Deterministic Analysis Methods Research Articles

A Probabilistic Liquefaction Hazard Analysis: Case Studies from the Marmara Region

Earthquake induced soil liquefaction poses a significant threat to buildings and infrastructure, as evidenced by numerous catastrophic seismic events. Existing approaches of regional liquefaction hazard assessment predominantly rely on deterministic analysis methods. This paper presents a novel Probabilistic Liquefaction Hazard Analysis (PLHA) framework based on Monte-Carlo (MC) simulations to mitigate future seismic risks associated with liquefaction. The proposed procedure requires only publicly available data, offering accessibility and applicability in resource-constrained settings. A key feature of the procedure is its ability to deal with uncertainties in earthquake and soil parameters using distribution functions. Liquefaction potential is assessed through parameters such as Liquefaction Potential Index (LPI) and Liquefaction Severity (LS). The procedure is implemented in MATLAB as part of a broader probabilistic risk assessment framework for developing countries. The developed procedure is applied to the high risk city of Adapazari, Türkiye; an area lacking prior PLHA studies. Results are validated against observed liquefaction data from a simulated scenario event of the 1999 Kocaeli earthquake. Probabilistic liquefaction hazard maps are generated for the study area and the entire Marmara region in terms of LPI and LS. A novel aspect of this work is the integration of a time-dependent Probabilistic Seismic Hazard Analysis (PSHA) model into the PLHA framework. Results are compared with those predicted using the Poisson model for the Marmara region. Findings demonstrate that the developed PLHA procedure offers a robust and flexible tool for predicting seismic liquefaction hazards, providing valuable insights for loss estimation and risk mitigation planning.

Deterministic Gradient-Descent Learning of Linear Regressions: Adaptive Algorithms, Convergence Analysis and Noise Compensation.

Weight learning forms a basis for the machine learning and numerous algorithms have been adopted up to date. Most of the algorithms were either developed in the stochastic framework or aimed at minimization of loss or regret functions. Asymptotic convergence of weight learning, vital for good output prediction, was seldom guaranteed for online applications. Since linear regression is the most fundamental component in machine learning, we focus on this model in this paper. Aiming at online applications, a deterministic analysis method is developed based on LaSalle's invariance principle. Convergence conditions are derived for both the first-order and the second-order learning algorithms, without resorting to any stochastic argument. Moreover, the deterministic approach makes it easy to analyze the noise influence. Specifically, adaptive hyperparameters are derived in this framework and their tuning rules disclosed for the compensation of measurement noise. Comparison with four most popular algorithms validates that this approach has a higher learning capability and is quite promising in enhancing the weight learning performance.

On improving approaches to reducing earthquake damage

Catastrophic earthquakes have not occurred in Russia since the Neftegorsk earthquake of 1995. Almost 30 years of apparent calm do not mean, however, that such disasters will not occur in the coming decades. Over these 30 years, there have been about 10 earthquakes of comparable strength, but all of them were in sparsely populated areas of Kamchatka, the Kuril Islands, and Altai. The experience of recent earthquakes in Turkey and Morocco shows that the approaches to seismic hazard assessment and the system of using such assessments in construction adopted worldwide, including in Russia, need to be adjusted. The paper discusses the need to apply both a probabilistic approach to seismic hazard assessment, necessary for seismic risk calculations, and deterministic analysis methods that will allow moving to more reliable assessments for detailed seismic zoning in areas of possible catastrophic earthquakes and for the construction of important facilities. Seismic risk assessments have not previously been carried out on a national scale, but are necessary for the implementation of a disaster insurance system and prioritization of costs for strengthening buildings in seismically hazardous areas. The article was prepared on the basis of a report heard at a meeting of the presidium of the RAS on June 11, 2024.

