Article
Environmental Sciences
Zhengjiang Lin, Ying Liu, Zhihui Cheng, Rui Zhao, Han Zhang
Summary: This study evaluated the health risks of drinking water in Hanyuan County by collecting and analyzing 96 samples of peripheral drinking water from 30 sites. The results showed that all indicators met the required standards except for nitrate. The study found that drinking water posed a specific carcinogenic risk to adults, with arsenic and hexavalent chromium contributing the most, and a specific non-carcinogenic risk to children if fluoride, nitrate, and arsenic exceeded certain levels.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Engineering, Civil
Jia-Hua Yang, Heung-Fai Lam, Yong-Hui An
Summary: The paper proposes a new two-phase adaptive MCMC method to address the problem of determining the posterior probability density function (PDF) in Bayesian model updating. By using a parameter-space search algorithm and a weighted MCMC algorithm, samples in the regions of high probability can be generated adaptively without going through computationally demanding multiple levels.
ENGINEERING STRUCTURES
(2022)
Article
Engineering, Multidisciplinary
Ke Zhang, Kailun Su, Yunhan Yao, Qingsong Li, Suan Chen
Summary: This paper presents a dynamic evaluation model of Markov chain Monte Carlo (MCMC) roundness error measurement uncertainty based on a stochastic process. The model samples the stochastic process using the MCMC method and calculates the state transition function to reflect the autocorrelation characteristics of the parameters. A comparison between high-precision and low-precision measurements verifies the accuracy and stability of the model, showing that the MCMC method is consistent with the traditional GUM method and Monte Carlo method. The MCMC method based on the stochastic process achieves dynamic evaluation of roundness error measurement uncertainty, obtaining accurate results and improving the evaluation accuracy.
Article
Engineering, Multidisciplinary
Maria Clavijo, Adriana M. Schleder, Enrique Lopez Droguett, Marcelo R. Martins
Summary: This paper presents RAM analysis for DP2 and DP3 systems in drilling operations, showing higher reliability for DP3 system. It provides more information about equipment failures with uncertainties and probability density functions, identifying critical equipment and repair times for each system.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY
(2022)
Article
Construction & Building Technology
Xin Li, Jiangyan Liu, Bin Liu, Qing Zhang, Kuining Li, Zhenxiang Dong, Lunjie Mou
Summary: This paper investigates the impact of data uncertainty on data-driven-based building FDD models from both high and low levels. Results show that data quality significantly affects the performance of the model, especially at higher levels of uncertainty.
JOURNAL OF BUILDING ENGINEERING
(2021)
Article
Engineering, Geological
Chao Zhao, Wenping Gong, Tianzheng Li, C. Hsein Juang, Huiming Tang, Hui Wang
Summary: Accurate characterization of subsurface stratigraphic configuration is crucial to geotechnical engineering work, but uncertainty can be significant due to complexity and limited data availability. This paper presents a method for characterizing subsurface stratigraphy with limited borehole data, demonstrating its effectiveness and advantages through comparative analyses and a case study in Western Australia.
ENGINEERING GEOLOGY
(2021)
Article
Energy & Fuels
Gyun Seob Song, Man Cheol Kim
Summary: Monte Carlo simulations are widely used for uncertainty analysis in the probabilistic safety assessment of nuclear power plants. Despite its limitations, this study provides mathematical formulations and analytic solutions for uncertainty analysis in a probabilistic safety assessment, demonstrating good agreement with Monte Carlo simulation results and providing deeper insights into uncertainty analyses.
Article
Multidisciplinary Sciences
Molly Asher, Nik Lomax, Karyn Morrissey, Fiona Spooner, Nick Malleson
Summary: This study uses Approximate Bayesian Computation (ABC) to update a COVID-19 model with the most recent data, improving its predictions and reducing uncertainties. The findings highlight the importance of incorporating up-to-date observations in infectious disease modelling, especially for policy development.
SCIENTIFIC REPORTS
(2023)
Article
Materials Science, Multidisciplinary
Hung Ba Tran, Hiroyoshi Momida, Yu-ichiro Matsushita, Koun Shirai, Tamio Oguchi
Summary: This study investigates the temperature dependence of magnetocrystalline anisotropy energy in CrI3 and its effect on the thermodynamic properties. The research successfully reproduces the negative sign of the isothermal magnetic entropy changes and reveals the role of anisotropic magnetic susceptibility and magnetization anisotropy. The findings shed light on the connection between magnetic field direction, entropy change, and free energy difference in CrI3.
