging Markov Chain Monte Carlo (AK-MCMC), is developed for adaptively approximating the failure surface with active learning Kriging surrogate model as well as dynamically updated Monte Carlo or Markov chain Monte Carlo populations. Third, the AK-MCMC procedure combined with the quasi-optimal importance sampling procedure is extended for estimating the failure probability and the parametric reliability sensitivity and global reliability sensitivity indices. For estimating the global reliability sensitivity indices, two new importance sampling estimators are derived. The AK-MCMC procedure can be regarded as a combination of the classical Monte Carlo Simulation (AK-MCS) and subset simulation procedures, but it is much more effective when applied to extremely rare failure events. Results of test examples show that the proposed method can accurately and robustly estimate the extremely small failure probability (e.g. 1e–9) as well as the related parametric reliability sensitivity and global reliability sensitivity indices with several dozens of function calls.

L’estimation des déplacements dus à la réalisation de tunnels est un sujet important pour les projets des travaux souterrains en sites urbains. Ces déplacements, plus particulièrement les tassements et gonflements, sont souvent l’origine d’endommagements pour les constructions avoisinantes et les ouvrages en cours de construction. Cet article présente le cadre d’une approche probabiliste à l’aide d’un modèle aux éléments finis permettant d’estimer ces déplacements. Cette approche permet de prendre en compte les incertitudes liées aux problèmes géotechniques (manque de données d’entrée, variabilité spatiale des sols, etc.). Une méthode pour la vérification probabiliste des déplacements est tout d’abord définie à l’aide du calcul de la probabilité de défaillance Pf (ou de l’indice de fiabilité β) en fonction des niveaux cibles de sécurité ou performance. Elle est ensuite appliquée à un tunnel réalisé en méthode conventionnelle grâce au couplage des outils ZSOIL et UQLab. Les évaluations de fiabilité (probabilité de défaillance et indice de fiabilité) de la fonction de sécurité basées sur les résultats des calculs aux éléments finis ont été réalisées avec succès à l’aide des méthodes FORM, AK-MCS et MCS. Cette approche probabiliste illustre l’évaluation de la fiabilité aux cas ELS selon les Eurocodes.

Abstract Introduction: Multiple myeloma (MM) relapse is common and may eventually lead to highly refractory/relapsed MM (RRMM). Therefore, novel treatment combinations are crucially needed for this highly challenging subgroup of patients (pts). The aim of the here presented phase I/II IIT was to test the tolerability and activity of a novel, so-called VBDD schedule within an outpatient regimen for extensively pretreated RRMM pts. In addition to Bortezomib, Doxorubicin and Dexamethason, which are all active and approved drugs in RRMM, Vorinostat has shown promising anti-tumor effects as a histone deacetylase inhibitor (HDACi). It inhibits the enzyme activity of HDAC 1, 2, 3 and 6, thereby allowing the activation of tumor suppressor genes. MM cells have been shown to escape from bortezomib treatment by formation of aggrosomes which desensitize cells to proteasome inhibitors and by microtubule mediated protein shuttling to lysosomes, where proteins are degradaded in order to prevent cytotoxic stress and ultimately escape from apoptosis (Fig.A). Albeit vorinostat has shown moderate activity when given alone, it has promising additive effects when combined with other antimyeloma agents, and was therefore used as an add-on agent within this RRMM regimen as it blocks microtubule coppling and aggrosome building and thereby may antagonize escape mechanisms in bortezomib-refractory pts. Methods: Vorinostat was escalated from 100mg (dose level 0), to 200mg (+1) and 300mg (+2). Primary objectives (MTD; 3+3 dose escalation), secondary objectives (safety, IMWG responses, PFS, OS) and supplementary endpoints (organ function, prognostic factors, QoL, comorbidity and HDAC-activity in PBMCs/BM) were assessed throughout the trial. Dose limiting toxicities (DLTs) were defined as any possibly drug-related adverse event (AEs) ≥grade 3 (CTCAE) during the 1st cycle. After completing 6 cycles, patients could receive either a bortezomib maintenance therapy or next-line treatment (e.g. 2nd ASCT). Results: 34 pts with RRMM with a median age of 63 years (47-78) and KPS of 90% (70-100%) have been enrolled, of which 33 received therapy according to the study protocol (1 pt deceased prior to study start due to aggressive MM progression and was therefore not included in the evaluation). The number of prior therapy lines was substantial with a median of 3 (1-8; with prior bortezomib, SCT and IMiDs in 88%, 94% and 42%, respectively). 3 pts each were treated in dose levels 0 and +1, and the remaining 27 pts in dose level +2. No DLTs were observed. In our current analysis, SAEs amounted to 15 and occurred in 9/33 pts (27%): Amongst them, 2 nonfatal SAEs were judged to be related to the investigational therapy (1 bacteraemia, 1 herpes zoster), for the others, no causal relationship to VBDD was found. The response according to IMWG criteria was rewarding with best ORR (>PR) and clinical benefit rate (CBR; >SD) of 42% and 94% (Fig. B), and end of treatment (EoT) ORREoT and CBREoT of 36% and 88%, respectively (Fig.C). Our data also revealed that the response was independent of the presence or absence of unfavorable cytogenetics. Comorbidity assessments assured no decline in pts' mental or physical condition and pts reported preserved or improved QoL with this well-tolerated 4-agent treatment regimen. Pharmacodynamic analyses in peripheral blood (PB) MCs showed substantial and early HDAC downregulation between VBDD cycles 1 and 2 in 11/16 pts (69%): median HDAC activity decreased to 52% of pre-treatment levels. Thereby, we were able to distinguish 3 groups of pts with substantial, more subtle or no PB HDAC decreases in 8, 3 and 5 pts, respectively. Of note, these HDAC changes correlated well with pts' serological and clinical responses, except in 2 pts. These intriguing results are currently further assessed and will be presented at the meeting. Conclusions: VBDD demonstrated to be an effective and well-tolerated outpatient regimen with promising response rates in heavily pretreated RRMM pts. The employed VBDD regimen, with a continuous, rather than pulsed vorinostat-schedule, constitutes a promising treatment option, expands current standard therapies and, similarly to other HDACi (i.e. panobinostat), suggests HDACi as a valuable add-on within this combination schedule in order to stabilize the disease and/or bridge RRMM patients to next-line treatments (i.e. autologous/allogenic stem cell transplantation) or novel clinical trial drugs. *AK and JW contributed equally Figure 1. Figure 1. Disclosures Off Label Use: We report on results of an Phase I/II IIT, in which the HDACi Vorinostat is used to treat relapsed or refractory Multiple Myeloma pts . Engelhardt:MSD: Research Funding; Janssen-Cilag: Research Funding; Comprehensive Cancer Center Freiburg: Research Funding; German Cancer Aid: Research Funding. Wäsch:German Cancer Aid: Research Funding; Janssen-Cilag: Research Funding; Comprehensiv Cancer Center Freiburg: Research Funding; MSD: Research Funding.