Quantitative analysis of subway station seismic deformation under random earthquakes and uncertain soil properties using the equivalent linearization method

The seismic performance of underground structures is strongly influenced by the characteristics of both the surrounding soil and the earthquake. In contrast to traditional deterministic analysis methods, this study uses a stochastic analysis approach to investigate the effect of uncertainties in nonlinear soil characteristics, shear wave velocity, density, and earthquake randomness on the response of underground stations. The equivalent linearization method is employed to approximate the nonlinear behavior of the soil. The soil was modeled using a linear elastic constitutive model combined with Rayleigh damping in the finite element model. Inter-story displacements are used to determine structural damage. Probabilistic analysis methods are used to obtain their statistical characteristics, and the probability of failure is calculated. The results show that, according to single parameter analysis, random ground motion results in the greatest probability of exceeding the threshold (PET), while ground shear wave velocity significantly affects the coefficient of variation (COV), and the effect of density is the smallest. The study also found that when soil nonlinearity, shear wave velocity, and random ground motion are considered simultaneously, the range, mean, standard deviation, and COV of interstory displacement all increase significantly, but the PET slightly decreases. In summary, the analysis results indicate that random ground motion has the greatest impact on interstory displacement, followed by shear wave velocity, with nonlinear soil characteristics having a smaller effect, and density the least. Therefore, the impact of various uncertainties should be fully considered in the analysis of underground structures, especially random ground motion and shear wave velocity.

Unmconf : an R package for Bayesian regression with unmeasured confounders

The inability to correctly account for unmeasured confounding can lead to bias in parameter estimates, invalid uncertainty assessments, and erroneous conclusions. Sensitivity analysis is an approach to investigate the impact of unmeasured confounding in observational studies. However, the adoption of this approach has been slow given the lack of accessible software. An extensive review of available R packages to account for unmeasured confounding list deterministic sensitivity analysis methods, but no R packages were listed for probabilistic sensitivity analysis. The R package unmconf implements the first available package for probabilistic sensitivity analysis through a Bayesian unmeasured confounding model. The package allows for normal, binary, Poisson, or gamma responses, accounting for one or two unmeasured confounders from the normal or binomial distribution. The goal of unmconf is to implement a user friendly package that performs Bayesian modeling in the presence of unmeasured confounders, with simple commands on the front end while performing more intensive computation on the back end. We investigate the applicability of this package through novel simulation studies. The results indicate that credible intervals will have near nominal coverage probability and smaller bias when modeling the unmeasured confounder(s) for varying levels of internal/external validation data across various combinations of response-unmeasured confounder distributional families.

On methodological tools for studying problems of the development of agro-industrial complex

The article considers the methodological tools used by domestic and foreign scientists to carry out research and educational activities in the field of studying problems of development of the agro-industrial complex. Within the framework of the study, it was established that, despite the applied and practice-oriented nature of food issues, scientists use theoretical rather than empirical research methods to a greater extent — they predominantly turn to traditional and simplest methods of processing information, without fully using the existing economic –mathematical and economic-statistical apparatus. It has been revealed that scientists, as a rule, use only methods of deterministic factor analysis in relation to clearly dependent quantities and indicators, while methods of stochastic factor analysis are practically not used; the insufficiency and limitation of using the balance method as one of the key ways to determine the substantive aspect of the studied sphere have been established. Based on the results, the author’s approach to the use of methodological tools for the balanced development of food supply for the population is proposed, where at the first stage calculation methods for assessing economic information are used, at the second stage — methods of factor analysis, at the third — theoretical research methods and at the final stage — methods of modeling and optimization of economic indicators. The results of the study can be used to improve the conceptual and fundamental foundations for the development of the domestic agro-industrial complex and future practical transformations in the food sector.

Fatigue study on improved composite box girder bridge with corrugated steel webs

The purpose of the study is to estimate the fatigue behavior of an improved composite box girder bridge with corrugated steel webs (ICBGBCSW) based on a three-dimensional vehicle-bridge coupled vibration system (VBCVS). In the VBCVS, the benchmark of the ICBGBCSW widely used in the highway bridges of Gansu Province of China was adopted as the bridge model, and a 6-axle fatigue load model established based on the traffic data of China was adopted as the fatigue vehicle loading. Based on the established VBCVS, the critical fatigue details of the ICBGBCSW were determined and the effects of pavement roughness condition (PRC) and vehicle speed on the stress ranges experienced by critical fatigue details were first investigated. Then, the fatigue live of critical fatigue details were evaluated based on the deterministic analysis method and reliability analysis method. The results show that the fatigue life of the ICBGBCSW can reach more than 375 years from a deterministic perspective under the current traffic conditions of Gansu Province of China and that the fatigue reliability index of the ICBGBCSW sharply decreases to the target fatigue reliability index of 2 if the PRC stays in the “Poor” class and the “Very poor” class for eight years and two years, respectively. The results obtained in the study can provide a technique support for the wide application of the ICBGBCSW and provide a reference for the bridge management agency to make plans for bridge pavement maintenance.