Article
Chemistry, Physical
Hung Ba Tran, Tetsuya Fukushima, Kazunori Sato, Yukihiro Makino, Tamio Oguchi
Summary: The study proposed a new model and scheme to investigate the magnetocaloric properties of Mn1-xCuxCoGe alloy, and found that the enhancement of magnetostructural coupling significantly affects the isothermal magnetic entropy change, depending on the conditions of magnetic phase transition temperatures.
JOURNAL OF ALLOYS AND COMPOUNDS
(2021)
Article
Chemistry, Multidisciplinary
Simone Arena, Irene Roda, Ferdinando Chiacchio
Summary: This paper discusses the importance of dependability assessment for system reliability, safety, and maintainability, and introduces Dynamic Reliability (DPRA) and Stochastic Hybrid Automaton (SHA) models. It aims to extend the functionalities of these tools to different maintenance policies through analyzing their main features and designing a software model to integrate into SHyFTOO.
APPLIED SCIENCES-BASEL
(2021)
Article
Chemistry, Multidisciplinary
Alexandre S. Avaro, Juan G. Santiago
Summary: This article presents a quantification of the uncertainty in the experimental determination of kinetic rate parameters for enzymatic reactions. The authors examine several sources of uncertainty and bias and compute typical uncertainties of kcat, KM, and catalytic efficiency. The extraction of these parameters for CRISPR-Cas systems is analyzed as a salient example. Reports of enzymatic kinetic rates for CRISPR diagnostics have been highly unreliable and inconsistent.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
(2022)
Article
Engineering, Manufacturing
P. Honarmandi, R. Seede, L. Xue, D. Shoukr, P. Morcos, B. Zhang, C. Zhang, A. Elwany, I. Karaman, R. Arroyave
Summary: The Eagar-Tsai (E-T) model in the context of 3D printing was studied systematically from an uncertainty quantification/propagation (UQ/UP) perspective. Model parameters were calibrated against experimental data using Markov Chain Monte Carlo (MCMC) sampling, and posterior distributions of parameter values were propagated. It was found that discrepancies between predicted and measured melt pool depths existed under keyholing conditions, but a physics-based correction improved agreement with experiments without increasing model complexity significantly.
ADDITIVE MANUFACTURING
(2021)
Article
Geosciences, Multidisciplinary
Alexander Schaaf, Miguel de la Varga, Florian Wellmann, Clare E. Bond
Summary: This paper introduces a method to incorporate geological information into probabilistic geomodeling using the open-source software GemPy. By checking simulated geomodel realizations against topology information without specifying a likelihood function, the method demonstrates the feasibility of constraining and improving probabilistic geomodel ensembles with reduced uncertainty.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2021)
Article
Engineering, Industrial
Robert Millar, Hui Li, Jinglai Li
Summary: In many engineering systems, the performance or reliability is characterized by a scalar variable. The distribution of this variable is important for uncertainty quantification in various applications. Standard Monte Carlo simulations are often used but struggle to efficiently estimate the tail of the distribution. The Multicanonical Monte Carlo method provides an adaptive importance sampling scheme, where samples are drawn from a nonstandard importance sampling distribution using Markov chain Monte Carlo (MCMC). However, MCMC is inherently serial and difficult to parallelize. In this paper, we propose a new approach that uses the Sequential Monte Carlo sampler for parallel implementation and demonstrate its competitive performance with mathematical and practical examples.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Environmental Sciences
Andreas Lindhe, Lars Rosen, Per-Olof Johansson, Tommy Norberg
Article
Water Resources
Viktor Bergion, Andreas Lindhe, Ekaterina Sokolova, Lars Rosen
Summary: The study evaluated the impact of unexpected risk events in drinking water systems on risk reduction alternatives using a scenario-based approach. Results showed that including unexpected risk events changed the probability of positive net present value for the analyzed alternatives in the decision model, with a more significant effect on systems with low base load and low pathogen log reduction.
EXPOSURE AND HEALTH
(2021)
Article
Environmental Sciences
Karin Sjostrand, Josefine Klingberg, Noor Sedehi Zadeh, Mattias Haraldsson, Lars Rosen, Andreas Lindhe
Summary: Water disruptions can cause production and sales losses for businesses, making risk reduction highly beneficial. This study aims to estimate time-dependent water supply resiliency factors for economic sectors to improve decision support for water supply planning and risk management.
