Sampling-Based RBDO Using Probabilistic Sensitivity Analysis and Virtual Support Vector Machine

An optimization method for learning statistical classifiers in structural reliability

Monte Carlo Simulation and Response Surface Method are two very powerful reliability analysis methods. Normally, in the reliability analysis of complex structures, the limit state function often can not be expressed in a closed-form. Usually, the codes for probabilistic analysis need to be combined with finite element models. ANSYS Probabilistic Design System (PDS) has provided a package to conduct probabilistic analysis automatically. This paper is going to compare the performance of these methods through an easy engineering problem in ANSYS. The results are going to be derived to show the feature of applying the corresponding reliability methods.

The Support Vector Machine (SVM) response surface method (RSM) is proposed on fast Markov chain simulation for the problem with implicit limit state function usually encountered in engineering reliability analysis and design. In the proposed method, Markov chain is used to generate the samples in the important region of the limit state function, and the SVM is employed to construct the response surface by use of these samples. Since Markov chain can adaptively simulate the samples in the important region, and the candidate state but not Markov state is used as the training samples for SVM, the proposed method can well approximate the limit state equation in the zone surrounding the design point, and can make full use of information provided by Markov chain simulation. In addition, the iterative strategy is adopted to improve the convergence speed of the failure probability. Moreover, the proposed method uses the SVM regression method to construct the response surface, which can automatically apply the Structural Risk Minimization (SRM) inductive principle in approximating the limit state equation, thus it can approximate the failure probability with high accuracy. Finally applications in a numerical example and an engineering example indicate that the proposed method owns good performance in calculating efficiency and accuracy.

Traditional reliability-based design optimization(RBDO)is either computational intensive or not accurate enough.In this work,a new RBDO method based on Support Vector Machines(SVM)is proposed.For reliability analysis,SVM is used to create a surrogate model of the limit-state function at the Most Probable Point(MPP).The uniqueness of the new method is the use of the gradient of the limit-state function at the MPP.This guarantees that the surrogate model not only passes through the MPP but also is tangent to the limit-state function at the MPP.Then Importance Sampling(IS)is used to calculate the probability of failure based on the surrogate model.This treatment significantly improves the accuracy of reliability analysis.For optimization,the Sequential Optimization and Reliability Assessment(SORA)is employed,which decouples deterministic optimization from the SVM reliability analysis.The decoupling makes RBDO more efficient.The two examples show that the new method is more accurate with a moderately increased computational cost.

Reliability analysis of nonlinear structural systems using response surface method

Digital therapeutic care (DTC) programs are unsupervised app-based treatments that provide video exercises and educational material to patients with nonspecific low back pain during episodes of pain and functional disability. German statutory health insurance can reimburse DTC programs since 2019, but evidence on efficacy and reasonable pricing remains scarce. This paper presents a probabilistic sensitivity analysis (PSA) to evaluate the efficacy and cost-utility of a DTC app against treatment as usual (TAU) in Germany. The aim of this study was to perform a PSA in the form of a Monte Carlo simulation based on the deterministic base case analysis to account for model assumptions and parameter uncertainty. We also intend to explore to what extent the results in this probabilistic analysis differ from the results in the base case analysis and to what extent a shortage of outcome data concerning quality-of-life (QoL) metrics impacts the overall results. The PSA builds upon a state-transition Markov chain with a 4-week cycle length over a model time horizon of 3 years from a recently published deterministic cost-utility analysis. A Monte Carlo simulation with 10,000 iterations and a cohort size of 10,000 was employed to evaluate the cost-utility from a societal perspective. Quality-adjusted life years (QALYs) were derived from Veterans RAND 6-Dimension (VR-6D) and Short-Form 6-Dimension (SF-6D) single utility scores. Finally, we also simulated reducing the price for a 3-month app prescription to analyze at which price threshold DTC would result in being the dominant strategy over TAU in Germany. The Monte Carlo simulation yielded on average a €135.97 (a currency exchange rate of EUR €1=US $1.069 is applicable) incremental cost and 0.004 incremental QALYs per person and year for the unsupervised DTC app strategy compared to in-person physiotherapy in Germany. The corresponding incremental cost-utility ratio (ICUR) amounts to an additional €34,315.19 per additional QALY. DTC yielded more QALYs in 54.96% of the iterations. DTC dominates TAU in 24.04% of the iterations for QALYs. Reducing the app price in the simulation from currently €239.96 to €164.61 for a 3-month prescription could yield a negative ICUR and thus make DTC the dominant strategy, even though the estimated probability of DTC being more effective than TAU is only 54.96%. Decision-makers should be cautious when considering the reimbursement of DTC apps since no significant treatment effect was found, and the probability of cost-effectiveness remains below 60% even for an infinite willingness-to-pay threshold. More app-based studies involving the utilization of QoL outcome parameters are urgently needed to account for the low and limited precision of the available QoL input parameters, which are crucial to making profound recommendations concerning the cost-utility of novel apps.

We introduce two semi-supervised models for the classification of remote sensing image data. The models are built upon the framework of Virtual Support Vector Machines (VSVM). Generally, VSVM follow a two-step learning procedure: A Support Vector Machines (SVM) model is learned to determine and extract labeled samples that constitute the decision boundary with the maximum margin between thematic classes, i.e., the Support Vectors (SVs). The SVs govern the creation of so-called virtual samples. This is done by modifying, i.e., perturbing, the image features to which a decision boundary needs to be invariant. Subsequently, the classification model is learned for a second time by using the newly created virtual samples in addition to the SVs to eventually find a new optimal decision boundary. Here, we extend this concept by (i) integrating a constrained set of semi-labeled samples when establishing the final model. Thereby, the model constrainment, i.e., the selection mechanism for including solely informative semi-labeled samples, is built upon a self-learning procedure composed of two active learning heuristics. Additionally, (ii) we consecutively deploy semi-labeled samples for the creation of semi-labeled virtual samples by modifying the image features of semi-labeled samples that have become semi-labeled SVs after an initial model run. We present experimental results from classifying two multispectral data sets with a sub-meter geometric resolution. The proposed semi-supervised VSVM models exhibit the most favorable performance compared to related SVM and VSVM-based approaches, as well as (semi-)supervised CNNs, in situations with a very limited amount of available prior knowledge, i.e., labeled samples.