Study on bearing performance and reliability of modified monopiles for OWTs using deterministic and probabilistic analysis methods

Existing studies mainly focus on the bearing capacity of monopiles, with less emphasis on reliability analysis, which is an important aspect of the performance of offshore wind turbine (OWT) foundations. This paper investigates the bearing capacity and reliability of traditional and hybrid monopiles using deterministic and probabilistic analyses. The three-dimensional finite element model (FEM) considering the structure-soil interaction is established and its accuracy is verified through comparisons with existing research findings. The responses of monopiles under wind and wave loads are compared in detail, including the rotation angle at the mudline, structural stress, and soil displacement. The environmental design parameters are obtained based on the 30-year annual extreme data, and the correlation between these environmental parameters is considered to obtain a large amount of random environmental parameter data. Then, the back propagation neural network (BPNN) and the response surface method (RSM) are combined to construct the failure response surface. Research shows that the reliability index of the plated monopile does not improve significantly, while the skirted-monopiles perform much better than the traditional monopile. The proposed FEM-BPNN-RSM coupled methodology provides a more comprehensive understanding of the monopile bearing capacity, and can be used for the future monopile structure design of OWTs.

Comparison of the Efficiency of Overcoming the Crisis of Russian and Foreign Oil and Gas Companies

The purpose of the study was to evaluate the performance of large domestic and foreign oil and gas companies based on a factor analysis of the return on equity in the context of financial and economic crises. The relevance of the topic is due, on the one hand, to the leading role of the oil and gas industry in the Russian economy, and, on the other hand, to the need develop modern tools for the assessment and prediction of the performance of companies and comparison of the results of economic activity with those of foreign competitors. The object of the study was the financial performance of the largest domestic and foreign vertically integrated oil and gas companies. The subject of the study is the methods of deterministic factor analysis for assessing indicators in the oil and gas sector. The paper analyzes the main empirical indicators that reflect the degree of efficiency, justifies the choice of the profitability indicator of own assets as the main indicator for research in the oil and gas industry, analyzes approaches to assessing the contribution of various factors to the final efficiency. Methods of classification, decomposition, statistical, comparative and factor analysis were used. A five-factor DuPont decomposition of the return on equity was carried out, and the key factors affecting the efficiency of companies were identified: tax burden coefficient, percentage burden coefficient, return on sales, asset turnover ratio, financial leverage ratio. Using the LMDI‑1 model, the impact of changes in economic factors on changes in the efficiency of companies in 2013–2017 and 2018–2021 was assessed. It was concluded that domestic companies, on average, slightly outperformed foreign ones in terms of the considered efficiency indicator and coped better with the consequences of financial and economic crises in the periods under review. At the same time, domestic companies have a higher value of the tax burden coefficient with lower values of assets turnover and financial leverage.

Safety Assessment for Full Flight between Beidou Radio Determination Satellite Service Airborne Equipment and 5G System

The Beidou Radio Determination Satellite Service (RDSS) is an advantageous service of the Beidou system. However, due to the weak landing power of Beidou RDSS signals and an operating frequency close to the 5G frequency, the system is vulnerable to interference from ground signals. In this paper, from the perspective of civil aviation safety, different evaluation indicators are used for the takeoff and cruise phases, respectively, to study the impact caused by adjacent frequency interference on airborne Beidou RDSS equipment. In the takeoff phase, accurate aircraft position information is obtained by processing real trajectory files. Deterministic analysis methods are used to determine the safety distance for the coexistence of the two systems. During the cruise phase, ground-based 5G base stations have less influence on the airborne RDSS receiver due to the high flight altitude, so the main consideration is electromagnetic compatibility between the airborne Beidou RDSS system and the 5G ATG system. By establishing a Boeing 737–800 simulation model, the antenna isolation degree is used as the evaluation index, and a reasonable antenna layout is given according to the evaluation results. In this study, the theoretical simulation and real flight data are combined to summarise the exact range of adjacent frequency influence during the takeoff phase and a reasonable antenna layout during the cruise phase.