Article
Environmental Sciences
Anna Ohlin Saletti, Lars Rosen, Andreas Lindhe
Summary: Infiltration and inflow into wastewater systems have significant impacts, such as flooding and pollution, making it crucial to handle these situations effectively. Existing models often lack risk-based decision-making and uncertainty analysis, highlighting the need for further research and development of more comprehensive decision support models.
Article
Environmental Sciences
Paul Drenning, Shaswati Chowdhury, Yevheniya Volchko, Lars Rosen, Yvonne Andersson-Skold, Jenny Norrman
Summary: Research has shown that combining GRO remediation measures with beneficial green land use can gradually reduce risks and restore ecosystem services. A risk management and communication framework is proposed to support the application of GRO in phytomanagement strategies at contaminated sites, with results indicating strong support for most risk mitigation mechanisms.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Environmental Sciences
Nadine Gaertner, Andreas Lindhe, Julia Wahtra, Tore Soederqvist, Lars-Ove Lang, Henrik Nordzell, Jenny Norrman, Lars Rosen
Summary: Water protection is a widely supported goal in society, but the implementation of measures is often complicated by competing interests. This paper introduces a method for assessing all services provided by a drinking water source and presents a specific list of services tailored to Sweden. The assessment can be used to communicate and negotiate protection measures with stakeholders and illustrate the synergies and trade-offs beyond drinking water protection.
Article
Environmental Sciences
Nils-Petter Skold, Viktor Bergion, Andreas Lindhe, Alexander Keucken, Lars Rosen
Summary: This article presents a Swedish case study that evaluates the installation of ultrafiltration membranes through combining risk assessment and cost-benefit analysis. The study uses quantitative microbial risk assessment to estimate the risk reduction from waterborne pathogens and monetizes the societal value of improved water quality. The results show that the installation of ultrafiltration membranes is a sound investment from a societal economic perspective, reducing infection risk and improving aesthetic water quality.
Article
Engineering, Environmental
Anna Ohlin Saletti, Andreas Lindhe, Tore Soderqvist, Lars Rosen
Summary: This paper introduces a novel risk-based model for assessing the cost to society from infiltration and inflow to wastewater systems. It monetizes effects related to wastewater treatment, pumping, combined sewer overflows, and basement flooding. The model applies a probabilistic approach to account for uncertainties and demonstrates its applicability in a case study in Gothenburg, Sweden. The main results indicate that investments at the wastewater treatment plant and restoration due to basement flooding events contribute the most to the costs. Sensitivity analyses show the dependence on factors such as infiltration and inflow volume, basement flooding share, and discount rate. The use of expert elicitation for quantifying input data is also highlighted as a valuable method. This model fills an important research gap and facilitates a more sustainable and comprehensive handling of water issues.
Review
Ecology
Emrik Lundin Frisk, Yevheniya Volchko, Olof Taromi Sandstrom, Tore Soderqvist, Lars O. Ericsson, Fredrik Mossmark, Andreas Lindhe, Goran Blom, Lars-Ove Lang, Christel Carlsson, Jenny Norrman
Summary: The subsurface is a multifunctional natural resource that is often overlooked, leading to unfair distribution and unsustainable development. Similar to ecosystem services (ES), geosystem services (GS) highlight the abiotic services and services provided by the subsurface. This study aimed to review the definitions and categorization of GS and suggest how they can support subsurface planning. The review found that the GS concept is both novel and inconsistent, with two prominent definitions. While some GS are included in the current ES framework, there are essential services that are omitted. A unified framework for GS is necessary to improve subsurface planning.
ECOSYSTEM SERVICES
(2022)
Article
Geosciences, Multidisciplinary
Johanna Merisalu, Jonas Sundell, Lars Rosen
Summary: This paper presents a decision support framework for mitigating hydrogeological risks in underground construction. The framework follows guidelines from the International Standardization Organization (ISO) and focuses on risk analysis and evaluation. Cost-benefit analysis (CBA) is used to monetize consequences and evaluate the economic aspects of risk mitigation.