Monte-Carlo Simulation (MCS) is a powerful tool in solving reliability problems. However, it is time-consuming use for the complex structural engineering problems. Another commonly used method, First-Order Second Moment Method (FOSM) usually requires the values and derivatives of limit state function. This paper presents two types of Least Square Support Vector Machine (LS-SVM) based reliability analysis methods, i.e. LS-SVM-based MCS and LS-SVM-based FOSM. In the first method, LS-SVM is adopted to replace the limit state function and enhance the efficiency of computing. In the second method, LS-SVM is adopted to approximate the limit state function and its partial derivatives which FOSM requires. Thus, based on the LS-SVM, both methods are substantially improved in efficiency. To assess the validity of this methodology, three structural examples are studied and discussed. The results prove that the LS-SVM based new methods are effective in structural reliability analysis problems involving the implicit limit state function.

A US-Wide Red Blood Cell Alloantibody Exchange Can Decrease Mortality and Produce Cost Savings in the Care of Alloimmunized Patients with Sickle Cell Disease

Cost-Effectiveness of Rapid Hemosil Versus in-House and Send-out ADAMTS13 Activity Testing in the Care of Patients with Immune Thrombotic Thrombocytopenic Purpura

Full-thickness injuries to the hand require durable soft tissue coverage to preserve tendon gliding and hand motion. We aim to investigate the cost effectiveness of hand resurfacing comparing free fascial flap reconstruction versus bilaminate synthetic dermal matrices. Cost effectiveness was modeled using decision tree analysis with the rollback method. Total active range of motion was modeled as the common outcome variable based on systematic literature review. Costing was performed from a payer perspective using national Medicare reimbursements. The willingness to pay threshold was determined by average worker's compensation for hand disability. Probabilistic sensitivity analysis was conducted for range of motion outcomes and the costs using 10,000 Monte Carlo simulations. The average cost of free fascial flap reconstruction was $14,201.24 compared with $13,674.20 for Integra, yielding an incremental cost difference of $527.04. Incremental range of motion improvement was 18.0 degrees with free fascial flaps, yielding an incremental cost effectiveness ratio of $29.30/degree of motion. Assuming willingness to pay thresholds of $557.00/degree of motion, free-fascial flaps were highly cost effective. On probabilistic sensitivity analysis, free fascial flaps were dominant in 25.5% of simulations and cost effective in 32.1% of simulations. Thus, microsurgical reconstruction was the economically sound technique in 57.5% of scenarios. Free fascial flap reconstruction of complex hand wounds was marginally more expensive than synthetic dermal matrix and yielded incrementally better outcomes. Both dermal matrix and microsurgical techniques were cost effective in the base case and in sensitivity analysis. In choosing between dermal matrix and microsurgical reconstruction of complex hand wounds, neither technique has a clear economic advantage.

PNS212 Applying a Normal Distribution to Costs in Probabilistic Sensitivity Analysis – NOT Necessarily a Negative Outcome

Reliability assessment of a subsea pipe-in-pipe system for major failure modes

To ameliorate reliability analysis efficiency for aeroengine components, such as compressor blade, support vector machine response surface method (SRSM) is proposed. SRSM integrates the advantages of support vector machine (SVM) and traditional response surface method (RSM), and utilizes experimental samples to construct a suitable response surface function (RSF) to replace the complicated and abstract finite element model. Moreover, the randomness of material parameters, structural dimension and operating condition are considered during extracting data so that the response surface function is more agreeable to the practical model. The results indicate that based on the same experimental data, SRSM has come closer than RSM reliability to approximating Monte Carlo method (MCM); while SRSM (17.296 s) needs far less running time than MCM (10958 s) and RSM (9840 s). Therefore, under the same simulation conditions, SRSM has the largest analysis efficiency, and can be considered a feasible and valid method to analyze structural reliability.

A single nucleotide polymorphism (SNP) is a DNA sequence variation in a population. SNP is just a single nucleotide difference in the genome. Many statistical methods have been proposed to predict the racial classification of individuals based on SNP genetic data. The selection of the right classification method is very important because it will determine the accuracy of the classification results. This research aims to identify the highest average accuracy between two popular classification methods in Machine Learning (ML), including K-Nearest Neighborhood (KNN) and Support Vector Machine (SVM). This study used SNP genetic data for 120 samples from 2 CEU-European races and Yoruba-African races, where for each sample 10 SNPs were selected with the same location identity. The experiment was carried out by testing each classification method with variations in the percentage of test data 10, 20, 30, 40 and 50, which was combined with Euclidean distance for the KNN classification method. Based on the results of the study, the accuracy of the prediction of the classification of individual races based on SNP genetic data, the classification using KNN has an average prediction accuracy that is better than the SVM classification if the SNP location used tests has a high correlation with the sample class. In this case, the highest average accuracy value of KNN is 98.906% and SVM is 98.779%. There is a significant difference between the highest average accuracy of KNN and SVM based on the Wilcoxon statistical test with a significance level of α = 0.05. Benefits of this research are to find the right classification method for predictions of individual racial classification based on SNP genetic data.