Integration of Effectiveness and Efficiency Indicators of State Support for Projects and Programmes for the Development of Higher Education in Russia

To achieve the set goal, the study employs expert-analytical methods of retrospective, deterministic, and factor analysis, as well as the tools of foresight technology, allowing the assessment of the impact of the system of factors on the effectiveness indicators of state financial support for universities. The key result of the study is a conceptual model for assessing the effectiveness and efficiency of state support for university development projects and programs, which is based on foresight methodology and establishes the interrelation between the system of factors and the indicators of efficiency and effectiveness of state funding for Russian universities. The proposed model not only offers a quality expert-analytical foundation for important financial decisions on state support for university development programs and academic excellence projects, but also encourages educational organizations to develop strategic development programs and roadmaps for their effective implementation.

Reliability Analyses of Soil Slopes with Multiple Spatially Varying Parameters Using Multi-Input Convolutional Neural Networks

Constructing meta-models and selecting a suitable deterministic analysis method are important to improve the computational efficiency and accuracy of the nonintrusive reliability analysis of a spatially varying soil slope. However, existing meta-models are not applicable to the slopes considering multiple parameters with high spatial variability. Moreover, it is difficult to identify the failure modes when the spatial variability is high by using deterministic analysis methods based on slip surface search. Therefore, a nonintrusive stochastic strength reduction finite-element method (SRFEM) is developed based on the multi-input convolution neural networks (CNNs) and ABAQUS 2016. The SRFEM developed based on ABAQUS is adopted as the deterministic analysis method to avoid the uncertain search for the critical slip surfaces of slopes with high spatial variability. A multi-input CNN is proposed to construct the meta-model to avoid the “curse of dimensionality” and replace the overmuch times of time-consuming finite-element simulations. It can fit the relationships between multiple spatially varying parameters and the factor of safety by processing different parameters with different streams of CNNs. Two illustrative examples show that the proposed method can accurately identify the failure modes of slopes with different degrees of spatial variability. The agreement of the reliability results based on the proposed method and the general random finite-element method (RFEM) shows the high accuracy of the proposed method. The time cost of the proposed method can be reduced to 6.0 × 10−3 times that of the general RFEM, verifying the high computational efficiency of the proposed method. The multi-input CNN also shows higher fitting accuracy and better interpretability than the single-stream CNN and the support vector machines (SVMs). The generalization ability, accuracy, and efficiency of the proposed method show its potential to carry out the reliability analyses of slopes with multiple spatially varying parameters.

Performance Analysis of Coordinated Interference Mitigation Approach for Automotive Radar

Millimeter automotive radar has great potential in Advanced Driver Assistance Systems (ADAS) to enable safety features such as adaptive cruise control and collision avoidance. However, with widely deployment of millimeter radars on vehicles, the risk of radar mutual interference becomes a major factor limiting the high performance of radar detection. In this paper, we analyze the mutual interference among multiple Frequency Modulated Continuous Wave (FMCW) radars. On one hand, we study the interference in detail by considering co-channel interference and adjacent channel interference simultaneously. Besides, the co-channel interference is analyzed by employing stochastic geometry model while the adjacent channel interference is assessed by the deterministic analysis method. On the other hand, we propose a Time-Frequency Division Multiple Access (TFDMA) scheme to mitigate the interference in a coordinated manner and evaluate it in terms of mitigation delay, the probability of interference, effective detectable density, maximum number of interference-free radar, and control signaling overhead. Finally, we study the power allocation strategy to enable the effectiveness of the coordinated interference mitigation approach based on the interference analysis. Simulation results verify the proposed framework for interference analysis by employing Monte Carlo method, and the performance improvement of the coordinated interference mitigation approach is 3.5 dB.