Meta-models or surrogate models are convenient tools for reliability assessment of problems with time-consuming numerical models. Recently, an adaptive method called AK-MCS has been widely used for reliability analysis by combining Mont-Carlo simulation method and Kriging surrogate model. The AK-MCS method usually uses constant regression as a Kriging trend. However, other regression trends may have better performance for some problems. So, a method is proposed by combining multiple Kriging meta-models with various trends. The proposed method is based on the maximum entropy of predictions to select training samples. Using multiple Kriging models can reduce the sensitivity to the regression trend. So, the propped method can have better performance for different problems. The proposed method is applied to some examples to show its efficiency.

Time-dependent reliability analysis requires the use of the extreme value of a response. The extreme value function is usually highly nonlinear, and traditional reliability methods, such as the first order reliability method (FORM), may produce large errors. The solution to this problem is using a surrogate model of the extreme response. The objective of this work is to improve the efficiency of building such a surrogate model. A mixed efficient global optimization (m-EGO) method is proposed. Different from the current EGO method, which draws samples of random variables and time independently, the m-EGO method draws samples for the two types of samples simultaneously. The m-EGO method employs the adaptive Kriging–Monte Carlo simulation (AK–MCS) so that high accuracy is also achieved. Then, Monte Carlo simulation (MCS) is applied to calculate the time-dependent reliability based on the surrogate model. Good accuracy and efficiency of the m-EGO method are demonstrated by three examples.

A significant challenge of surrogate model-based structural reliability analysis (SRA) is to construct an accurate approximated model of the nonlinear limit state function (LSF) with high order and high dimension effectively. As one of the sequential update-strategies of design of experiment (DoE), the active learning method is more attractive in recent years due to greatly reduces the burden of reliability analysis. Although the active learning method based on information entropy learning function H and the line simulation (AK-LS) is a powerful tool of SRA, the computational burden from the iterative algorithm is still large during the learning process. In this research, an improved learning criterion, named the weight information entropy function (WH), is developed to update the DoE of Kriging-based reliability analysis. The WH learning function consists of the information entropy function and an adaptive weight function (W). Locations in the variable space and probability densities of the samples are taken accounted into the WH learning function, which is the most important difference from the H learning function. The samples that are closer to the LSF and has a greater probability density can be preferentially selected into the DoE comparing to others by changing the weight of information entropy during the learning process. The WH learning function can efficiently match the limit state function in an important domain rather than the entire variable space. Consequently, the approximated model of LSF via Kriging interpolation can be constructed more effectively. The new active learning method is developed based on Kriging model, in which WH learning function and Monte Carlo simulation (MCS) are employed. Finally, several engineering examples with high non-linearity are analyzed. Results shown that the new method are very efficient when dealing with intractable problems of SRA.