Article
Engineering, Industrial
Mateusz Oszczypala, Jakub Konwerski, Jaroslaw Ziolkowski, Jerzy Malachowski
Summary: This article discusses the issues related to the redundancy of k-out-of-n structures and proposes a probabilistic and simulation-based optimization method. The method was applied to real transport systems, demonstrating its effectiveness in reducing costs and improving system availability and performance.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Wencheng Huang, Haoran Li, Yanhui Yin, Zhi Zhang, Anhao Xie, Yin Zhang, Guo Cheng
Summary: Inspired by the theory of degree entropy, this study proposes a new node identification approach called Adjacency Information Entropy (AIE) to identify the importance of nodes in urban rail transit networks (URTN). Through numerical and real-world case studies, it is found that AIE can effectively identify important nodes and facilitate connections among non-adjacent nodes.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Hongyan Dui, Yaohui Lu, Liwei Chen
Summary: This paper discusses the four phases of the system life cycle and the different costs associated with each phase. It proposes an improvement importance method to optimize system reliability and analyzes the process of failure risk under limited resources.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Xian Zhao, Chen Wang, Siqi Wang
Summary: This paper proposes a new rebalancing strategy for balanced systems by switching standby components. Different switching rules are provided based on different balance conditions. The system reliability is derived using the finite Markov chain imbedding approach, and numerical examples and sensitivity analysis are presented for validation.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Fengyuan Jiang, Sheng Dong
Summary: Corrosion defects are the primary causes of pipeline burst failures. The traditional methodologies ignore the effects of random morphologies on failure behaviors, leading to deviations in remaining strength estimation and reliability analysis. To address this issue, an integrated methodology combining random field, non-linear finite element analysis, and Monte-Carlo Simulation was developed to describe the failure behaviors of pipelines with random defects.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Guoqing Cheng, Jiayi Shen, Fang Wang, Ling Li, Nan Yang
Summary: This paper investigates the optimal joint inspection and mission abort policies for a multi-component system with failure interaction. The proportional hazards model is used to characterize the effect of one component's deterioration on other components' hazard rates. The optimal policy is studied to minimize the expected total cost, and some structural properties of the optimal policy are obtained.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Hongyan Dui, Yaohui Lu, Shaomin Wu
Summary: A new resilience model is proposed in this paper for systems under competing risks, and related indices are introduced for evaluating the system's resilience. The model takes into account the degradation process, external shocks, and maintenance interactions of the system, and its effectiveness is demonstrated through a case study.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yang Li, Jun Xu
Summary: This paper proposes a translation model based on neural network for simulating non-Gaussian stochastic processes. By converting the target non-Gaussian power spectrum to the underlying Gaussian power spectrum, non-Gaussian samples can be generated.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yanyan Liu, Keping Li, Dongyang Yan
Summary: This paper proposes a new random walk method, CBDRWR, to analyze the potential risk of railway accidents. By combining accident causation network, we assign different restart probabilities to each node and improve the transition probabilities. In the case study, the proposed method effectively quantifies the potential risk and identifies key risk sources.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Nan Hai, Daqing Gong, Zixuan Dai
Summary: The current risk management of utility tunnel operation and maintenance is of low quality and efficiency. This study proposes a theoretical model and platform that offer effective decision support and improve the safety of utility tunnel operation and maintenance.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Tomoaki Nishino, Takuya Miyashita, Nobuhito Mori
Summary: A novel modeling methodology is proposed to simulate cascading disasters triggered by tsunamis considering uncertainties. The methodology focuses on tsunami-triggered oil spills and subsequent fires and quantitatively measures the fire hazard. It can help assess and improve risk reduction plans.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Mingjiang Xie, Yifei Wang, Jianli Zhao, Xianjun Pei, Tairui Zhang
Summary: This study investigates the effect of rockfall impact on the health management of pipelines with fatigue cracks and proposes a crack propagation prediction algorithm based on rockfall impact. Dynamic SIF values are obtained through finite element modeling and a method combining multilayer perceptron with Paris' law is used for accurate crack growth prediction. The method is valuable for decision making in pipeline reliability assessment and integrity management.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Saeed Jamalzadeh, Lily Mettenbrink, Kash Barker, Andres D. Gonzalez, Sridhar Radhakrishnan, Jonas Johansson, Elena Bessarabova
Summary: This study proposes an integrated epidemiological-optimization model to quantify the impacts of weaponized disinformation on transportation infrastructure and supply chains. Results show that disinformation targeted at transportation infrastructure can have wide-ranging impacts across different commodities.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Jiaxi Wang
Summary: This paper investigates the depot maintenance packet assignment and crew scheduling problem for high-speed trains. A mixed integer linear programming model is proposed, and computational experiments show the effectiveness and efficiency of the improved model compared to the baseline one.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yuxuan Tian, Xiaoshu Guan, Huabin Sun, Yuequan Bao
Summary: This paper proposes a DFMs searching algorithm based on the graph neural network (GNN) to improve computational efficiency and adaptively identify DFMs. The algorithm terminates prematurely when unable to identify new DFMs.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)