An estimation method for an unknown covariance in cross-section adjustment based on unbiased and consistent estimator

ABSTRACT A new estimation method of an unknown covariance in the cross-section adjustment method for the development of an application library is proposed. The unknown covariance is defined by the difference between the true covariance (the population covariance) and a prior covariance assumed by an analyst. The unknown covariance is estimated using an empirical covariance consistent with the observed data. To estimate the unknown covariance, an unbiased and consistent estimator in regression analysis has been incorporated into the conventional cross-section adjustment. This estimator does not require assumptions for the probability distribution of the observation data. The statistical properties of this estimator were numerically verified. In addition, the effectiveness of the proposed method was confirmed by another numerical test using actual integral experimental data. In the second numerical test, the modeling uncertainty (covariance) due to the deterministic analysis method was assumed to be unknown. The results show that the proposed method can practically estimate the unknown covariance and adjusted cross-sections using only prior information on covariances.

The Forecast Analysis of Chinese Resident Consumption Based on the Residual Autoregressive Model

This paper aims to improve the accuracy of resident consumption forecast by comparing different methods and selecting the residual autoregressive model. The model combines deterministic analysis method and autoregressive modeling to extract deterministic and random information, respectively, and improve the prediction accuracy. The paper introduces the modeling process of the residual autoregressive model and conducts empirical analysis on the consumption level data of Chinese residents to evaluate the fitting accuracy of the model. Finally, the model is used to forecast the consumption level of Chinese residents in 2021-2023, and the results indicate an average growth rate of about 7% over the next three years, assuming no major changes in the external environment. This paper contributes to the understanding of the residual autoregressive model and its application in the field of resident consumption forecasting, providing valuable insights for policymakers and researchers in this area.

A Deterministic Method for Evaluating Safety Factor of Deep Excavation Stability against Groundwater Inrush Equivalently Considering Soil Uncertainty

In current design specifications for deep excavation, the determination of safety factors against groundwater inrush only considers the self-weight effect of soil mass at the bottom. However, the shear strength and its spatial variability in a cement-solidified bottom layer also plays an important role in safety factor estimation. Based on that, in this study, the strength reduction method was introduced into reliability analysis of deep excavation engineering, and the influence of shear strength and its spatial variability of cement-solidified soil on the stability of deep excavation is studied. Furthermore, a deterministic analysis method that can equivalently consider soil uncertainty is proposed and verified.

Mekânsal otokorelasyon ve kümeleme analizi yaklaşımı ile Göksu Çayı Havzası’nın (Sakarya Nehri Havzası) bütünleşik ve sürdürülebilir havza yönetim modeli

Doğal ve beşeri ortam koşullarının yoğun etkileşim halinde olduğu havzalarda birçok kapsamdaçeşitli modellerle yönetim çalışmaları uygulanmaktadır. Bu araştırmanın amacı, coğrafi çeşitliliği,etkileşimleri ve potansiyel riskleri barındıran Göksu Çayı Havzası’nın farklı değişkenler üzerindenmekânsal otokorelasyon ve kümeleme analizine dayalı havza yönetim modelinin oluşturulmasıdır.Coğrafi Bilgi Sistemlerinin (CBS) etkin kullanıldığı çalışmada, deterministik, kantitatif,korelasyon ve dağılış analizi yöntemleriyle çok basamaklı sistematik oluşturulmuştur. Havzanınbütün coğrafi unsurlarını, etkileşimleri, doğal dinamik işleyiş yapısını ortaya koymak ve ilişkiselolarak kümelenme dağılışını oluşturmak için birçok parametrenin analizleri ile dört ana değişken(alt model) üretilmiştir. Ana değişkenler, jeomorfolojik uygunluk-elverişlilik, yağış akış, çoklu-riskve arazi kullanım modellerinden oluşur. Her bir model karşılıklı olarak mekansal korelasyona tabitutulmuş ve havzanın kümeleme analizi dağılış verisi üretilmiştir. Beş farklı kümenin tespit edildiğiveri, sorun-risk potansiyeli ve sürdürülebilir-uygun kullanım potansiyeli açısından da analizedilmiştir. Daha sonra dağılış verisi, Lokal Moran’s I-Anselin testi ve Getis-Ord Gİ istatistiği ile anlamlılıkve kümelenme açısından test edilmiştir. Analizlerden, havzanın yüksek çerçevesini oluşturansahaların sürdürülebilir-uygun kullanım potansiyeline sahip kümelenme gösterdiği, İnegölOvası, Yenişehir kuzeyi ve Göksu Vadisi’nde sorun-risk potansiyeli yüksek kümelenmenin olduğutespit edilmiştir. Havzada sürdürülebilirliğin sağlanması için, ekolojik sahaların korunması, sel,taşkın, erozyon, heyelan tedbirlerin arttırılması, akarsulardaki su kalitesinin kontrol edilmesi veantropojenik baskı yoğunlaşmasının daha uygun alanlara yönlendirilmesi gerekmektedir.

Approbation of the Averaged Method of Chain Substitutions for Three- and Four-Multiples and Multiplicative-Multiples Factor Models

The aim of the present study is to present the results of the approbation of the methodology of the averaged method of chain substitutions for three and four-multiple and multiplicative-multiple factor models and to systematize in tabular form all mathematical expressions developed so far to determine the individual factor influences by types of factor models. The relevance of the research is caused by the disadvantages and the limited applicability of the methods of deterministic factor analysis developed so far, which is one of the areas of financial and economic analysis. The scientific novelty of the research is the new mathematical expressions developed by the author for determining the individual factor influences according to the methodology of the averaged method of chain substitutions for three and four multiple and multiplicative-multiple factor models. Previous and new mathematical expressions according to the averaged method of chain substitutions are systematized by types of factor models in tabular form. The main conclusion is that the averaged method of chain substitutions has complete universality of application for all types of factor models and is characterized by accuracy and unambiguity of the results obtained for quantification of individual factor influences.

An accident diagnosis method of pressurized water reactor based on BI-LSTM neural network

Due to the lack of accident data, previous researchers used accident data obtained from system code simulation to train and verify the machine learning models. In this study, a long short-term memory (LSTM) neural network was used to construct a pressurized water reactor (PWR) accident diagnosis model. The model was trained and tested using simulation data of a PWR based on the deterministic analysis method and uncertainty analysis method. The accuracy of the LSTM model based on deterministic analysis and uncertainty analysis are 96.67% and 98.77%, respectively. The LSTM model developed in the present study is superior to traditional mathematical statistics and support vector machine (SVM) models in accident diagnosis, especially for diagnoses with uncertain data. The results of this study show that the LSTM model has practical value in reactor accident diagnosis.

Cost-effectiveness of edaravone dexborneol versus edaravone for the treatment of acute ischemic stroke in China: Based on the TASTE study.

Background and purpose: The TASTE trial indicated that patients with acute ischemic stroke (AIS) using edaravone dexborneol have a significantly higher proportion of 90-day good functional outcomes (mRS 0-1) than those using edaravone. This study compared the cost-effectiveness of the aforementioned interventions in treating AIS in the Chinese setting, aiming to inform treatment decisions in clinical practice. Methods: A model combining a decision tree and a Markov model was developed to assess the cost-effectiveness of edaravone dexborneol versus edaravone for AIS over a 30-year time horizon from the Chinese healthcare system's perspective. Both efficacy and safety data were extracted from the TASTE study. Local costs and utilities were derived from publications and open-access databases; both cost and effectiveness were discounted at a rate of 5% per year. Sensitivity analyses were conducted to ensure robustness and identify the main drivers of the result. Results: Compared with edaravone, edaravone dexborneol for AIS was found to be cost-effective in the first year and highly cost-effective as the study time horizons extended. In the long term (30 years), edaravone dexborneol yielded a lifetime gain of 0.25 (0.07-0.45) quality-adjusted life years (QALYs) at an additional cost of CNY 2201.07 (-3,445.24-6,637.23), yielding an ICER of CNY 8823.41 per QALY gained under the willingness-to-pay (WTP) of 1.5 times per capita GDP (121,464 CNY). The result is robust in both deterministic and probabilistic sensitivity analysis (PSA) methods, with the advantage of the edaravone dexborneol strategy increasing over time. Specifically, the probability of edaravone dexborneol dominant dexborneol is 76.30%, 98.90%, and 99.50% over 1-, 5-, and 30-year time horizons. Conclusion: Both short- and long-term economic analyses suggest that edaravone dexborneol is highly likely to be a cost-effective alternative to treat AIS compared with edaravone in China.